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Powder-based additive manufacturing – a review oftypes of defects, generation mechanisms, detection,property evaluation and metrologyHossein TaheriIowa State University, [email protected]

Mohammad Rashid Bin Mohammad ShoaibIowa State University

Lucas KoesterIowa State University, [email protected]

Timothy BigelowIowa State University, [email protected]

Peter C. CollinsIowa State University, [email protected]

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Powder-based additive manufacturing – a review of types of defects,generation mechanisms, detection, property evaluation and metrology

AbstractPowder-based additive manufacturing (AM) technologies have been evaluated for use in different fields ofapplication (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably betterquality than conventionally produced parts. Manufacturing defects and their effects on the quality andperformance of AM parts are a currently a major concern. It is essential to understand the defect types, theirgeneration mechanisms, and the detection methodologies for mechanical properties evaluation and qualitycontrol. We consider the various types of microstructural features or defects, their generation mechanisms,their effect on bulk properties and the capability of existing characterisation methodologies for powder basedAM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects onmechanical properties and design considerations are also reviewed. Together, these provide a framework tounderstand the relevant machine and material parameters, optimise the process and production, and selectappropriate characterisation methods.

Keywordsadditive manufacturing, process evaluation, quality control, defects, inspection, in-line monitoring, non-destructive testing, NDT, design standards, mechanical properties

DisciplinesAerospace Engineering | Electrical and Computer Engineering | Manufacturing | Materials Science andEngineering

CommentsThis article is published as Taheri, Hossein, Mohammad Rashid Bin Mohammad Shoaib, Lucas W. Koester,Timothy A. Bigelow, Peter C. Collins, and Leonard J. Bond. "Powder-based additive manufacturing-a reviewof types of defects, generation mechanisms, detection, property evaluation and metrology." InternationalJournal of Additive and Subtractive Materials Manufacturing 1, no. 2 (2017): 172-209. DOI: 10.1504/IJASMM.2017.088204. Posted with permission.

AuthorsHossein Taheri, Mohammad Rashid Bin Mohammad Shoaib, Lucas Koester, Timothy Bigelow, Peter C.Collins, and Leonard J. Bond

This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ece_pubs/189

http://dx.doi.org/10.1504/IJASMM.2017.088204

http://dx.doi.org/10.1504/IJASMM.2017.088204

https://lib.dr.iastate.edu/ece_pubs/189?utm_source=lib.dr.iastate.edu%2Fece_pubs%2F189&utm_medium=PDF&utm_campaign=PDFCoverPages

172 Int. J. Additive and Subtractive Materials Manufacturing, Vol. 1, No. 2, 2017

Copyright © 2017 Inderscience Enterprises Ltd.

Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology

Hossein Taheri Center for Nondestructive Evaluation (CNDE), Applied Science Complex II, 1915 Scholl Road, Ames, IA 50011, USA and Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA Email: [email protected]

Mohammad Rashid Bin Mohammad Shoaib Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA Email: [email protected]

Lucas W. Koester Center for Nondestructive Evaluation (CNDE), Applied Science Complex II, 1915 Scholl Road, Ames, IA 50011, USA Email: [email protected]

Timothy A. Bigelow Center for Nondestructive Evaluation (CNDE), Applied Science Complex II, 1915 Scholl Road, Ames, IA 50011, USA and Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA Email: [email protected]

Powder-based additive manufacturing 173

Peter C. Collins Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA and Center for Advanced Non-Ferrous Structural Alloys (CANFSA), Ames, IA 50011, USA Email: [email protected]

Leonard J. Bond* Center for Nondestructive Evaluation (CNDE), Applied Science Complex II, 1915 Scholl Road, Ames, IA 50011, USA and Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA and Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA Email: [email protected] *Corresponding author

Abstract: Powder-based additive manufacturing (AM) technologies have been evaluated for use in different fields of application (aerospace, medical, etc.). Ideally, AM parts should be at least equivalent, or preferably better quality than conventionally produced parts. Manufacturing defects and their effects on the quality and performance of AM parts are a currently a major concern. It is essential to understand the defect types, their generation mechanisms, and the detection methodologies for mechanical properties evaluation and quality control. We consider the various types of microstructural features or defects, their generation mechanisms, their effect on bulk properties and the capability of existing characterisation methodologies for powder based AM parts in this work. Methods of in-situ non-destructive evaluation and the influence of defects on mechanical properties and design considerations are also reviewed. Together, these provide a framework to understand the relevant machine and material parameters, optimise the process and production, and select appropriate characterisation methods.

Keywords: additive manufacturing; process evaluation; quality control; defects; inspection; in-line monitoring; non-destructive testing; NDT; design standards; mechanical properties.

Reference to this paper should be made as follows: Taheri, H., Shoaib, M.R.M., Koester, L., Bigelow, T.A., Collins, P.C. and Bond, L.J. (2017) ‘Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology’, Int. J. Additive and Subtractive Materials Manufacturing, Vol. 1, No. 2, pp.172–209.

174 H. Taheri et al.

Biographical notes: Hossein Taheri is a PhD Graduate Research Assistant in the Center for Nondestructive Evaluation (CNDE) and Mechanical Engineering Department at the Iowa State University. Prior to the PhD program, he worked as a Research Assistant in the Material Evaluation and Testing Laboratory (METLAB) at South Dakota State University, where he received his Master’s degree in Mechanical Engineering working on composite materials testing and evaluation. His research interests focus on material evaluation and characterisation and non-destructive methods with specific emphasis on additive manufacturing.

Mohammad Rashid graduated with a BSc in Aerospace Engineering and a minor in NonDestructive Evaluation (2016). He worked as a Research Assistant for Department of Aerospace Engineering. He interned at Malaysian Institute of Aviation Technology conducting research on UAVs and designing and printing 3D parts for UAVs. His research interest areas include engineering mechanics, additive manufacturing and non-destructive testing.

Lucas W. Koester received his PhD from the University of Nebraska at Lincoln studying wave propagation and scattering in polycrystalline media. His current and past research interests include wave propagation in polycrystalline media, defect scattering, and modelling with emphasis on additive manufacturing processes and materials.

Timothy A. Bigelow graduated from the University of Illinois-Urbana in 2004 with a PhD. He was then a Visiting Assistant Professor at the University of Illinois at Urbana-Champaign for a year before getting hired as an Assistant Professor at the University of North Dakota. In 2008, he moved to Iowa State University where he was promoted to Associate Professor with tenure in 2014. His research interests focus on ultrasound backscatter analysis for non-destructive evaluation and biomedical applications as well as developing ultrasound for therapy applications to benefit human health.

Peter C. Collins is an Al and Julie Renken Associate Professor of Materials Science and Engineering, as well as the Site Director for the Center for Advanced Non-Ferrous Structural Alloys (CANFSA). He received his undergraduate degree in Metallurgical Engineering from the University of Missouri-Rolla, and MS and PhD in Materials Science and Engineering from The Ohio State University. Prior to joining ISU, he was a member of the faculty at the University of North Texas, and has also spent time standing-up a not-for-profit 501-3(c) manufacturing laboratory in the Quad Cities, and regularly engages with both industry and the government. He has 17 years of experience in additive manufacturing, and currently is very active in establishing processing-microstructure-property relationships for different additive manufacturing approaches and materials. He is also an expert in physical metallurgy and electron microscopy.

Leonard Bond is the Director at Center for Nondestructive Evaluation (CNDE), Iowa State University. He is a Professor in both Aerospace Engineering and Mechanical Engineering Departments with more than 35 years of experience working on NDE topics. He has held positions as an academic, consultant and in federal laboratories. He was a member of faculty at University College London (UCL), and was promoted to Reader in Ultrasonics. He moved to the USA and was a Research Professor at University of Colorado, Boulder. Prior to joining CNDE in 2012, he was with DOE National Laboratories for 15 years as a Laboratory Fellow at Pacific Northwest National Laboratory. He is the co-author of the book Ultrasonics (3rd edition), CRC Press (2011), published


more than 300 papers and holds ten patents. He is a Fellow of the AAAS and the UK’s Institute of Physics. His current projects include work on materials characterisation, modelling and NDE for additive manufacturing.

1 Introduction

Metal industrial products have traditionally been produced using various forms of casting and molding in combination with forming that can include forging, rolling, and extrusion. In many cases these methods are combined with machining using subtractive processes and then joining to produce a part or other product. Along with traditional and subtractive methods, powder-based processing routes have been used for part production especially for geometrically complex structures. Over decades, experience and analysis has been combined to formulate codes and standards as well as to mature various characterisation, testing and evaluation methods which have identified classes of defects,1 selected alloys for particular applications, and assessed their significance when incorporated into deformation models in which stress is applied to a part or system (ASM, 2015). There is now an increasingly diverse range of additive manufacturing (AM) process techniques that provide the ability to produce parts from computer-generated models with little to no additional material removal.

The manufacturing flexibility of AM processes provides the possibility of developing novel designs [e.g., topology optimised structures (Gaynor et al., 2014)] for products which are simply not possible with casting or subtractive (e.g., machining) methods. Powder-based methods for metal manufacturing are versatile and have been explored for various applications (Yap et al., 2015; Thornton, 2015; Sing et al., 2015; Foster et al., 2015; Yeong et al., 2014; Frazier, 2014; Sames et al., 2016). Despite the promise of AM, there exist a number of significant impediments to its widespread utilisation, particularly in industries that produce low production volume and high value components like aerospace. Quality control and mechanical characterisation remains a major challenge (Huang and Leu, 2014; NIST, 2013; Urabe et al., 2014; Criales et al., 2015; Koester et al., 2016). The quality and mechanical properties of the manufactured parts are influenced by the generation and existence of microstructural features and potential defects (Song et al., 2015; Olakanmi et al., 2015; Collins et al., 2014, 2016).

Reliable control of mechanical properties needs to be achieved for AM to see increased use with novel designs that utilise the method’s full potential, particularly for high value components (Haden et al., 2015). For this reason, it is necessary to develop new and to adapt current metrology tools for the assessment of microstructural features and provide reliable detection and characterisation of defects. Integrating these tools with a good understanding of the mechanisms of defect formation during the manufacturing process should enable AM methods to be more widely adopted. It is also necessary to understand the significance of the various classes of defects on part functionality and life under the influence of operational stresses. When considering a components life cycle, it is desirable to optimise the manufacturing process and then plan monitoring and replacement of parts before they fail.

Several studies have evaluated the causes and occurrence of defects in AM and their influence on mechanical properties and the life of parts (Gong et al., 2014a, 2015; Bauereiß et al., 2014). Conventional non-destructive methods for the detection of defects


and other evaluation of the deposited material of AM parts are considered in several papers, e.g., Everton et al. (2015), Brien and James (1988) and Slotwinski (2014). However, there are few published studies that consider the types of defects that can potentially occur in fabrication using AM and review the capabilities of detection, sizing, and monitoring methodologies. This review paper bridges this gap.

2 Powder-based additive manufacturing processes

Powder bed fusion (PBF) and directed energy deposition (DED) are two AM processes where powders are the feedstock. In both methods, the processing parameters and raw material characteristics influence quality and mechanical properties of the as deposited part. The physical mechanisms by which the various processing parameters and powder characteristics influence the parts microstructure, defect populations, and attending mechanical properties are topics of multiple ongoing research efforts across the AM community.

While the mechanisms by which various process parameters influence defects and microstructure may not be completely known, several parameters associated with PBF and DED powder-based AM technologies have been correlated with defects and microstructure. These parameters include the quality of the powder feedstock and the power imparted by the heat source. Although there are more parameters that are common to PBF and DED than there are differences, the differences are important and will impact the thermal gradients of the molten pool and surrounding material. For example, DED creates a mobile molten pool that is intimately coupled with the continuous injection of powder into the pre-programmed tool path of the heat source. The molten pool size, powder feed rate, and shielding gas flow are all critical process parameters (Yu et al., 2010). In the PBF method, pre-heating of the powder bed influences the solidification process and thermal gradient in the part (Savalani and Pizarro, 2016; Lee, 2015).

In both processes, powder is consolidated after imparting energy with a heat source. Both sintering2 and melting of powder are used to affect near-net shape structures in AM.3 Sintering-based AM processes generally achieve a green or brown compact that requires additional processing to achieve a fully dense part. Alternatively, fusion-based AM processes require no further consolidation, but may benefit from secondary processing steps such as hot isostatic pressing (HIP) or subsequent heat-treatments. The process parameters and material attributes known to affect final part properties are summarised in Table 1.

The physical processes that occur during AM are very complex, and are just beginning to be fully understood and quantified (Collins et al., 2016; Markl and Korner, 2016; Matthews et al., 2016). Indeed, as shown by Matthews et al., the particles not only move during the AM process, but that the fundamentals physics of the process (e.g., metal vapour flow) are highly variable, and can create, effectively, vortexes which cause the powder to move. Once entrained in the liquid, the melt pool dynamics are equally complex, with Marangoni convection, evaporation, wetting and capillarity playing strong roles (among many other operating physics). The liquid metal velocity is quite high, and results in features that resemble comet tails as melting particles leave molten material behind them as they move through the molten pool (Mendoza et al., 2017). These physics present challenges in understanding and modelling AM processes, but it is expected that


over the next three to five years, a number of research groups will be able to contribute to the knowledgebase of the heat source/particle interactions. Table 1 PBF and DED process and material variables affecting the parts characteristics

Process parameters Powder parameters PBF/DED Particle size and distribution Heat source (laser/electron beam) energy Internal porosity Scanning rate (speed) Particle shape and topography Scanning spot size (radius/length) Surface tension/wettability Scanning raster spacing and pattern Viscosity Sintering/Melting environmental condition Specific heat Shielding gas flow rate Melting temperature Laser beam type and characteristics Thermal conductivity PBF Absorptivity/reflectivity Bed temperature Emissivity DED Chemical composition Powder flow rate Shielding gas

2.1 Process parameters

2.1.1 Heat source characteristics

The type of heat source and the energy density (see Scanning Characteristics) selected depend upon the material to be deposited. Lasers and electron beams are the most commonly used sources of energy in AM. Lasers supply monochromatic coherent light and can be used for a wide range of materials. Electron beams are characterised by a spot size that is typically far smaller than that of a laser, although the beam can be steered by electromagnetic lenses very rapidly, effectively allowing the melt pool size and position to be controlled and varied very quickly (Soylemez et al., 2010). Electron beams can only be used for conductive materials. Among the materials most suitable for electron beam AM techniques are weldable metals, including titanium and Ti-based alloys, Ni-based superalloys, Co-based alloys, Fe-based alloys, tantalum, tungsten, niobium, stainless steels (300 series), 2,319 and 4,043 aluminium, and Zircalloy.

AM laser heat sources are generally Q switched resulting in ultra-short pulse times. and Nd:YAG lasers are operating with power in the range from 50–500 W, but very high power CO2 lasers up to 18 kW have also been used (Chua et al., 2003). Nd:YAG lasers (λ = 1,064 nm) have a shorter wavelength, a capability of tighter focusing, and have higher energy absorption for metallic materials. In pulsed wave mode, the energy is delivered in a short time window of milliseconds (10–3 seconds) for melting and sintering applications (Majumdar and Manna, 2013), resulting in a shorter interaction time when compared with a continuous wave laser. Pulsed laser systems have been shown to be more suitable for use in sintering processes since good metallurgical bonding with less heat accumulation can be obtained (Majumdar and Manna, 2013; Santos et al., 2006).

In electron beam based AM techniques, a high power electron beam (typically 50 W to 40 kW) is generated in a thermionic electron gun where electrons are emitted by a heated tungsten filament. The electrons are then accelerated with an electrical field and


are focused and steered by electromagnetic coils. In certain cases, it is possible to use close-loop control to tune the energy of the electron beam thereby maintaining constant build temperature. Another capability of electron beam methods is that it is possible to deflect (‘steer’) the beam at very high rates (faster than thermal diffusion), which can be used to establish and maintain several melt pools simultaneously, a technique known as multi-beam heating (Vayre et al., 2013).

Other types of heat sources which are traditionally used in processes similar to additive manufacturing can be considered as the potential source of energy for AM. The development and control of robotic manipulation systems in multiple dimensions has enabled novel implementation of a broad range of welding-like processes in additive layer manufacturing. In plasma deposition techniques, a controlled plasma heat source forms a molten pool where a flow of metal powder is deposited (Zhang et al., 2003). In plasma heat sources, an electric arc is created between a cathode (tungsten electrode) and an anode (copper) under inert gas (Argon) shielding between the cathode and anode terminals (Messler, 1999). In addition to plasma methods, other arc-based heat sources were also reported to be used for additive manufacturing. Gas tungsten arc welding (GTAW) is an arc-based method which has been used for depositing metallic materials (Jandric and Kovacevic, 2004). Both gas metal arc welding (GMAW) and GTAW have been used by Almeida and Williams (2010) for fabrication of titanium alloy parts. Net shaping of metallic parts has also been achieved using processes such as metal inert gas (MIG) and metal active gas (MAG) welding techniques (Akula and Karunakaran, 2006). These methods tend to have larger absolute melt pool dimensions and thus are generally used to form large near net shaped parts when compared with those formed using laser or electron beam methods.

2.1.2 Scanning characteristics

Scanning speed (mm/s), spot size and the pattern of the scanning spot are all important parameters in the AM process. The energy density can be defined as (Glardonl et al., 2001):

d

PEv d

=⋅

where P is the average laser power (rate of energy flow averaged over one full period), vd is the scan velocity, and d is the beam diameter. In practice, the equality in this equation should more correctly be a proportionality, given variations in the shape of the molten pool. It has been observed that there is a minimum energy density above which the properties of the material are acceptable (Sears, 2002; Collins, 2004). Thus, energy density is directly proportional to the average laser power and inversely proportional to the scanning speed. Balancing these parameters generally leads to an operational window within which the systems can be operated to give desired part characteristics. The optimum scan velocity may be correlated with the thermal gradient experienced by the material (e.g., its ‘cooling rate’) and desired production rate of the machine. The former can be related to microstructure, texture, compositional homogeneity (Collins et al., 2016) while the latter is limited by the capability of the positioning or control systems for beam placement while maintaining process parameters within the optimal operational window that result in desired and ideally optimised material properties.


While laser-based AM methods typically use a single beam, multi-beam laser-based AM techniques have been demonstrated and shown to provide higher precision and improved deposition rates. In multi-beam laser-based AM, it is necessary to consider new process variables that influence the quality of the deposited material, including the percentage of beam overlaps, relative spatial positions, power and frequency variations (Patwa et al., 2013).

During AM deposition, for each layer, the heat source melts or sinters the powder in a predefined scanning pattern which generally consists of sequential scanning vectors (although parallel scanning vectors are possible in multi-beam laser-based AM and electron-beam based AM). Scanning vectors are co-optimised with scanning speed by considering uniform heat flow in the part. The scanning patterns and related scanning vectors greatly influence the thermal history of the part. Their optimisation is dependent upon the part geometry and multiple material thermophysical properties (e.g., thermal conductivity, heat capacity, surface tensions). Some common scanning patterns used in AM include zig-zag, parallel and hexagonal patches (Clijsters et al., 2012).

2.2 Powder and substrate characteristics

2.2.1 Powder

Both pure metal and alloy powders have been used in AM processes. However, powders of metal alloys are more commonly used for high value parts. A critical assessment of the literature indicates that the majority of investigations have focused on titanium (Wauthle et al., 2015; Gu et al., 2012) and aluminium (Bartkowiak et al., 2011; Buchbinder et al., 2011; Louvis et al., 2011; Vora et al., 2016; Brice et al., 2015) in pure powder processing, while Ti, Ni and Fe-based materials are typical for alloy powders (Santos-Ortiz et al., 2015). Ti-based alloys are used extensively in aerospace applications due to their high tensile strength and toughness, lightweight and the ability to withstand extreme temperatures (Dinda et al., 2008; Li et al., 2016) and in medical applications (Krishna et al., 2007; Liu et al., 2016; Dobrzańska-Danikiewicz et al., 2015; Brånemark et al., 2011; Singh et al., 2006; Banerjee et al., 2005b) due to their biocompatability. Ni-based alloys have superior creep, tensile strength, and corrosion resistance properties which make them ideal materials for jet engine and gas turbine components.

Powder attributes, such as morphology, surface chemistry, size, internal porosity and any entrained defects or foreign materials have a significant influence on the quality of the as-deposited material, the transmission of prior defects, generation of new defects, and the attending mechanical properties. Thus, the characterisation of powder is critically important when seeking to measure and/or predict the presence of inhomogeneities in the final product (Bond et al., 2014). Regarding the measurable attributes of powder, the particle shape, average size and particle size distribution are important for packing and processing (Slotwinski et al., 2014) in PBF, while flowability is important for both PBF and DED (Herzog et al., 2016).

2.2.2 Substrate

Due to large temperature gradients created between the molten pool and surroundings in powder based additive manufacturing, parts are usually made on a base plate or substrate which acts as both a mechanical support and a heat sink. The substrate and its thermal


characteristics are therefore important to provide adequate cooling and support during the forming process. In general, there is a significant economic advantage if the substrate can also be incorporated into the final shape of the additively manufactured component. The incorporation of the substrate into the final component can reduce build time and cause the process to consume less energy. In contrast, for cases when the substrate is not included in the final structure, it must be removed at the end of fabrication process using some form of cutting or machining.

2.3 Material (powder) parameters

2.3.1 Absorptivity

Absorptivity is the ratio of the absorbed radiation to the incident radiation, and is a function of both the material and the wavelength of the incident radiation. The absorptivity for metal powders is a variable in the energy balance of the process, and influences the critical (minimum) energy density. Table 2 provides examples of reported absorptivity of common metals used in AM. However, as can be seen from Table 2, the absorptivity of the materials in their powder form is significantly higher than their absorption in dense form. This is due to multiple scattering of the laser beam in the powders (Boley et al., 2015; Tolochko et al., 2000). In addition, for powder bed AM, the physical depth where the intensity of the radiation falls to of the original radiation intensity is called the optical penetration depth and depends on the absorptivity of the powders (Gu, 2015; Gusarov and Smurov, 2010; Tolochko et al., 2000). Although rarely possible, ideally the laser wavelength would be matched with the powder characteristics as energy density will change with both the powder absorptivity and frequency (wavelength) of the laser (Kruth et al., 2003). Table 2 Absorptivity of common materials used in additive manufacturing corresponding to

Nd:YAG and CO2 lasers

Material

Nd:YAG laser λ = 1.06 μm CO2 laser λ = 10.6 μm Powder

form Dense form Powder form Dense form

Tolochko et al.

(2000)

CRC Handbook

(Weaver and Frederikse,

2016)

Handbook of optical

constants of solids (Palik, 1985)

Tolochko et al.

(2000)

CRC handbook

(Weaver and Frederikse,

2016)

Handbook of optical

constants of solids (Palik, 1985)

Cu 0.59 0.03 0.03 0.26 0.02 - Al - 0.04 0.04 - 0.02 0.02 Fe 0.64 0.35 0.34 0.45 0.03 0.02 Ni 0.64 0.27 0.28 0.42 0.02 0.02 Ti 0.77 0.45 - 0.59 0.04 - TiC 0.82 - 0.36 0.46 - 0.43 Cr - 0.37 0.37 - 0.05 0.05 Al2O3 0.03 - - 0.96 - 0.94


2.3.2 Surface tension/wettability

In both sintering and fusion-based AM processes, the liquid-solid surface tension impacts the resulting product. This tension is a temperature and composition dependent variable. The surface tension of the solid-liquid interface (γsl), solid-vapour interface (γsv), and liquid-vapour interface (γlv) influence wettability which can be measured by the contact angle (θ).

cos sv sl

lv

γ γθγ−

=

As cosθ → 1, the liquid completely wets the solid. Spatial variation of temperature within the melt pool causes variation in surface tension and drives the melt pool to move from lower to higher surface tension regions, due to Marangoni convection.

2.3.3 Viscosity

The viscosity and wettability of the liquid metal enable the melt pool to spread across the previously deposited layer. Viscosity of the molten pool, μ, in a solid-liquid mixture in sintering systems is presented as:

2

011 l

m

φμ μφ

−−⎛ ⎞= −⎜ ⎟⎝ ⎠

where μ0 is the base viscosity, φl is the volume fraction of liquid phase, and φm is the volume fraction of solids. In melting based processes where the liquid formation is complete, the dynamic viscosity of the liquid is defined as:

01615

mμ γkT

=

where m is the atomic mass, k is the Boltzmann constant, T is the temperature and γ is the surface tension of the liquid.

2.3.4 Thermal conductivity

The effective thermal conductivity of a packed powder can be estimated by (Glardonl et al., 2001):

(1 )pK ω K≅ −

where ω is the packing density of the powder bed and K is the conductivity of the dense material. This effective thermal conductivity strongly depends on particle-to-particle contact. Based on experimental measurements, Fischer et al. (2003) found that a loose pack has thermal conductivity that can be more than one order of magnitude smaller than for fully dense materials. Thermal conductivity for different metal powders was measured in several studies (Swift, 1966; Hadley, 1986). More recently, other researchers have used simulation and found that conductivity for an AM material is almost decoupled from bulk properties (Turner et al., 2015). It was found that the combination of the thermal characteristics of the material, substrate and environmental processing conditions affect


the cooling and solidification rates that strongly influence the resulting part microstructure (Hofmeister and Griffith, 2001).

Figure 1 Powder bed fusion additive manufacturing system (see online version for colours)

2.4 Processing mechanisms

2.4.1 Powder bed fusion mechanism

Powder bed fusion (PBF) systems normally include a heat source, an automatic powder layering mechanism, a computer control system and related sensors and accessories. Such a system is shown in schematic form as Figure 1. An electron beam source requires a vacuum environment while laser sources typically utilise an inert gas environment or gas shielding to prevent excessive oxidation. Powder is spread over the previous layer in each step of production using a roller or a blade. After each step of layering, the build platform lowers the part so the process can be repeated for subsequent layers. Typically, melting processes are faster than sintering, but require higher energy expenditure (Gibson et al., 2015).

2.4.2 Direct energy deposition mechanism

The concept of direct energy deposition (DED) is very similar to the other additive manufacturing methods. However, the powder is supplied through feed nozzles into an inert gas shielded delivery system. The beam and powder nozzles are focused coincidently at the deposition plane. It is possible to incorporate up to 6 degrees of freedom for the position and motion of the deposition head, allowing for deposition to occur below a part in an unsupported geometrical sense. The incoming material is heated prior to deposition as it passes through the beam, and may be melted either during this pass through the beam or by thermal conduction once the powder is in the molten pool through the nozzles into the path of a laser or electron beam. Figure 2 shows a schematic of a DED process and representative configuration of the nozzles relative to the beam. DED may be used to repair high value components where the existing high value components act as the substrate.


Figure 2 Direct energy (laser) deposition, (a) schematic diagram (b) additive manufacturing news article (2015) (see online version for colours)

(a)

(b)

Source: TRUMPF GmbH (2015)

3 Types of inhomogeneities and their generation mechanisms in AM parts

Variations in process parameters and powder attributes influence not only the microstructural features present in AM components (e.g., grain size, texture, solute


distribution), but may lead to the generation of defects. Laser power, scan speed, layer thickness, spacing of scan lines, powder feed rate, powder size distribution, and surface chemistries are among the many parameters that influence the quality of the deposited material. Many studies have been conducted which seek to understand and quantify the effects of these parameters on the final microstructural characteristics, e.g., Rombouts et al. (2006) and Slotwinski and Garboczi (2014). However, as the combined influence of all related parameters is not completely understood, robust process models still need to be developed (Rombouts et al., 2006; Ng et al., 2009; Gong et al., 2014a) and other critical experiments are required.

3.1 Microstructural anomalies

Any feature seen in the microstructure of AM parts that deviates from what is desired can be considered as an anomaly (or a ‘defect’) depending upon the end application. As noted previously, in this work, ‘defect’ is in reference to any structural deviation away from an otherwise uniform, isotropic, fully dense solid of the target alloy. Examples are seen in the form of: porosity, lack of fusion (LOF), microcracks and hot-tears, variations in crystallographic texture and grain size, unwanted variations in composition, unexpected or metastable phases, and non-metallic inclusions.

3.2 Porosity

Porosity is a common defect found in AM parts. Many process parameters and feed material attributes have been associated with porosity. Further, the porosity that exists can occur at different length scales. For sintering-based AM processes, micro-porosity (sub-powder scale) is generally related to pores inside the starting powder that are transmitted to the final deposition. For both sintering-based and fusion-based AM, porosity that is present at the macroscale may be categorised into two main classes: gas porosity and LOF (Ng et al., 2009).

3.2.1 Gas porosity

At the present time, most research articles attribute gas porosity to trapped shielding gas that arises from three primary sources. In DED methods, a high powder flow rate can lower the specific energy of the melt pool, resulting in increased gas entrapment. Care must be taken to not include unmelted particles that can be ‘pulled out’ during metallographic sectioning in this category, as this leads to false positive indications of gas porosity. The second source in deposition methods is entrapped gas within the starting powder particles. Lastly, Marangoni flow, which is defined as the mass transfer along an interface between two fluids due to surface tension gradient, causes gas retention bubbles within the melt pool which lead to large pores (Barua et al., 2014).

For laser based methods, the following formula can be used as a predictor of porosity

percentage indicator through the normalised enthalpy s

HhΔ (Wu et al., 2014; Hann et al.,

2011):


3s s

H ηPh ρh π σ vΔ

=α

where ΔH is the specific energy (J/kg), hs is the enthalpy at melting (J/kg), η is the surface absorptivity, P is the power (J/s), ρ is the density at melting (kg/m3), α is the thermal diffusivity (m2/sec), σ is the half width of beam spot (m) and v is the speed (m/sec). Increasing the normalised enthalpy will decrease the porosity percentage. A correlation between the normalised melt depth and normalised enthalpy is presented in Figure 3, which is a comparative evaluation based on data from several studies and for different materials. Figure 3 captures results from multiple studies, including data from two different laser fibre diameters (200 and 400 μm) and two scanning velocities (1 and 2 m/min) with data from Rai et al (2007) for a range of metals (Hann et al., 2011; Rai et al., 2007). Importantly, there is a minimum heat input (enthalpy) that is required to result in melting. Deviations from an ideal energy input, and hence melt pool depth and enthalpy, will change the attributes of the molten pool, including the potential to entrap gas resulting in gas porosity.

Figure 3 Normalised depth as a function of normalised enthalpy for different laser parameters and different material

Source: After Hann et al. (2011)

Porosity in structural applications is generally detrimental to part performance. The influence of both the starting powder and the process parameters have been investigated with the objective of reducing/eliminating the porosity in final components. It has been found that samples fabricated using powders produced by gas atomisation (GA) show three times higher interlayer porosity than those formed using powders produced by Plasma Rotating Electrode Process (PREP) at all powder feed rates and laser powers


(Ahsan et al., 2011). It has also been found that in DED, powder flow rates outside the operational window will increase porosity. The use of GA powders also been associated with pores that are larger in diameter when compared to PREP powder at all laser powers and powder feed rates, while there is less porosity in all cases with powder diameter less than 40 (Ahsan et al., 2011). This has been attributed to the increased levels of entrapped porosity within the powders produced by GA compared with PREP powders, and explained by considering the fact that smaller particle sizes should result in higher melt pool temperatures/fluid flow and thus the elimination of entrapped porosity.

Increasing the energy density can eliminate some of these smaller pores. However, other types of inhomogeneities can form at higher energy densities (Meier and Haberland, 2008; Bauereiß et al., 2014). According to these authors, the inhomogeneities that occur at higher energy densities are part morphologies including increased surface roughness and density variation. Regarding the former, this is most likely due to complex (and offsetting) molten pool physics that operate at higher molten pool temperatures, including the spreading of the molten pool due to decreased surface tension, and a concurrent vaporisation of some elements which can lead to local cooling. The selective evaporation of some elements has been associated with a reduction of density of the final components. At lower energy densities, insufficient melting leads to cavities in the part. Irregular, lattice like pores form when the scan line spacing is too large and energy density is insufficient. The effect of scanning speed on the finish of the build plane and sidewalls has also been investigated. In these studies, it was shown that an increase in scanning speed initiates fragmentation in both surfaces (Meier and Haberland, 2008). Since any new layer is built on the rough and corrugated surface of a previous layer, the thickness of the new layer has significant variability. When compounded with the dependence of melt pool depth and normalised enthalpy in Figure 3, process related defects such as lack of adequate binding and porosity can occur (Bauereiß et al., 2014).

3.2.2 Porosity due to LOF

When there is insufficient energy in the melt pool, the resulting inability to melt the powder particles can cause LOF porosity in AM parts. In DED, an incorrect or varying standoff distance between the deposition nozzle and substrate causes defocusing of the laser beam and reduced energy density (i.e., higher spot diameter in the energy density equation of Glardonl), which can cause LOF porosity (Barua et al., 2014). The size and composition of the substrate can also affect the thermal diffusion away from the melt pool and cause LOF, as well as substrate-deposit delamination. LOF defects are usually found along boundaries between layers, are irregularly shaped, and often contain unmelted powder as shown in Figure 4(a) and Figure 4(b) (Olakanmi et al., 2015). LOF can be divided into three categories (Liu et al., 2014):

a separated surface with un-melted powder

b separated surface without un-melted powder

c narrow and long shaped with un-melted powder.

In general, it is found that increasing the scanning speed decreases the specific energy and therefore increases the risk of causing LOF defects (Ng et al., 2009). The occurrence of LOF increases as the powder feed rate increases and as the normalised enthalpy of the melt pool is decreased. In looking at mitigation strategies it has been found that


increasing the track overlap will not have a significant effect on reducing the tendency for LOF defect formation (Ng et al., 2009).

Figure 4 Optical micrographs of LOF defects in the cross-sections of SLM Ti64 with 30 µm layer thickness shown as a cross section of the build direction at high scanning speed (both cross-sections were etched to reveal the microstructures and defects), (a) along the layer boundary (b) LOF defect with un-melted powder particle

Source: Liu et al. (2014)

3.3 Anisotropy and phase stability

Changing process parameters such as laser power and scanning parameters, specifically scanning speed and its effects on energy density, has been shown to cause a considerable change in the grain structure (Gong et al., 2014b), the phases present (including the promotion of metastable phase formation), their distribution within the microstructure (Scharowsky et al., 2015), and tendencies for defect generation (Zhong et al. 2015) in AM parts (Liang et al., 2014a, 2014b). The variation in the temperature gradient in the melt pool result in variation in the solidification rate, resulting in concurrent variations in microstructure, including phase stability (Marya et al., 2015). Further, the atmosphere can have an influence on phase stability, microstructural features/morphology, and defects. For example, even a small amount of oxygen contamination can cause oxidation changing the resulting texture and adding impurities to the microstructure in some AM methods which are processed under inert gas shielding or environments (Murgau, 2016). Several studies have reported the anisotropy seen in material properties caused by the different scanning patterns and process parameters used (Ahn et al., 2002; Shamsaei et al., 2015) and has also been shown to be dependent upon the material employed (Zhu et al., 2015; Carroll et al., 2015).

3.4 Inclusions

For the sake of completeness, it is useful to consider the formation of dispersoids of varying types in the microstructure. From the perspective of physical metallurgy, these dispersions may be either intentional (and thus beneficial), or an undesirable (and deleterious) microstructural feature. When they are intentional, they are often dispersions that are (often) incoherent with the matrix, but of a size that is sufficiently small (< 250 nm) that they do not lead to large stress concentrations in the microstructure, and tend to improve the mechanical properties, such as yield strength. However, while this size may be intentionally introduced and is attainable in additively manufactured


materials (Banerjee et al., 2005a; Brice and Fraser, 2003), it is not the only type of dispersion present. In other cases, the material can chemically react with the shielding gas, forming exogenous intermetallic particles such as oxides and sulphides. The size of these inclusions are generally in the range of 0.5 μm to 1 millimetre – a size scale that is a cause for concern when considering the mechanical properties, specifically ductility, fracture toughness, and fatigue. Impurities in powders can exacerbate the size of these inclusions. The number, size, shape (morphology) and distribution of inclusions over the part significantly affect final part performance, particularly fatigue strength (Wilby and Neale, 2015).

Current methods of materials characterisation are often destructive, requiring that the material be sectioned and appropriately prepared to quantify the microstructural features present. For example, scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD) techniques can be used to observe and quantify porosity, grain size, shape, and orientations to determine the local anisotropy. Similarly, various spectroscopic techniques can be used to measure the composition of the material as well as of phases in the material (e.g., inclusions). A challenge for some types of additive manufacturing is that the length scales of the important features, especially as it relates to anisotropy, where the domains over which different orientations exist may span several millimeters (Brice et al., 2016). Such length scales are not compatible with current analysis techniques, such as EBSD. However, there are exciting new techniques, including spatially resolved acoustic spectroscopy (SRAS) (Smith et al., 2014, 2016b; Sharples et al., 2006; Li et al., 2012) which may provide a way to conduct large scale analysis of variations in the orientation of grains, providing a way to correlate processing with properties and performance (Haden et al., 2015). There are some non-destructive methods to assess anisotropy, including X-ray-based tomographic approaches, but they are sensitive to sample thickness and can be hindered by a spatially varying crystallographic texture.

3.5 Geometrical anomalies

Dimensional inaccuracy for an AM produced part can be problematic, particularly when considering a prototype or high value part where the end use is for a component requiring fine dimensional control (Smith et al., 2016a). The layering process used in AM methods can result in rough surfaces and possible deviations from specified CAD model tolerances or other geometrical anomalies in the final part. Typically, the CAD model is converted to a stereolithography (*.stl) file format where the designed geometries and surfaces are discretised into geometric meshes. A macro-level ‘stair-case’ effect can occur on part surfaces due this discretisation (Moroni et al., 2014). In addition it has been shown that melt pool dynamics have a large influence on sidewall dimensions for the finished parts (Lee and Farson, 2015). The risk of occurrence for curling, waviness and surface roughness are also all influenced by the previously discussed process and material parameters.

Melt pool dimensions and fluid flow have been shown to influence the sidewall dimensions and surface finish in deposited parts (Gockel et al., 2015). To minimise geometrical anomalies, a stable melt pool size/shape is required (Lee and Farson, 2015). The Marangoni effect has a strong influence on melt pool size and shape and can introduce anomalies in deposited layers due to its dependence on composition and the local thermal gradients.


3.6 Balling phenomenon

The balling phenomenon represents a type of defect that is generated in laser sintering-based AM processes. Several researchers have investigated and sought to explain the balling mechanism (Shen et al., 2006; Gu and Shen, 2009; Bauereiß et al., 2014). A sub-critical energy density has been identified as the primary cause of balling which resulted in insufficient material being present in the liquid phase to promote sintering. In addition, balling at higher scanning speeds has been attributed to instabilities in the molten pool due to a capillary effect. Formation of oxide layers on both the solid and molten material due to presence of oxygen in the powder or built chamber will change the wetting process of surrounding material and cause balling phenomenon (Louvis et al., 2011). These factors then change the viscosity of the semi-molten phase, limit the liquid flow and melt pool morphology, leading to balling occurring on the sintering surface. Remedies include increased the laser power, reducing the scan speed, and decreasing the layer thickness to achieve higher energy density. Adding deoxidants to the powder can generate a smooth sintering surface and consequently lower the risk of ball formation by mitigating formation of an oxide layer on the melt pool. Figure 5 shows a schematic of the balling phenomenon exhibited by coarsening spherically-shaped sintered particles and limited liquid formation.

Figure 5 Schematic of balling phenomenon featured by coarsening spherical-shaped sintered particles and by limited liquid formation (see online version for colours)

3.7 Cracks (and similar linear features)

Several different physical factors and process parameters can cause cracking in AM parts. Melted powder can merge with the closest surface contact point, often a solid or liquid neighbouring particle and not the previous layer. Continuation of this phenomenon can cause a change in the distribution of thermal energy and generation of large channels devoid of material bound to the substrate that resemble cracks in the final part (Bauereiß et al., 2014). Melt pool movement also causes mass transfer/movement along the interface due to surface tension gradients (known as the Marangoni effect) and can cause entrapped gas porosity, or cracks (Shifeng et al., 2014; Scharowsky et al., 2012). Thermal gradients can generate cracks in the parts when there are differences in thermal properties


between the substrate and the build material, or when there are large thermal gradients in the molten pool while solidification is proceeding (i.e., hot tearing).

In addition to these cracks that can form during service, it is possible to have cracks form during service. Powder contamination, especially inclusions or un-melted particles originating from the feedstock, can cause subsequent cracking in service due to stress concentrations around inclusions under fatigue loading. Geometric anomalies can form stress concentrators that can potentially form the starting point for crack growth in service.

3.8 Defects in powder materials

As noted previously, internal voids in feedstock powder materials have been identified as a source of defects in AM components. Poor packing density during consolidation can create internal voids in as-deposited materials. Impurities in the powders can also lower the quality of the final part and generate porosity and inclusions (Benson and Snyders, 2015). Sieving the as-atomised powders (Lee et al., 2006) and triboelectric separation (Stencel et al., 2000) are reported as potential methods for removing impurities from powder feedstock. The particles themselves can also contain geometric defects including voids (Moylan et al., 2014a; Philtron and Rose, 2014). One such example is that of an X-ray image of titanium particles that exhibit internal voids, as well as powder particle size and shape variations is shown in Figure 6. Smaller sized powder particles exhibit better compaction and lower defect rates than when compared with larger particle. However, smaller particles may also lead to increased interstitial contents in the final components or safety issues during powder processing and handling. It has also been found that the final part surface roughness increases with larger particle and consequently larger layer thickness is employed (Abd Elghany and Bourell, 2012). Figure 6 Example of a high resolution X-ray image of a sample of metal powder

Note: Individual and multiple internal voids can be seen in addition to a range of particle size and shapes.

Source: Bond et al. (2014)


3.9 Defects in functionally graded materials manufactured by AM methods

One opportunity in application of AM technology is the fabrication of functionally graded materials (FGMs). FGMs are formed by mixing different alloy powders during the laser sintering or melting process. Producing controlled gradients in mechanical properties enables opportunities for new designs and products which utilise these tailored properties (Collins et al., 2003; Banerjee et al., 2003; Liang et al., 2014b). For such materials the spatial control of chemical composition becomes an additional design parameter, and consequently new approaches to material testing and evaluation need to be developed.

Thermal stresses due to the AM process are the main cause of defects, primarily cracking, in FGMs due to a systematic variation in the thermophysical properties and the presence of large thermal gradients. This cracking commonly occurs in brittle ordered phases that form during the fabrication process, and is the result of transitioning across phase boundaries as a result of the compositional variation. This kind of cracking has been reported in several studies for different compositions of materials (Hofmann et al., 2014). Such transitions are not surprising when the thermodynamics of materials are considered. While FGMs offer tremendous promise in designing a material whose properties can be engineered for a given set of requirements at a specific location within a component, in reality it is necessary to incorporate a detailed materials design component to realise effective and defect free FGMs. The local composition will influence the attending microstructure (Liang et al., 2014b; Collins et al., 2003; Banerjee et al., 2003) and properties (Liang et al., 2014b; Collins, 2004). In addition to cracking due to brittle ordered phases, it is possible to have differences in the coefficients of thermal expansion lead to cracking in some FGMs, especially those with abrupt changes in composition. One additional type of defect that can be found in FGMs is un-melted particles formed when mixing materials with different thermophysical properties and melting points.

4 Influence of process parameters and inhomogeneities on mechanical properties

Whether due to process parameters, environmental conditions, or material (powder) attributes, all of the defects discussed above contribute to mechanical property variations seen in AM products. The microstructures of AM processes are from, typically, non-equilibrium processes with significant thermal gradients and complex thermal histories that vary spatially within a component. Not surprisingly, the post-processing heat treatment can alter many of the mechanical properties for a finished part. However, the anisotropic characteristics of AM fabricated materials, due to the thermal gradients and previous layer that ‘template’ the next layers grains (e.g., epitaxial growth) are likely to persist unless recrystallisation can be promoted or multiple variants of a dominant second phase can be promoted. Several studies discuss the mechanical behaviour of different AM parts (Leuders et al., 2013; Song et al., 2015; Shifeng et al., 2014), including orientation-induced variations in the mechanical properties (Brice et al., 2016). There has also been work that reported the influence of different types of defects on final part mechanical performance (Liu et al., 2014; Lu et al., 2015).


4.1 Effect of process parameters

The mechanical properties of the final part are affected by and related to the specific manufacturing method used, as well as to particular process parameters. Several studies have investigated the influence of manufacturing methods and process parameters on finished part mechanical properties (Abd Elghany and Bourell, 2012; Simchi, 2006; Yadroitsev et al., 2013). For example, process evaluation on Ti-6Al-4V samples manufactured by SLM and EBM revealed that yield and tensile strength of the samples produced by SLM are higher than for those produced by an EBM method. This is most likely attributed to differences in composition (including aluminium loss under vacuum), as that has been determined to have a strong influence on the mechanical properties of Ti-6Al-4V (Collins et al., 2014). However, the ductility, hardness, and consequently the fatigue strength of the samples produced by both methods are similar (Gong et al., 2015), and are less dependent upon composition.

In addition to microstructural inhomogeneity’s and mechanical property variation, the as-deposited density of AM components depends on powder characteristics, process parameters, layer thickness and scan line spacing (Simchi, 2006). Laser power in the top range of the operational window results in higher density. Increasing the thickness of layers likely decreases the final part density unless the energy density is adjusted to account for the increase in melt pool depth required. However, several parameters limit the minimum layer thickness that can be employed, such as the maximum particle size. The powder spreading mechanism can disrupt previous layers when the layer thickness is close to or smaller than the maximum particle size. This is particularly detrimental during the early stages of the build process where disturbances propagate geometric errors into the successive layers (Agarwala et al., 1995). In Simchi’s (2006) study, density seems to be linearly proportional to the scan rate on a semi-log scale.

Simchi’s study also analysed the influence of oxygen content, particle shape, size and its distribution on the porosity, and concluded that higher densities are obtained when the powder particles are fine and oxygen content is low while processing within the operational window, which likely correlates with reduced internal porosity. By decreasing scan speed and hatch distances (i.e., increasing energy density) the volumetric mass density of the resulting material increases, and, not surprisingly, has an influence on the mechanical properties. For example, the effect of layer thickness and scanning speed on tensile strength of 304L stainless steel samples was studied by Abd Elghany and Bourell (2012). Three different layer thicknesses (30, 50 and 70 μm) at two scanning speeds of 70 and 90 (mm/s) were considered. The samples with higher layer thickness were more brittle in nature due to the occurrence of higher porosity (Abd Elghany and Bourell, 2012).

4.2 Influence of defects

The existence of defects can cause parts to have poor mechanical properties under certain loading conditions. It has been found that fatigue cracks are usually initiated from stress concentrations associated with pores and LOF defects and that the elimination of these defects would significantly increase the fatigue life (Liu et al., 2014; Tammas-Williams et al., 2016). These results have also been confirmed for Ti-6Al-4V samples where porosity of 5 vol.% of the defects is shown to be a limiting factor for mechanical properties acceptance produced with a high energy density. However, it has been found


that defect occurrence at a rate as low as 1 vol.% has a considerable effect on mechanical properties. For LOF defects caused by lower energy density, even 1 vol.% of defects has been shown to strongly affect both tensile and fatigue properties (Gong et al., 2015), most likely due to stress concentrators (e.g., small radii of curvatures) in such defects. It was also found that defects closer to the surface affected fatigue life more, when compared to the defects that were deeper or far from the surfaces due to higher stress concentrations for the near-surface defects (Liu et al., 2014).

5 Application of non-destructive techniques on flaw detection and in-line monitoring of AM parts

In order to improve product quality and minimise the risk of failure caused by defects, it is important that defects be detected as early as possible in the manufacturing process. This, in principle, could then allow corrective action during the process to be taken to minimise material waste and increase both quality and yield. It would also minimise the extra costs needed for repair and rework of sub-standard items (Koester et al., 2016). Inspection and monitoring data can also be used to provide feedback and materials characterisation which can be used to optimise the manufacturing process and to determine the operational window of a particular material system and AM method.

Several reviews of current monitoring methods including non-destructive evaluation (NDE) tools, new approaches to total quality management for the characterisation of materials from metal powder to finished parts, and a discussion of in-line metrology needs and techniques for AM processes can be found in the literature (Koester et al., 2016; Bond et al., 2014; Slotwinski and Moylan, 2012; Slotwinski, 2014). A brief introduction of NDE techniques under development for monitoring AM processes is given here.

5.1 Optical inspection techniques

Optical inspection is a useful tool for NDE of parts and process monitoring and is attractive due to its low cost and ease of implementation. In an AM process, in-line vision monitoring systems are a promising candidate for defect detection and quality monitoring (Barua et al., 2011; Sparks et al., n.d.). However, there are significant challenges faced in its implementation. Several methods of visual inspection can be applied for visualisation of defects which are used with or without mechanical or optical aids.

5.1.1 Scanning electron microscopy

SEM is commonly used for obtaining images from cross sectional or other desired sections of materials after the final part is completed. Data obtained using SEM techniques can be used to analyse both the starting powders and finished parts at higher spatial resolutions than X-ray computed tomography (CT) (Slotwinski et al., 2014). SEM micrographs can be used to verify the crack formation which initiates in the brittle phase, assess microstructural variations, or coupled with other SEM-based analytical tools that can be used to obtain compositional information (via energy dispersive spectroscopy) or texture (via EBSD). However, these SEM techniques are neither in-situ nor real-time.


Further, analysis by SEM requires that metallographic samples be prepared, which is inherently a destructive method, making it only an offline process analysis tool.

5.1.2 Optical tomography

The general steps of defect detection and process control in vision systems include image acquisition, image processing, detection algorithms and a control system. For camera-based monitoring systems, images of deposited layers are usually obtained by a single lens reflective camera (Zenzinger et al., 2015). However, Iravani-Tabrizipour and Toyserkani (2007) used a trinocular optical detector composed of three CCD cameras and interference filters for real-time measurement of deposition height and used a neural network model to determine optimal threshold value for the images. Although optical [and infrared (Sames et al., 2016; Peter, 2015; Turner et al., 2015; Dehoff, 2015)] imaging systems are promising methods for defect monitoring and detection, at least in research studies, there also several challenges that will limit in-process use. These include the inability to visualise instability of the melt pool and fundamental limits of optical detection wavelengths. Real time monitoring and analysis for typical builds also generate large datasets and in-process implementation on an industrial scale machine is a non-trivial problem.

5.2 Ultrasonic techniques

Ultrasonic (UT)-based techniques have a wide range of applications in material testing and evaluation. This family of methods has been extensively applied for inspection and characterisation of conventional materials and advanced material systems (Margetan, 2012; Taheri et al., 2014). UT techniques have also shown some promise as methods for characterisation of AM materials such as porosity detection in aeronautical structures (Ciliberto et al., 2002) and in more routine application to finished parts.

In addition to defect detection, microstructure and mechanical properties of materials can be evaluated by UT techniques. In the cases non-contact UT methods such as using laser ultrasound, the advantage is to be able to be applied on rough surfaces, at higher temperature and during manufacture. The application of laser ultrasound for in-line inspection of laser powder deposition (LPD) Inconel samples with machined artificial flaws has been evaluated by Cerniglia et al. (2015). An infrared Nd:YAG pulsed laser was used as the transmitter and a continuous wave laser combined with an interferometer was used as the receiver for the generation and detection of UT waves, respectively. The results show the ability to detect micro-scale defects in layer-by-layer deposition process and this have been confirmed by use of an ultra-high sensitivity X-ray technique (Cerniglia et al., 2015).

The UT velocity is a bulk material dependent parameter that demonstrates sufficient sensitivity to detect small changes (~0.5%) in total porosity. Porosity measurement by this method has also been demonstrated to map spatial variations in porosity (Slotwinski and Garboczi, 2014). Mapping porosity, elastic moduli and density using UT techniques can also be used for material testing and evaluation, at least in a finished part (Bond et al., 2014).


UT (and acoustic) emissions from manufacturing processes can also be used for health monitoring and fault diagnosis of additive manufacturing systems. Some defect generation events produce acoustic emissions that can be monitored and located in space, such as spontaneous crack formation caused by large thermal gradients. Acoustic emission has been extensively used in monitoring and flaw detection in welding (Homsawat et al., 2015; Charunetratsamee et al., 2013). Several studies have considered its application to additive manufacturing technology (Strantza et al., 2015). Furthermore, based on previous applications giving real time NDE for different manufacturing processes (Clavette and Klecka, 2015), it appears to have the potential to be used for in-line monitoring of the inhomogeneities in parts at relatively low cost.

5.3 Electromagnetic and Eddy current techniques

Changes in electrical and dielectric properties of electrically conductive materials can be used to detect changes in capacitance due to porosity or other defects (Rogé et al., 2003; Du et al., 2013). Direct current resistivity technique appears to be capable of not only detecting cracks but also measuring hardness and density. Eddy current testing can be used for surface crack detection but it is not suited for detecting internal cracks (Brien and James, 1988). Advanced techniques and devices using eddy current techniques make it a promising method for some defect detection and inspection applications. High resolution and array eddy current techniques may enable use of this method for inspection and testing of additively manufactured materials. However, similar to conventional materials evaluation, material properties and surface finish will impact the potential application and success of the method.

5.4 X-ray radiography and CT

X-ray imaging and CT can be used for defect detection and material characterisation for either powder or finished parts. Based on several investigations (Bond et al., 2014; Du Plessis et al., 2015; Siddique et al., 2015), micro CT is now a relatively rapid and cost effective way to obtain structural information at the very early stages in a manufacturing process. The size distribution, shapes and internal features of the particle such as porosity can be determined quickly from a CT scan. Radiography techniques with image or volumetric processing can also be used to assess porosity, particle shape distribution, and size (Bond et al., 2014). X-ray CT was compared with Archimedes’ method and mass/volume measurement in Slotwinski et al.’s (2014) study to monitor porosity where 5 mm CoCr cylindrical AM samples were cut from the larger reference cylindrical disk samples, 40 mm in diameter and 10 mm thick. The measured porosity given by all three methods were similar and were used to evaluate the resultant change in UT wave speed caused by porosity (Slotwinski and Garboczi, 2014). The trend for porosity generation can also be evaluated using micro CT. Optical sections of powders have also confirmed the existence of entrapped voids in raw powder materials (Ng et al., 2009).

5.5 Thermography

Laser and electron beam sintering and melting methods of additive manufacturing are based on thermal evolution of feedstock materials. Monitoring and detection of the


temperature profile of layering steps can potentially be used for determination of the quality of the material. Thermal dissipation is influenced by the microstructural characteristics of the part. Geometrical anomalies, material loss, inclusions and voids can be detected by a number of thermographic techniques. This method is non-contact and potentially full-field making it a promising candidate for in-line monitoring and complex structures inspection similar to optical methods (Dinwiddie et al., 2013; Sames et al., 2016).

Calibrated red, green and blue (RGB) intensity values of the colours of the obtained images and radiant surface temperature can be used to approximate a value for the temperature of each pixel in an image from visible emissions in melting processes. Infrared filters, a high speed shutter, and pulsed energy delivery systems can be synchronised with image acquisition and used in conventional techniques (Kizaki et al., 1993a, 1993b). CCD cameras have been used to monitor surface temperature, to determine mass flow rate of the powder and to monitor the dimensions of the deposited track (Grevey and Vannes, 1997). The colour gradient of the melt pool and deposited track can provide a metric for process monitoring and identification of sintered and un-melted particles of powder and appears as sources of noise in image processing.

For a research system, the temperature profile shape versus pixel data can be extracted to give a signature for an acceptable deposition or for deposition over a defect (Barua et al., 2011). Some other work in this field has made some progress using similar infrared thermography (Rodriguez et al., 2012; Moylan et al., 2014b), near-infrared thermography (Price et al., 2012) and thermography approaches for in-line monitoring (Krauss et al., 2012).

Table 3 summarises the application of NDE methods for defect detection and material evaluation of AM parts. Table 3 Comparison of potential and capabilities for application of NDE methods for defect

detection and material evaluation for finished additive manufacturing parts

Defect

NDE technique

Defect of material characteristics

Porosity Crack Mircrostructural anomalies

Geometrical anomalies

Mechanical properties

Electromagnetic properties

Residual stress

Visual C* C1 A A N N N Ultrasonic A A A B A N B Electromagnetic B A D B N A C Radiography A A C A N N A Thermography D B D B N N N

Notes: * A – applicable; B – possible/needs development for use in AM; C – low probability of successful application to AM; D – not applicable to AM. 1Larger cracks can be detected by visual inspection when the part condition is closer to failure which is not desirable.

6 Effect of defects on design consideration and material applications

As discusses earlier, the existence of defects can change final part material properties and quality. In-line monitoring and early detection of these defects will enable subsequent


process control to modify the AM process, achieve higher deposition quality, and reduce or eliminate avoidable costs. Non-destructive testing (NDT) and NDE of the parts can also define the quality of the finished part with resulting data used to predict material quality and performance. NDE methods have practical limits in terms of feature resolution and pairing this with the application design constraints can identify a potential NDE method to meet the detection need. Post-processing methods such as HIP can eliminate some of the defects which could occur in the finished parts that tend to be on the lower end of detectability by NDE methods. HIP which was originally developed for diffusion bonding of nuclear reactor components in 1955 (Saller et al., 1956), is the application of high isostatic pressure (via an inert gas) at elevated temperature on the final AM part (Atkinson and Davies 2000). Currently, HIP is widely used for consolidation of metal powders and quality improvement in additively manufactured parts (Gaytan et al., 2009; Lu et al., 2015). HIP is generally used to reduce porosity in the final parts. However, HIP cannot eliminate all porosity. The performance of HIP in eliminating porosity depends on the type of the material and quality of the powders used (Gaytan et al., 2009). Several studies have shown the influence of the geometry and size distribution of the defects on mechanical properties and how HIP affects them. Atkinson and Davies (2000) report that an increase in microporosity of 13% in 1 wt pct Cr-0.25 wt pct Mo cast steel decreases the impact toughness from 145 to 110 (J/cm2). The mechanical properties of Ni-Al bronze (AB2) containing 10 to 20 vol.% porosity has also been compared to as-cast and hipped (HIP) conditions. The results show that HIP improves the properties, including a 6% increase in 0.2% proof stress, 34% increase in ultimate tensile strength and 117% increase in elongation (Atkinson and Davies, 2000). These trends of improvement can also be seen in fatigue and creep life (Atkinson and Davies, 2000) and are attributed to shrinking or eliminating internal porosity. An X-ray CT study by Tammas-Williams et al. (2016) have shown that HIPing on Ti-6Al-4V parts manufactured by selective electron beam melting eliminated all internal porosities, considering the limiting factor for the detectable nominal size of porosities (i.e., undetectable below 5 μm), which coincided with the detection limits of the equipment. Not surprisingly, HIP has also been shown to increase the density in components by reduction or elimination of porosity (Lu et al., 2015; Ordas et al., 2015) and decrease scatter in fatigue and tensile properties often seen in AM components (Tammas-Williams et al., 2016; Lu et al., 2015).

7 Standardisation of materials and methods

With the increasing use of AM parts and the number of AM methods available, there is a need to establish standard specifications for materials, processes, designs and characterisation. Several standard specifications have been published or are under development by American Society for Testing and Materials (ASTM). A listing of some current standards is given in Table 4 (ASTM International, 2016).


Table 4 ASTM standard specification for different materials and methods in additive manufacturing1

Designation Title

Terminology: ISO/ASTM52900 Standard terminology for Additive Manufacturing – General

Principles – Terminology ASTM F2792 Standard Terminology for Additive Manufacturing Technologies Design: ISO/ASTM52915 Standard Specification for Additive Manufacturing File Format (AMF)

Version 1.2 Materials and processes: F2924 Standard Specification for Additive Manufacturing Titanium-6

Aluminum-4 Vanadium with Powder Bed Fusion F3001 Standard Specification for Additive Manufacturing Titanium-6

Aluminum-4 Vanadium Extra Low Interstitial (ELI) with Powder Bed Fusion

F3049 Standard Guide for Characterizing Properties of Metal Powders Used for Additive Manufacturing Processes

F3055 Standard Specification for Additive Manufacturing Nickel Alloy (UNS N07718) with Powder Bed Fusion

F3056 Standard Specification for Additive Manufacturing Nickel Alloy (UNS N06625) with Powder Bed Fusion

F3091/F3091M Standard Specification for Powder Bed Fusion of Plastic Materials Test methods: F2971 Standard Practice for Reporting Data for Test Specimens Prepared by

Additive Manufacturing F3122 Standard Guide for Evaluating Mechanical Properties of Metal Materials

Made via Additive Manufacturing Processes ISO/ASTM52921 Standard Terminology for Additive Manufacturing-Coordinate Systems

and Test Methodologies WK249798 New Guide for Intentionally Seeding Flaws in Additively Manufactured

(AM) Parts WK49229 New Guide for Anisotropy Effects in Mechanical Properties of AM Parts WK49272 New Test Methods for Characterization of Powder Flow Properties for AM

Applications WK47031 New Guide for Nondestructive Testing of Additive Manufactured Metal

Parts Used in Aerospace Applications

Notes: 1Because of continuous update in ASTM standard specification, the most recent edition of each document should be considered from ASTM.

2WK: Under development at time of report.

8 Summary

Each AM material forming processes has characteristics associated with the incident heat source, material feed stock, and material transfer mechanisms which combine to


influence physical processes of the molten pool and which determines properties of the final products, including the potential for occurrence of flaws and anomalies in the parts. The so-called ‘material state’ and allowable manufacturing anomalies for additive manufactured materials are still the subject of investigation. The nature of additive manufacturing provides opportunities to implement new approaches to defect assessment during processing. These new approaches are still under developed.

In order to select the appropriate detection and monitoring methods, it is essential to understand the different types of defects, their critical sizes and how and when they evolves during processing. Knowledge of how and when the types of defects appear will increase the potential for early detection of defects in additively manufactured parts, offers the opportunity for in-process intervention and decrease the time and cost of repair or rework. Additive manufacturing encompasses a wide range of materials, processes and coupled factors that affect the type and properties of defects that can be generated. Porosity, cracking, microstructure and geometrical anomalies are among the most common types of defects encountered that can significantly alter the mechanical properties of the finished parts. Fatigue resistance appears to be the property most sensitive to these types of defects, based on data reported in the literature, although it must be emphasised that the literature tends to be limited to tests that can be conducted on small specimens and in a typical laboratory setting. Thus, other weak-link driven properties (e.g., fracture toughness), slower tests (e.g., creep), or less common tests (e.g., torsional or shear tests) may occur. It has been already found that tensile strength will drop considerably if the volume fraction of porosity increases above ~1%. Surface roughness and crack generation are significantly affected by process parameters such as scanning speed and energy density. Speed of crack growth can have a considerable influence on fatigue resistance.

Several non-destructive techniques have been identified for detection of defects, process monitoring and evaluation of materials in AM parts and are in varying stages of development. Among these methods UT and radiographic techniques appear the most promising. Non-contact implementations of methods of these NDE techniques do appear to have more potential for use in quantitative in-line monitoring and defect detection.

Acknowledgements

The aspects of this paper were developed as part of a Core Project review and planning activity performed by the Center for NDE (CNDE), Iowa State University, while a Phase III NSF Industry University Cooperate Research Center (IUCRC). CNDE became a graduated Center on January 1st 2016. The author would like to thanks the various sponsors who have provided insights and additively manufactured samples. The PCC would like to acknowledge support from the National Science Foundation Grant No. 1606567 (‘DMREF/Collaborative Research: Collaboration to Accelerate the Discovery of New Alloys for Additive Manufacturing’), the National Science Foundation Grant No. 1641143 (‘I/UCRC for Advanced Non-Ferrous Structural Alloys’), and DARPA Contract HR0011-12-C-0035 (‘An Open Manufacturing Environment for Titanium Fabrication’).


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Notes 1 The authors would like to provide a side comment on the term ‘defects’, a technical term that

can create a negative perception regarding a material or process. In this paper, the term ‘defect’ is used in the traditional sense of a deviation away from a perfect material (i.e., a microstructural anomaly or discontinuity – e.g., a pore). In this context, all materials have defects. The size and type of defect is what is important. Further, in keeping with a fundamental understanding of materials, the term ‘defect’ is not meant to imply a loss of functionality. Rather, defects may reduce the lifetime of components under cyclic loading, or reduce some properties in a probabilistic sense under some defined set of stressors.

2 Sintering in AM is simply partial melting and is a legacy term for incomplete consolidation (ASTM F2792). The use of this term is understood to be a distinction between a partial and full melting process, especially for metallic materials.

3 Sintering and complete melting represent the extreme ends of a continuum that includes partial melting. The degree of melting depends upon the energy density. We adopt these terms to reflect the legacy publication and research.

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