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Citation for the original published paper (version of record):

Wang, X V., Wang, L. (2049)"Digital twin-based WEEE recycling, recovery and remanufacturing in the backgroundof Industry 4.0.International Journal of Production Research, 57(12): 3892-3902

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Digital Twin-based WEEE Recycling, Recovery and Remanufacturing in the

background of Industry 4.0

Xi Vincent Wang*, Lihui Wang

Department of Production Engineering, KTH Royal Institute of Technology, Sweden

* Corresponding Author:

Xi Vincent Wang

Tel.: +46 8 790 9024; fax: +46 08 21 08 51

Brinellvägen 68, 114 28 Stockholm, Sweden

Email: wangxi@kth.se (Xi Vincent Wang)

Lihui Wang

Tel.: +46 8 790 8305; fax: +46 08 21 08 51

Brinellvägen 68, 114 28 Stockholm, Sweden

Email: lihuiw@kth.se (Lihui Wang)

2

Digital Twin-based WEEE Recycling, Recovery and Remanufacturing in the

background of Industry 4.0

The Waste Electrical and Electronic Equipment (WEEE) recovery can be categorised

into two types, i.e. recycling at the material level, and remanufacturing at the component

level. However, the WEEE recovery is facing enormous challenges of diversified

individuals, lack of product knowledge, distributed location, and so forth. On the other

hand, the latest ICT provides new methods and opportunities for industrial operation and

management. Thus, in this research digital twin and Industry 4.0 enablers are introduced

to the WEEE remanufacturing industry. The goal is to provide an integrated and reliable

cyber avatar of the individual WEEE, thus forming personalised service system. The

main contribution presented in this paper is the novel digital twin-based system for the

WEEE recovery to support the manufacturing/remanufacturing operations throughout

the product’s lifecycle, from design to recovery. Meanwhile, the international standard-

compliant data models are also developed to support WEEE recovery services with high

data interoperability. The feasibility of the proposed system and methodologies are

validated and evaluated during implementations in the cloud and cyber-physical system.

Keywords: WEEE, Waste Electronics, Remanufacturing, Digital Twin, Industry 4.0

1. Introduction

The modern Information and Communication Technology (ICT) opens new doors for mostly

all the engineering fields. It provides new ways of connection, communication and integration

that string different resources and processes together and then form a dynamic, up-to-date, and

reliable cyber environment. Smart sensors, Radio-Frequency Identification (RFID), smart tags,

GPS, and networked devices provide rich connectivity and possibility of utilising and

managing hardware remotely. Among different engineering scenarios, Waste Electrical and

Electronic Equipment (WEEE) requires more attention due to its nature of high variety,

uncertainty and unpredictability.

3

Even though WEEE Directive and Restriction of Hazardous Substances (RoHS)

Directive define the producer’s responsibility for collection and recycling (EU 2012; European

Parliament 2008), unfortunately in practice, it is challenging to track product status in the

current recycling chain. In recent research, the remanufacturing process is introduced to the tail

of the product lifecycle, to recover the resources at the component level. The functioning parts

inside WEEE are either repaired, refurbished, reconditioned, reprocessed, remanufactured,

replaced, and then put on the secondary market or, if possible, sold to the new customers. In

that case, the remanufacturer requires a considerable number of knowledge and data about the

WEEE before it can be processed. For instance, the collector needs a good understanding of

the WEEE to evaluate the status and functionality before distributing them to the proper

stakeholder. Similarly, the remanufacturer need deep knowledge of the product components,

before some of them are repaired or remanufactured. In practice, a big portion of the product

life-cycle information is lost during the product development and manufacturing stage

(Kurilova-Palisaitiene et al. 2015). As illustrated in Figure 1, the data stream is also interrupted

after the product is sold to the end user. The WEEE collector and remanufactures need to

rework and recreate the life-cycle information by themselves by different channels and

methods. The data suspension is partially caused by the end users’ lacking product/recycling

knowledge; and on the other hand, it relates to the absence of feasible system or methodology

allowing end users to view and update the product status. After the product reaches the end of

the service period, the collector gathers the unwanted devices as WEEE. In some cases, a user

can sell his or her used EEE to a dealer and get a refund or discount from it when buying a new

EEE. In that case, the dealer partially plays the role of a collector. At this phase, the professional

collector may be able to retrieve some information about the product based on the limited data

provided by the producer. A bigger portion of product data is re-generated based on

examinations, collectors’ knowledge and experience about specific types of goods and/or

4

models, and so forth. To the end, these limited results are transferred to the recyclers. It is

evident that the conventional WEEE recycling method does not provide an effective

communication and management approach, and a big amount of data and knowledge is lost

along the process, especially at the product development, manufacturing and service phases

(Kurilova-Palisaitiene et al. 2015). Thus, this paper aims to tackle the connectivity and

integration issue along the WEEE recycling/remanufacturing chain.

Figure 1. Conventional WEEE Data Flow

2. Literature Review

In the past years, different systems and methods have been proposed to improve the

performance of WEEE remanufacturing/recycling system. In this section, recent WEEE

research is reviewed, and the research on digital twin is also discussed as a potential supporting

technology for WEEE recycling/remanufacturing.

2.1. Networked WEEE Management and Tracking

Due to the distributed nature of WEEE, it is a logical thinking to track and manage the

WEEE over the network. Much of the research work is successful in tracking the WEEE flow

in large scale, e.g. in the unit of tonnes (Li et al. 2013, 2006; Afroz et al. 2013; Habib et al.

2015). However, an efficient mechanism of bridging individual devices to the information

system is still missing. In some countries like South Korea, the recyclers are legally required

to report their recycling data to EcoAS (Electrical and electronic equipment and vehicle ECO-

Assurance System in Korea) (Korea Environment Corporation 2017). Even through EcoAS is

EEEEEE WEEEMaterial/

Component

Producer/Distributor End User Collector Recycler/Remanufacturer

5

a web-based system, all the information need to be input manually, and still to a significant

scale, which leads to low efficiency and poor data integrity.

In the data domain, Kuo (2010) proposed a web-based collaborative-design platform

to support WEEE recycling. In this approach, all the component data, including the

components outsourced by the suppliers and vendors, need to be streamed to the web-based

system to support the later WEEE recycling analysis by offering related environmental

information. The researchers from Portugal (Carvalho et al. 2010; Oliveira et al. 2011)

developed a spatiotemporal static and dynamic database. In their research, the area, location,

road and waste bin information is integrated into the geographic information system

supporting the reverse WEEE logistic chain. The above-mentioned research provides feasible

technologies to connect multiple WEEE stakeholders in different scales. However, there is

still a lack of a systematic and dynamic approach to integrate all the related processes and

technical knowledge.

RFID, as indicated by the name, is a wireless device for identification and tracking. It

is popular in the WEEE research due to its portability, accuracy, and low cost. Thoroe et al.

(2011) proposed a networked system for extended producer responsibility and recycling for

WEEE. In the proposed system, RFID tags play as the middleware connecting the wastes in

the physical world with the software applications in the digital world. An integrated system

was also developed using RFID to track the location of WEEE (Sun et al. 2013). The tracking

service is capable of tracking, reporting and integrating the status of WEEE parts across the

product lifecycle. Similarly, Xavier et al. (2014) utilised RFID for cost assessment on reverse

logistics management. RFID is capable of identifying and tracking the WEEE, but it is not

suitable to maintain dynamic WEEE data due to its limit of data capacity and hardware binding.

While RFID is collecting device data at the low level of the system, the cloud is widely

chosen to integrate the WEEE information at the high level. Baryannis et al. (2017) suggested

6

Quality of Service (QoS) profiles describing technical specifications for applications in the

Service-Oriented Architecture (SOA). In this research, Web Service Specification Language

(WSSL) is utilised to provide the description capabilities. Bekkouche et al. (2017) proposed an

improved Harmony Search algorithm aiming to the optimised result respect the global QoS

constraints. Zhang et al. (2010) developed a WEEE recycling system based on the cloud. In the

proposed system, SOA is also introduced to manage the value-adding during the co-creation

service process. An information framework for WEEE remanufacturing was suggested in the

cloud environment based on semantics and cloud (Xia, Gao, Chao, et al. 2015; Xia, Gao, Wang,

et al. 2015; Xia et al. 2014). In this research, planning and optimisation algorithms are also

developed for WEEE disassembly.

To recap, the research achievements in the networked WEEE remanufacturing system

focus on either the data acquisition or system integration. The connection between these two is

very loose, and the data interruption issue mentioned in the previous section is still unsolved.

Thus, it is necessary to develop a feasible method to collect and integrate the dynamic WEEE

data from the physical world. Among different methodologies and technologies, digital twin is

a good candidate to tackle this issue.

2.2. Digital Twin

The original concept of the digital twin was proposed in NASA’s integrated technology

roadmap in the area of modelling, simulation, information technology and processing (Shafto

et al. 2010, 2012). It indicates the “simulation of an (aerospace) vehicle or system that uses the

best available physical models, sensor updates, fleet history, etc.” The key spirit is establishing

an ultra-realistic model and mirroring it to its corresponding flying twin. Beyond simulation,

Glaessgen and Stargel (2012) interpreted the digital twin as the virtual digital fleet leader and

pointed out its importance to design, verification, testing, monitoring, prediction, health

management and maintenance by improving the reliability and safety. Tuegel et al. (2011; 2012)

7

further extended the digital twin from aerospace scenarios to aircraft. A structural digital twin

system was developed with a focus on the damage and life prediction. In the proposed system,

stress, temperature, and vibration assessments were undertaken based on the tracking database

and prediction model aiming at higher reliability.

Even though the concept was originally proposed for the aerospace and aviation

industry, in the past years the research on digital twin was gradually extended to manufacturing

science. Rosen et al. (2015) pointed out the digital twin’s important impact on the future

manufacturing industry, under the background of Industry 4.0. Besides simulation, digital twin

is predicted to play a major role in decision making inside an autonomous system, which even

addresses both the product and process levels for parts, machines and factories (Posada et al.

2015). New Industry 4.0 services were also suggested based on the digital twin, to generate

insights that can be utilised for the future car design and optimisation (Rüßmann et al. 2015).

At the design phase, Boschert and Rosen (2016) proposed an information system using

digital twin across the product design, engineering, operation and service phases. In this system,

the digital twin was utilised to string different existing IT systems, i.e. product lifecycle

management, product data management, supervisory control and data acquisition. At the

operation stage, an information architecture was proposed to offer individualised smart services

based on condition monitoring. Meanwhile, the digital twin was built based on the monitoring

results (Dreyer et al. 2017). In this research, a test run was conducted based on the machining

data from the production site. Canendo (2016) presented an abstract model connecting digital

twin to the industrial IoT lifecycle. The design, operation, and service phase of industrial IoT

were improved with information feedback and feedforward flows between them.

To recap, the WEEE management needs to be further improved in system integration,

data utilisation and connectivity. The digital twin approach shows high potential in merging

the data from the physical world and the software system in the cyber world. Its capability and

8

potential has been partially proven by research works extending the application scenarios from

simulation to manufacturing systems. Until now, no research applies digital twin in the WEEE

recycling or remanufacturing field. Therefore, this research proposes a novel approach to

establish digital twins for WEEE. Compared with the current WEEE approached mentioned

before, digital twin provides a tightly coupled avatar reflecting the actual status of the WEEE,

as they are normally deviated after serving for a different period of time in different

environments. It also decreases the system weight by reducing reliance on statistical

distributions based on assumptions. Instead, the actual product data provides reliable

knowledge of individual WEEE, which supports the remanufacturing process planning

directly.

The conventional digital twins normally involve only one processing system by one

stakeholder. However, in the WEEE recovery domain, the digital twins need to be shared

among multiple application systems involving multiple stakeholders. To combat these new

challenges, this research develops an innovative system architecture and standardised data

model specifically to establish a feasible digital twin environment for WEEE.

3. Digital Twin for WEEE Recovery

This research aims to answer the research questions including 1) how to conduct a WEEE

recovery system from the Industry 4.0’s perspective, and 2) how to develop the Digital Twin

as a cyber-avatar for the WEEE. The trend of Industry 4.0 is clear, which consists of the Internet

of things, cloud computing and cyber-physical systems as the three essential pillars. In recent

years, big research efforts have been devoted to the RFID-based WEEE recycling, which aligns

with the concept of the Internet of Recyclable Things (IoRT). Multiple cloud systems have been

developed to support the high-level data integration and WEEE management (Wang et al. 2015;

Wang and Wang 2014). Hence, two pillars for the next generation of WEEE recovering, which

9

can be defined as WEEE 4.0, are in place. Eventually, to archive a reliable cyber-physical

system, digital twin can play the key role of bridging between the cyber world (cloud) and the

physical world (RFID/smart tag network).

As illustrated in Figure 2, the WEEE digital twin is initiated based on the cyber

knowledge from the product design phase. The geometry, components, material, and most

importantly hazardous substances are integrated and maintained in the product documentation.

The physical and environmental knowledge of the product is also embedded in the product 3D

model, which forms the initiation of the digital twin for WEEE recovery.

Figure 2. Digital Twin for WEEE Recovery

When the product is sold to an end user, he or she can update the product status via

various Industry 4.0 enablers, e.g. mobile apps, smart tags, websites, and so forth. More details

of the enabling technologies can be found in the remainder of this section. At this phase, the

changes of product, e.g. location, ownership, upgrading, repairing, and maintenance, can be

updated and maintained inside the mirrored digital twin.

When the EEE device stops service, the users can update the digital status via mobile

app or web service. He or she can contact a professional collector who has the expertise about

Producer/

DistributorEnd User Collector Recycler/

Remanufacturer

Product Design Product StatusLogistic and

Exam results

Recovered Meterial

or Component

Industry 4.0 Enablers

Industry 4.0 Enablers

Ph

ysic

al

Wo

rld

Cyber

Wo

rld

Dit

ita

l

Tw

ins

10

this specific device. In that case, the sorting process is not necessary anymore, since the WEEE

is already sorted during the interaction between the end users and the collectors. Otherwise, if

the user is not familiar with the recycling experts or process, the mobile app or web service can

suggest the best disposal point nearby to discard the WEEE properly. After the WEEE is

transported to the collectors, more detailed evaluation and testing can be applied to the

individual equipment. Based on the examination results, the digital twin can be updated, and

the proper operation can be planned accordingly, e.g. recovery at the component level, material

level, or discard directly.

Eventually, the route of the collected WEEE is decided based on the latest data

maintained in its digital twin. The recycler or remanufacturer can take in the waste devices and

initiate the operation without the need of additional test or evaluation, as all necessary data is

stored in the digital twin already. Except for the discarded parts, the recycled material can be

utilised by the producer again for new products, and the recovered components are either

reconditioned, repaired or reproduced. All the recovery and reconditioning information is

recorded and stored in the ingredient specification, as part of the new product’s digital twin.

Hence, it forms a continuous and sustainable data flow, while the full knowledge of the product,

component and material is well maintained in the digital twin for future product analysis and

decision-making.

3.1. System Architecture for a Digital Twin-enabled Cyber-Physical System

The system architecture of a WEEE cyber-physical system centred by a digital twin is

shown in Figure 3. The system is established based on the Service-Oriented Architecture

(SOA), in which both the electronic device and its digital twin are considered as the service

objectives (Baryannis et al. 2017; Bekkouche et al. 2017). As mentioned, the digital twin plays

as the bridge between the physical world and the cyber world. As the product moves from the

Beginning-Of-Life (BOL) to Middle-Of-Life (MOL) and End-Of-Life (EOL), the knowledge

11

and information maintained inside its digital twin becomes bigger and richer simultaneously.

As an integrated and centralised knowledge resource, the digital twin provides rich information

to support the production and recovery operation throughout the product lifecycle. The system

structure answers the first research question regarding how to how to conduct a WEEE

recovery system from the Industry 4.0’s perspective.

Figure 3. A WEEE Digital Twin-enabled Cyber-Physical System

In the cyber-physical system for WEEE, the digital world is supported and hosted by

cloud computing environment. The cloud works as the container and provides the

manufacturing and remanufacturing modules, so-called CAx modules when they are needed.

The product design begins in the Computer-Aided Design (CAD) module, where the designers

document the functional and environmental features of the product from the beginning. The

Computer-Aided Engineering (CAE) module helps to simulate the performance to validate and

improve the product design. At this phase, the success of traditional digital twin approach in

the simulation can be inherited and further enhanced by the strong computing power from the

cloud. Additionally, the environmental performance and impact can be simulated at this phase

via Life-Cycle Assessment (LCA) module too, e.g. design for recycling, design for disassembly

and design for remanufacturing. The simulation results and disassembly are maintained in the

product archive for future remanufacturing operations.

Product DesignProduction System

EngineeringProduction Planning/

Excecution

Digital Twins

CAD CAE MES CALSRecycling

ModuleCAM

Logistics

End User

App

Product Status

Cloud-based System

WEEE Data

Design Engineering Manufacturing Service CollectingRecycling/

Remanufactuirng

Monitoring Databases

Digital Twin

Ph

ysic

al

Flo

w

Dig

ial

Tw

in

Kn

ow

led

ge

Servi

ce

Flo

w

Cyber

Wo

rld

Remanufactu

ring Modules

PLM ERP

12

During manufacturing processes, the operations are controlled by the Manufacturing

Execution System (MES). With the help of multiple Industry 4.0 enablers, e.g. smart monitors,

wireless sensor systems and other Internet of Things, the digital twin is strengthened by the

related operation and product performance data collected from the factory. At the BOL phase,

the manufacturing process, operations and data are coordinated by the Product Lifecycle

Management (PLM) and Enterprise Resource Planning (ERP) systems.

After an end user purchases the product, the user can interact with the digital twin via

multiple Industry 4.0 enablers. For example, RFID reader is an affordable device for general

usage, and Near Field Communication (NFC) is available in many smartphones by default.

Even if the methods mentioned above are not available, 2D tags can be a feasible tool for

WEEE recycling. In the latest version of QR (Quick Response) code, up to 1264 characters can

be recorded in one printed tag. Nowadays a smartphone with a camera is common. The user is

capable of scanning the tag attached to the electronic device, to get the basic technical and

environmental information of the device easily (Figure 4). Then the change of product

condition, e.g. update, repair, change of parts, and so forth, can be updated either from the

mobile apps or through web services. It needs to be noticed that before the users’ update, all

the digital twins of the products in the same model are universal. That means all the goods on

the market can share the same version of digital twins. However, after the individual user makes

changes to his/her product, the change will be linked to the unique product ID, and the universal

digital twins will become distinguishable. Even though the printed QR code tag is static and

unchanged, the digital twin linked to this unique code is updated remotely. Every digital twin

becomes unique, which supports different recycling and remanufacturing operations in the

EOL phase. Here is how individualised product and personalised service initiated and realised.

13

Figure 4. Digital Twin Supported by QR Code

After the product stops service, related collecting and recycling service is ordered based

on the latest and real product status maintained in the system. Reasonable logistic strategy for

collection can be generated via the Computer-Aided Logistics System (CALS). When the

WEEE reaches the remanufacturer, the proper recycling process can be specifically planned

according to the product history. For example, during reconditioning, recently replaced

component is issued with a longer warranty period while the repaired ones shorter. Eventually,

the product is disassembled, sorted, and recycled according to the exact status. The cloud-based

system shows its capability of covering the whole product lifecycle from the beginning to the

end. It needs to be admitted that the interfacing and integration among multiple

software/hardware systems is a critical task due to compatibility and interoperability challenges

(Wang and Xu 2013; Wang et al. 2017). Hence, data models based on the international standard

is developed to support the dynamic interactions in the proposed system.

3.2. Data Model Supporting Digital Twin

In the proposed digital twin system, it is obvious that the dynamic data stored inside the

digital twin works as the centre of the whole product lifecycle. It is thus critical to maintain the

knowledge in a centralised and standardised method. In the past years, the system integration

upgrade

repair

part replacement

Unique QR

code/ID

Universal Digital

Twin

Personalised

Digital Twin+

14

of manufacturing and remanufacturing processes is challenging as the software/hardware

applications are developed by multiple vendors in different formats and standards. Hence, it is

difficult to integrate and utilise the outputs from these applications. In this research, the current

ISO standard models are extended to the WEEE and remanufacturing domain (ISO 1994, 2004,

2016). The outline of the developed data structure is shown in Figure 5.

Figure 5. Data Model for WEEE Digital Twin

Entity Digital_Twin is the core of the data model set. The technical specifications of a

digital twin are described by multiple attributes. In a typical digital twin approach, the attributes

and descriptions are conducted from the geometry, design or functionality’s perspective.

However, those approaches are not sufficient to support the multi-system-multi-stakeholder

requirement for WEEE. Thus as a unique development of the WEEE digital twin, the data

attributes are conducted based on three broad categories, i.e. BOL, MOL and EOL data. The

EEE User and Service Provider - MOL

EEE Producer - BOL WEEE Recycler – EOL

(ABS)Service

WEEE Remanufacture - EOL

WEEE_Remanufacturer

Product_EEE_Specification

(ABS)Digital_Twin

Producer

Recoverable_Component

Recoverable_Material

its_producer

End_Userits_end_user

Remanufacturing Service

its_producer its_provider

its_provider

WEEE_Recyler

Recycle_Service

its_remanu_handler

L[0:?]

L[0:?]

(ABS)Service_Provider

L[0:?]

L[0:?]

Component

Material

Product_Status

its_material

its_component

status_change

Inspection_Service

L[0:?]

its_status

its_BOL_MOL_spec

L[0:?]

its_service L[0:?]

Repair_Service

L[0:?]

its_service L[0:?]

EEE_Service_Provider

material_upgrade

component_upgrade

WEEE_Specification

Upgrade_Service

Maintenance_Service

Assembly_Spec

Disassembly_Sepc

its_assembly

its_disassembly

its_disassembly its_material L[0:?]

its_input L[0:?]

its_service L[0:?]

its_input L[0:?]

L[0:?]

its_service L[0:?]

its_EOL_spec

its_part_and_component L[0:?]

its_recycle_handler

its_customer

Reconditioning_Service

L[0:?]

its_service L[0:?]

its_input L[0:?]

15

BOL data is stored in the Product_EEE_Specification entity, which is originally provided by

its Producer. Here the extended responsibility of the producer is addressed by the attribute

connection from the producer to both EEE and WEEE entities. The initial recycling and

recovering information are stored in the Material entity, which keeps the information about the

critical ingredient, and Component entity for critical and recoverable parts. At the operation

level, the assembly and disassembly specifications are in place to support the recycling and

recovering process later. After the product specification is initiated, it is accessible in the public

platform, e.g. public Cloud, to support the next phase of Digital_Twin operation.

After the product is sold to the end user, he or she can register the ownership of the

product based on the unique Product_ID. As mentioned earlier, this is the phase for the

Digital_Twin models turning from universal into unique. At this phase, both the End_User and

EEE_Service_Provider have access to the MOL-stage product specification, and the possibility

to work with the digital twin model actively. Some of the operations does not affect the product

specification, e.g. Repair_Service, Maintennance_Service, Inspection_Service, while others

change the Material or Component entity mentioned before, e.g. Upgrade_Service. The users

can update the Status entity, including location, ownership, product life, etc.

When the product reaches the EOL phase, the WEEE_Specification plays as the main

sub-entity of the Digital_Twin. The knowledge defined in entity WEEE_Specification is

interlinked to the Digital_Twin with a focus on recycling and remanufacturing operations. They

include the details about Material and Disassembly_Spec strategy to recover the valuable

materials like plastic, steel, aluminium and precious metals. Based on this information, the

related component recovery and reconditioning/remanufacturing services can be planned and

executed by their service providers.

Note that the recycler, remanufacturer and original product producer are all sub-entities

of general service providers. That means one company can have multiple roles in the system if

16

needed. For example, if the original product producer is willing to recycle the material and

some of the components for the manufacturing of new products, it can also serve as the WEEE

service providers and stream the recovered material/component directly to the production flow.

To recap, the proposed work supports the digital twin operation from the system level

to the data level. The ISO-compliant data model sets guarantee the versatility and adaptability

for current and future applications. It answers the second research question regarding how to

develop the Digital Twin as a cyber-avatar for the WEEE.

4. Implementations

To validate and evaluate the feasibility of the proposed system, a cloud-based cyber-physical

system is conducted in the laboratory environment. The cloud consists of two servers with 24

CPU cores to provide the computing power of design, simulation and WEEE management. As

one of the most significant advantages of the cloud system, the resource assigned to the digital

twin can be elastic and flexible. When the digital twin is initiated, the basic amount of resource

is allocated to the environment. If the digital twin needs more computing resource, more

computing cores, memory and space can be distributed to the WEEE environment dynamically.

In this work, two CPU cores, 4 GB memory and 500 GB space are allocated to the test

environment, which is equal to a standard workstation’s computing power.

Table 1. Cloud-based Digital Twin Environment

Device System Specification Assigned to Test Environment

Computing Server HP ProLiant DL120 Gen9 8SFF x 2 1

CPU Intel Xeon E5-2620v3 2.4 GHZ Intel Xeon E5-2620v3 2.4 GHZ

Processor Cores 24 2

Memory 64 GB 4 GB

Disk Space 2 TB 500 GB

Operating System Ubuntu Server MS Windows

System Type 64-bit 64-bit

Graphics Card Standard Virtual VGA Graphics Standard Virtual VGA Graphics

17

To establish the WEEE digital twin, multiple services modules are developed in the

cloud environment. The Clara.io web service is utilised to create and modify the product model

in 3D space (Figure 6a). Here the product design is created, and the related assembly and

disassembly information can be added by the product producer via the data management tool

compliant with the ISO standard models (Figure 6b) (Wang and Xu 2015).

After the product is transferred to the end user, he or she is able to scan the QR code

mounted on the electric device and access the public front end of the EEE management system

(Figure 6c). It needs to be noted that the supporting modules developed in the system can be

easily accessed on both computers and mobile devices, e.g. smartphones and tablets. Based on

the unique product ID on the physical QR tag, the virtual product model is tightly connected to

a specific product in the physical world. Thus, a meaningful digital twin is established at this

stage. The end user can update the product status, e.g. location, change of ownership, upgrading

records, etc., easily via mobile devices.

When the product reaches the EOL, WEEE remanufacturing service model can

interpret the information stored in the QR code, and then retrieve more detailed EEE and WEEE

data stored inside the digital twin (Figure 6d). Based on the Cloud rich data, the system is able

to identify proper recycling and remanufacturing operations, to search for suitable service

providers close to the current WEEE location, as well as to arrange collection service

automatically. After the WEEE is collected, the recycler or remanufacturing can generate

operation plans based on both the knowledge stored in the digital twin and on-site evaluation

results. The recycling or remanufacturing service output is also documented in the Material

and Component entities inside the digital twin model eventually. Thus in the implementations,

the entire product lifecycle is integrated into the digital twin environment from design to

recycling/remanufacturing, and the feasibility of the proposed system is proven.

18

(a)

(d)

(b)

(c)

Figure 6. Cloud Services Supporting Digital Twin: (a) Design Tool View from Smart

Phone, (b) Data Management Tool, (c) QR Code-based WEEE Tracking Mechanism from

Smart Phone, and (d) WEEE Management System in the Cloud

5. Conclusions

The modern ICT technology has changed the way of life and business dramatically in the past

ten years. It provides new ways of living, thinking and doing business. Pushed by new

technologies like the cloud, Internet of Things and Cyber-Physical Systems, Industry 4.0

represents the trend of smart factories and smart devices with easy connectivity, high

interoperability and data transparency. These new trends also provide new opportunities to the

WEEE remanufacturing. Thus this research targets the challenges of heterogeneous objects,

distributed locations, lack of background knowledge, and so forth in WEEE. The main

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contributions reported in this paper include 1) extending digital twins to support the

EEE/WEEE manufacturing and remanufacturing for the first time, 2) integrating the Industry

4.0 enablers around the digital twin, which works as the central player in the proposed WEEE

recycling system, and 3) developing the product information models from design to

remanufacturing based on international standards. The supporting methods and applications

are also developed and validated through implementations. In the future, more quantifiable

comparisons are suggested on the cloud-based approaches against conventional systems.

It is admitted that there are certain limitations in the current system. The willingness and

support of the stakeholders are critically essential. In many cases, the original producer may

not want to expose the product design and technical information to the public to protect its

intellectual property. Thus, it is essential to define a proper boundary of the digital twin, by

sharing the recycling/remanufacturing information, without infringing the intellectual property.

At the policy level, the standard and execution of extended producer responsibility need to be

further improved, to encourage the engagement of WEEE stakeholders, especially the

producers. At the society level, education and dissemination facing general public is also

needed to prompt the importance and process of WEEE handling. Additionally, at the

technology level, the WEEE stakeholders normally do not necessarily have the full knowledge

of the whole product lifecycle. Hence, it requires robust and easy-to-use systems to support the

stakeholder’s engagement with limited knowledge and experience.

In the Industry 4.0 era, it has to be noticed that the security and safety of the digital twin

in the system is another critical challenge. The WEEE management system can learn from the

success of other web-based systems, e.g. e-business, online stocking and trading platforms. The

wide access to the EEE/WEEE data needs to be protected by multiple methodologies, e.g.

firewall, access control, authentication management, private keys, encrypted communication,

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secure tokens, and so forth. The cyber-physical system needs to guarantee that only the right

person accesses the right data at the right time.

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