© Fraunhofer IFF

AUTOMATION IN PRODUCTION – HUMAN ROBOT COLLABORATION

Sebastian Häberer

1

Kameo Hotel Amata Bangpakong, March 2019

© Fraunhofer IFF

Employment

Researcher, Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg

Bachelor’s candidate, Corporate Industrial Ergonomics, Volkswagen AG, Wolfsburg

JOIN intern, Research & Development, DHL Solutions & Innovation, Troisdorf

Student assistant and intern, Fraunhofer Institute for Factory Operation and Automation IFF, Magdeburg

Education

Training and Certification in MTM-Practitoner

Bachelor of Science (B.Sc.) and Master of Science (M.Sc.) in Logistics Engineering Management, Otto von Guericke University Magdeburg, with specializations in:

Logistics planning and virtual reality

Supply chain management (SCM) and networks

Résumé

Industry expertise

Automotive (supplier)

Logistics

Service

Professional expertise

Logistics planning and organization

Process planning and optimization, restructuring

Demand and capability analyses

Project management and steering

Key projects

Development of a standard that assesses physical stress of untimed work

Supervision and refinement of a delivery concept based on the crowd principle

Development of an integrated approach to implement hybrid assembly systems

SME 4.0 competence center Magdeburg

Expertise

Sebastian Häberer

Current Position

Expert Engineer,Logistics and Factory Systems Business Unit

Fraunhofer Institute for Factory Operation and Automation IFF Magdeburg

Person

5

© Fraunhofer IFF

Industrie 4.0-CheckUp - Training of Trainer ProgramRoadmap for the implementation of Industrie 4.0

7

https://www.youtube.com/watch?v=3rG9-gUhcJA&list=PLIIjFDzTdgkPBOAtujEqZ4jSS8QFjem5l

© Fraunhofer IFF

Industrie 4.0-CheckUp - Training of Trainer ProgramIndustrie 4.0-CheckUp – References among the World

8

*VDI Technical Committee 7.27 - STEPS 4.0 Systematic transformation and evaluation of production systems

*

© Fraunhofer IFF

Industrie 4.0-CheckUp - Training of Trainer ProgramModule Milestones - FTPI

9

Module 1

Pilot CheckUp

Module 3

CheckUps with local consultants

Module 2

Train the method of Industrie 4.0

Completion of one pilot Industrie4.0-CheckUp project by expert from the Fraunhofer IFF

Future CheckUp staff by FTPI will observe this CheckUp and all steps within the process

Adaption of the Industrie 4.0-CheckUp methodology to the FTPI business environment

Development of a curriculum for training and completion of a training course for consultants in Thailand

Module 4

Awareness Workshops for Industrie 4.0

One Industrie 4.0 CheckUp will be implemented by consultants in cooperation with Fraunhoferexpert

Supervised by Fraunhofer IFF and results verified by Fraunhofer IFF staff

New digital business models - Develop business models systematically – 15.02.2019

Identification technologies as an enabler for Industrie 4.0 – 18.02.2019

Automation in Production – Human Robot Collaboration – 28.03.2019

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationFocus points

State of the art - Human-Robot Collaboration

Normative requirements for safe interaction without a safety fence

Method for decision support for the use of HRC scenarios

Examples and first experiences of human-robot cooperations in assembly

10

©fotomek - stock.adobe.com

© Fraunhofer IFF

Fraunhofer Institute for Factory Operation and Automation IFF

Located in Magdeburg, Germany

200 Researchers

€ 20 Mio Research budget p.a.

International experience on six continents

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The Fraunhofer Institute for Factory Operation and Automation IFFWhere do we come from

© Fraunhofer IFF © Fraunhofer IFF

© Fraunhofer IFF

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The Fraunhofer Institute for Factory Operation and Automation IFFProviding a system perspective on the factory

Fields of Research

Resource Efficient Production and Logistics

Smart WorkSystems

ConvergentSupplyInfra-structures

Logistics and Factory Systems

Material Handling Engineering and

Systems

Measurement and Testing

Technologies

Robotic Systems

Virtual Engineering

BiosystemsEngineering

Convergent Infrastructures

Fraunhofer IFF Business Units

© Fraunhofer IFF

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The Fraunhofer Institute for Factory Operation and Automation IFF Using Digital Technologies as Enablers of Industrie 4.0

Source: acatech 2013, http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf

Automation Digitalization of Systems

Digitalization of Processes

Digitalization of Infrastructure

Digital Twin concepts for cyber-physical systems

Data-based assistance systems to support workers in a networked

production environment

Visualization of infrastructures to support operations and

decision-making

Digitalization

Mechanization

Electrification

© Fraunhofer IFF

Objective

Visualization of assembly instructions

intuitive presentation

locally und temporally correct

The right part at the right time

Check through the worker: “Worker Self-Assesment”

Basis: Geometric CAD-Data

Application

Pre-assembly of components

Wiring of components

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The Fraunhofer Institute for Factory Operation and Automation IFF Assistance for Control Cabinet Assembly

Current Situation

Target Situation

© Fraunhofer IFF

Objective: Making spectroscopy available for wider applications

HawkSpex Mobile App uses adjustable illumination of mobile display and front camera to record spectral image

Using purpose built machine learning models to analyze properties

Application area:

Agriculture and food processing

Cosmetics and fashion retail

Quality control and product authentication

New value adding business models

The Fraunhofer Institute for Factory Operation and Automation IFFUsing smartphones as spectral sensor

22

© Fraunhofer IFF

VDTC officially recognized as a European Digital Innovation Hub providing companies with cutting edge support towards Industrie 4.0

Supporting international networks to increase access to knowledge

DIH as one-stop-shops for companies, especially SME, to improve their competitiveness through digitalization

VDTC as a central actor in a network of regional stakeholders to promote and support digitalization in Saxony-Anhalt and beyond

23

The Fraunhofer Institute for Factory Operation and Automation IFFEuropean level coordination of SME support - Digital Innovation Hubs and Competence Center for SME

Digital

Innovation

Hubs

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationDevelopment of markets

24

Pro

du

ctvo

lum

ep

er

va

ria

nt

Product variety

1850

1913

1955

1980

2000

e.g.. 3D-Print

e.g. BMW online carconfigurator

„People can have the Model T in any colour - as long asit´s black“

Henry Ford (1913)

e.g. VW beetle

Source: According to Yoram Koren: The Global Manufacturing Revolution; Source: Ford, beetleworld.net, bmw.de, dw.de

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationVolatility increases flexibility requirements

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Production CustomerSupplier

Internationalization of procurement markets

Increasing product complexity

Short-term orders

Increasing number of variants and customized products

Extremely short delivery

times

Order decline or order increase

Drastically shortened product

life cycles

Increasing intensity of competition

Small lot sizes

shortage ofresources

Unsafe replenishment

time

Source: www.mlive.com Source: en.wikipedia.org

© IFA Rotorion© BMW

© Bimmertoday

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationRequirements of future-oriented production solutions

Connection and integration Digital value chain

Changeability Dynamic process optimization

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Source: Bildquellen: acatech; MuM; Bilfinger; M&R

© Fraunhofer IFF

Multiple entry of data

Coordination problems

Information deficits defies information

Overload of information

Incorrect information

IT solutions

Information losses during shift transfer

Manual data input

High time expenditure

Deficits in the flow of information

Automation in Production – Human Robot CollaborationIncrease productivity potential through digitization

29

operating objectives

Long durations

Delivery problems (scheduling difficulties)

High waiting and lay times

High stocks

Little flexibility

Late troubleshooting

…

Deficits in the material flow

Disturbances in the material flow can often be explained by poor information flow

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationPotential Savings

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Costs Impact Savings

Inventory costs Reduction of reserve inventoryElimination of bullwhip effect (Burbidge and Forrester effect)

30 to 40%

Manufacturing costs Improvement of OEEProcess control loops Improvement of vertical and horizontal staff flexibility

10 to 20%

Logistics costs Increased level of automation (milk run, picking, etc.) 10 to 20%

Complexity costs Expanded performance marginsReduction of trouble shooting

60 to 70%

Quality costs Near real-time quality control loops 10 to 20%

Maintenance costs Optimization of spare part inventoriesCondition-based maintenance (process and measured data)Dynamic prioritization

20 to 30%

Source: Bauernhansl, Thomas: Die Vierte Industrielle Revolution – Der Weg in ein wertschaffendes Produktionsparadigma. 2014

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationThe biggest opportunities of Industrie 4.0

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Production optimization New business models Expansion of product and service portfolio

Improved customer service Sales increase

Source: MittelstandDigital 2017, BMWI

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationData flow in the production process

Data fusion Data analysis

Model Simulation

Bandwidth

Monitoring

Trends,

Forecast

Decision support

Data entry

Sensors Digital Factory

Data storage

Data protection Amount of data

Preprocessing

real-time Know-how

Visualization

Products, Processes, Facilities

OptimisationRegulation

Interfaces

Automatization

Standards

Key issues / technologies

Networking topics

Existing solutions

Development needs

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© Fraunhofer IFF

Automation in Production – Human Robot CollaborationThe biggest challenges on the way to the digital industry

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Skilled workers qualification Missing standards

Data security Investment costs

Source: MittelstandDigital 2017, BMWI

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationMatch the objectives with topics of Industrie 4.0

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Development of new products and

services

Grow profitably with new business

models

Customer acquisition and customer loyalty

Cost savings and efficiency

Making production more flexible, faster,

and more individualized

Employees acquisition, qualify

and hold

Organize knowledge as resources

Security in the digitization of

business processes

RFIDIT-Security

data analysis

ERP-SystemsWork 4.0

intelligent

Sensor Networks

CRM Virtual Reality

knowledge managementBig Data

Assistance systems

Smart Grid

Cloud5G

Industrie 4.0

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationEvery company needs to find an individual way!

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© Fraunhofer IFF

Automation in Production – Human Robot CollaborationForecast of different Industrie 4.0 Road Maps

Laboratory solutions

Primary Showcases or laboratory solutions in

development

Isolated / selected pilot applications

In the coming years, mainly technology-

driven isolated / plug-in solutions will

be developed

Ready adoption of standard solutions

The market penetration of

isolated solutions merge to

combinations of many solutions

through existing channels

Over the life cycle of the production machines, Industrie 4.0 will take

place holistically, provided the machines,

infrastructure and employees are able to do

industrie 4.0

Transition to true Industrie 4.0

New factoriesGreenfield

Existing factories

Brownfield

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Source: Zollenkop/Lässig (2017), Pg. 67

© Fraunhofer IFF

Automation in Production – Human Robot CollaborationPromoting a holistic perspective on Industrie 4.0 - Good-practice recommendations

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Industrie 4.0 is not only about modernizing equipment

Improved digital data collection

Better exploitation of data

Fusing different data sources

Modern equipment produces data and needs data

Companies need to take account of this new paradigm

Process ownership and employee competences for decentralized decision-making

Employee digital skills and appropriate assistance systems

Innovation-, personnel-, and change management aspects

Digital business model adaptation© Neugebauer, Reimund; Hippmann, Sophie; Leis, Miriam; Landherr, Martin (2016): Industrie 4.0 - From the perspective ofapplied research. 49th CIRP Conference on Manufacturing Systems (CIRP-CMS 2016). Available online at www.sciencedirect.com

© Fraunhofer IFF

Traditional

Strategic

Approaches

Automation in Production – Human Robot CollaborationDigital innovation - Fundamental changes in the strategic context

vs.

Test

&

Learn

“Move fast and break

things”

Uncertain environ-ments

No respect for

incumbency

Speed

Agility

Always in prototype

stage

Constant user

feedback

Win the customer

everyday a new

Centra-lization

Hierarchical Decision-making

Long-term planning

cycles

Incumbancyas market

barrier

Focus on legacy assets

Strict focus on ROI

Top-down

Depart-mental

silos

39

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Capability Maturity Model

What stage of Industrie 4.0 has your company reached?

STA

GE

S O

F IN

TE

GR

AT

ION

Ind

ust

rie

4.0

FACTORY OF THE FUTURE INDUSTRIAL ECOSYSTEMS

… Dark Factory

Smart DataAssistance Systems Human-Robot Collaboration…

Big Data

Standards

Networked Manufacturing

Integrated Acquisition ofProduction and Logistics Data…

Partial Automation and Local SolutionsExecution of Individual Actions…

…

…

… …

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«6 principles of Industrie 4.0

Summarized integration

levelData collection and processing

Assistance systems

Networking and integration

Decentralization and service orientation

Self-organization

and autonomy

Qualification of employees

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Principles of Industrie 4.0

6 Principles of Industrie 4.0

Assistance systems

Networking and Integration

Decentralization and Service orientation

Self-organization and autonomy

Data collection and processing

Qualification of Employees

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»Industrie 4.0-CheckUp«A standardized approach for customized results

Pro

cure

men

t

Pro

du

ctio

n /

Pla

nt

con

tro

l

Ma

inte

na

nce

Mark

eti

ng

/ P

resa

le /

Sellin

g

Qu

ality

/ L

ab

Ord

er

/ D

isp

atc

h C

en

ter

Assistance systems

Networking and Integration

Decentralization and Service orientation

Self-organization and autonomy

Data collection and processing

Paym

en

t

Company departments (generic example)

IT S

erv

ice /

Data

In

teg

rati

on

Aft

er

Sale

s /

Cu

sto

mer

Serv

ice

Hu

man

Reso

urc

e

Company departments (generic example)

What is the current profile of employee qualifications? Which requirements currently exist and will emerge in the future? How far is there an understanding of the Industrie 4.0 concept among employees?

How is data collection organized in the company? Is there a coherent collection and processing of digital data? How is data used in the company processes? Are there media breaks or other bottlenecks?

How are employees supported through cognitive or physical assistance systems? How is information provided to employees and other stakeholders?

In how far are machines and production resources, departments, IT systems, data and information networked and integrated? Is there an value chain oriented integration of systems (customers, suppliers, service providers)?

How are centralization/decentralization and service orientation organized in terms of organizational entities, machines/equipment, employees and network partners?

In how far are control loops, data and information management, machines and equipment autonomous or self-organizing? Is there automated decision making based on collected and processed data?

Qualification of Employees

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Methodology-mix to get a holistic company picture

Analysis phase geared towards getting the full picture of the company

Using a diverse tool-box to adapt to company requirements and company particularities

Guided by a set of more than 200 questions and indicators but not following a strict questionnaire approach – assessment based on the experience of Fraunhofer staff

Highly adaptable methodology –implement-ted in manufacturing-, engineering-, pharma-ceutical-, food processing-, textile- and mining companies

Cost

benefit

analysis

Strategy

Roadmapping

Scenario

techno-

logies

Value

stream

design

SWOT

analysis

Growth

share

matrix

Work-

shopsTechnology

Scouting

Interviews

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Maturity assessment and definition of starting position – Example

Industry 4.0 - Stage model - Barometer

1,1

1

2

3

4

5

Ind

ust

ry 4

.0

Standardized production Landscape

Transparent Factory (Big Data)

Transparent System (Smart Factory)

Fully automated Factory (Dark Factory)

Industrial Ecosystems

Integration level - Total Overview

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Action development – Where to start? What does it take?

Analyzation of Challenges and Problems from interview phase

Maturity index of different departments and ID4 principles

Knowledge of Technology and Tools

Creativity & Teamwork

Experience

© Fraunhofer IFF

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»Industrie 4.0-CheckUp« Use AR technologies as assistance system for technical - Example

Quality of service ↗Installation time ↘Worker motivation ↗After Sales

Responsible

IT

Involved

Project proposal

Provide 3D tile models to installation workers as part of an AR applicationdesigned for smartphones and AR hardware like smart glasses at the building site

Process instructions are displayed to the worker while doing his job, including more detailed instructions and hints for complex tasks like patterns or room corners

Raised qualification of technical service employees

Own knowledge management features

Faster and better training of rookie employees

Benefits

Digital product models are needed

Investment costs for software and hardware

Setting up system and including external workers takes a lot effort

Challenges

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Roadmapping the digital evolution - Timing of measures to take account of interdependencies – Example

Workflow management

Predictive analytics - sales

Dynamic and continuous supplier evaluation/analysis

Qualification and

digital integration of suppliers

Tracking and tracing

Maintenance: electronic service

applications

Maintenance: data analytics and

prognostics

Maintenance: data acquisition

Smart maintenance strategy

Retrofitting

Mobile assistance systems for decentralized documentation

Reliable production scheduling

Complete inventory control and automatic

order release

Data for manufacturing assistance

Electronic Batch Record Review

Stabilized processes with SPC

Dynamic capacity scheduling tool

Predictive maintenance

Smart Procurement

Work order tracking and

progress visualization

Implement frozen time

Handling Truck traffic

Video management

Serialization

LIMS

MES-SystemCross-Checking

Microsoft Power BI

Automated material movement

Cause effect analysis

Modified“HeilbronnerHalbpalette”

Cold Supply Chain

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«Depicting a company‘s capability to absorb technology

Single actions Semi-automation and local solutions

Integrated acquisition of production, quality and logistics data

Documentation, basis for analysis, digital modelling, low cost automation

Linked product and process dataDigital factories, new HMI, assistance systems,

human-robot collaboration

Interconnected product and process structure, and

infrastructure

Highly automated subsystems, self-learning control algorithms

Autonomous supply chain

Networked automated production and logistics systems

Transparent factory(big data)

Smart factory(smart data)

Dark factory(fully automated)

Industrial ecosystems

1

2

4

3

5

Local efficiency

The maturity index helps classifying a company’s technological and process capabilities

Premise to define relevant measures to increase digitalization

Provides a perspective on the existing tech-nology and process landscape to build on

Provides a perspective which technological steps are necessary and sensible

Provides a perspective on how to structure processes and organization to accommodate technical digitalization efforts

Provides a perspective on the company’s absorptive capability for specific technologies

A higher level on the maturity index shows

An increased use of digital data, i.e. collection and processing

An increased decentralization and automation of decision making

An increased digital integration of the value chain

We do not give school marks

1 is not bad and 5 is not good

The levels depict the company’s technolo-gicalcurrent state

© Fraunhofer IFF

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»Industrie 4.0-CheckUp«What is the CheckUp for? What should the CheckUp do?

What the Industrie 4.0-CheckUp does…

Holistic view of the drivers in the company cross-section

Company classification with regard to the degree of maturity regarding Industry 4.0

Catalog of measures for concrete application

Decision support for possible investment projects

Cost-benefit assessment (qualitative)

What the Industrie 4.0-CheckUp does not deliver …

Material flow analysis and reorganization of production processes

Material and shopping lists show the investments

Cost-benefit analyzes (quantitative)

Reviews of IT architectures

© Fraunhofer IFF

Automation in Production – Human Robot Collaboration Basics of assembly - Functions of assembly

82

TASK CLASSES OF THE ASSEMBLY

JoiningDIN 8593

Assembly

Filling

Pressing

Joining through primary shaping

Joining throughshaping

Joining throughwelding

Joining throughsoldering

Glueing

HandlingVDI 2860

Store Changing quantity

Moving

Fixing

Checking

CheckingVDI 2860

Checking

Measure

AdjustingDIN 8580

Adjustment byforming in

Adjustment byshaping

Adjustment byseparating

Adjustment by joining compensating parts

Adjustment bysetting

Special Operations

Select

Heating

Cooling

Cleaning

Deburring

Printing

Covering

pulling off

Unpacking

Oiling

Spraying

© Lotter, 2012

Source: Lotter 2012

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Work order

Environmental influences

Wo

rk

ass

ign

me

nt Working person(s)

ToolsAppliances, Tools

Work objectsWorking materials

Input Output

Work result

Quantity

Quality

Material

Information

Energy

Social / Emotional/ Organizational / Communicative

Physical / Organismic

Chemical / Material

backlash effect

effectbacklash

Target Purpose setting

Automation in Production – Human Robot Collaboration Basics of assembly - Representation of a working system

Source: Schlick 2010

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Rationalisation aspects of assembly

Constructive

Standardization, series development

Product design, modular constructionmethod

Individual part design

Tolerances

Quality characteristics

Joining method

Connecting elements

Technical

Joining method, connecting elements

Mechanisation

Automation

Buffer layout

Organizational

Buffer layout

Production program

Organizational form

Performance tuning of the stations

Jumper use

Batch determination

Retooling sequences

Conversion organisation

Division of labour

Working science

Division of labour

Work content

Personnel qualification

Working Methods Design

Workplace design

Work environment design

De

sig

n f

ield

s

Source: Lotter, 2012

Automation in Production – Human Robot Collaboration Basics of assembly - Rationalisation approaches to assembly

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Organizational forms of production according to the process principles

workbenchproduction

workshopproduction

manufacturing according to

the flow principle

starproduction

productionisland

constructionsite

production

Source: REFA 1993

Automation in Production – Human Robot Collaboration Basics of assembly - Organizational forms of production according to the process principles

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Variant diversity

Manual assembly

Automatic assembly

Hybrid assembly

high

high

low

low Quantity

Fle

xib

ilit

y

Pro

du

ctiv

ity

Automation in Production – Human Robot Collaboration Basics of assembly - Application area of hybrid mounting systems

Source: Lotter, 2012

© Fraunhofer IFF

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+ intelligent sensortechnology

+ cognitive abilities

+ flexibility

+ fine motor skills

+ creativity

+ learning ability

+ high repeat accuracy

+ perseverance

+ precise process forces

+ media resistance

+ high speed

+ combination of high performance and cognitive skills

+ work facilitating

+ increasing quality

Human RobotsHuman & Robot

Interaction

Safety

Protect people from the machine

Security

Protect the machine from humans

Source: Häberer 2016

Automation in Production – Human Robot Collaboration Basics of assembly - Strengths of humans and robots

© Fraunhofer IFF

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1/4 all missed days due to musculoskeletaldisorders

productiondowntime costs

Sicherheit und Gesundheit bei der Arbeit, baua 2016

Any

Worker in mechanicalengineering is between45 and 55 years old.

of industrialenterprises do not find technical specialists

DIHK-Arbeitsmarktreport 2018

Assembly at BMW up to

manuallyStatistisches Handbuch für den Maschinenbau, Ausgabe 2012, VDMA Volkswirtschaft und Statistik.

Statistisches Handbuch für den Maschinenbau, Ausgabe 2012, VDMA Volkswirtschaft und Statistik.

Generation 50+ share of employed persons

today

2025

Lt. Angaben von BMW

Automation in Production – Human Robot Collaboration Basics of assembly - Motivation for human-robot collaboration

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92

characteristic telemanipulator balancer exoskeletonhuman-robot

collaboration

human hybrid

robot

working

performanceHuman controls machine Human handles machine Power assistance by machine

Cooperation between

humans and robots

Integration of human and

machine

system

compositiondisassociated disassociated hybrid combined hybrid

design process-dependent process-dependent user-dependent process-dependent user- and process-dependent

flexibilityProduct variance limited by

tools and kinematics

Product variance limited by

tools

Product variance limited by

tools; limited to persons

Product variance limited to

tools; user-independent

adaptable to product and

operator through modular

architecture

quantity low low-medium low low-medium low-medium

supportHazard avoidance; increasing

accessibility

Guide- and carrying

support

Support for strength,

endurance and mobilitywork acceptance

see exoskeleton + increase of

accuracy, quality assurance,

error avoidance, etc.

system

constructionspecial production

Special design for a specific

applicationspecial production

Special design for a specific

or flexible applicationModular construction kit

Automation in Production – Human Robot Collaboration Basics of assembly - Characteristics of known hybrid assembly systems

Source: © Weidner und Wulfsberg 2013

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Human-robot collaboration describes the exchange of information and actions between humans and robots that are necessary for the

execution of a task.

Source: DIN EN ISO 8371

Automation in Production – Human Robot Collaboration Basics of assembly - Definitions

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95

manuallyguided

manipulators

variantshuman-robot collaboration

driverlessfloor

conveyors

Autonomousmobile robots

Assistance robots and

cobots

tele-operators

servicerobots

humanoidRobots

microrobotics

Robots asintelligent

tools

Highlyflexible robots

Space-minimized

robots

Autonomoustransport

robots

Source: Hesse 2010

Automation in Production – Human Robot Collaboration Basics of assembly - Variants of assistance robots

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97

data interfaces

control

energy supply

end effector

mechanical structure

sensors

Automation in Production – Human Robot CollaborationState of the art - HRC - Variants of assistance robots

Source: KuKa 2016, Warnecke 1990

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Impact with possibility of evasion

Impact with limited possibility of evasion

Impact without possibility of evasion

Jamming in the robot structure

Types of unwanted contact between Humans and Robots

Automation in Production – Human Robot CollaborationState of the art - HRC - Types of unwanted contact between humans and robots

Source: Khatib 2008

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101

Protective measures of the designer Protective measures of the user

Protective measures for industrial robots

Inherently safe design

Technical and complementary protective measures

User information on the machine and in the user manual

Safe working practices, supervision, type-approval for carrying out work

Provision and use of additional protective devices

Use of personal protective equipment

Education and Training

Source: DIN EN ISO 12100

Automation in Production – Human Robot CollaborationState of the art - HRC - Protective measures for industrial robots

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108

Source: Häberer 2018

Automation in Production – Human Robot CollaborationState of the art - HRC - Step Model

Coexistence

Synchronized

Cooperation

Level of Interaction

Req

uir

em

en

tsto

Safe

tyC

on

cep

t

Collaboration

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Interlocking devices

ISO 14119

Sensitive protective devices

Application : IEC/TS 62046

Mats and lasts : ISO 13856-1

Lasts : ISO 13856-2

Puffer: ISO 13856-3

General design approaches : Risk assessment ISO 12100safety measures :

Separating protective devices

ISO 14120

Safety distances : ISO 13857

Minimum distances : ISO 13854

Arrangement of protective devices : ISO 13855

Not- Separating protective devices – Safety-related parts of control systems

ISO13849-1 and ISO 13849-2

IEC 61496-1

Light curtains, light grids: ISO 13856-1

Laser, scanner: ISO 13856-2

Security- cameras: ISO 13856-3

Two-hand circuits

ISO 13851

Approval devices

ISO 10218-1

Industrial robot systems - ISO 10218-2

A-norms

B-norms

C-normsleg

en

dAutomation in Production – Human Robot CollaborationState of the art - HRC - Relevant standards for the safety of industrial robots

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Technical application variants of human-robot collaboration

Safety evaluatedmonitored stop

Hand guidanceSpeed and distance

monitoringPower and force

limitation

s ≥ smin

v = 0F ≤ Fmax

p ≤ pmax

s ≥ smin

v ≠ const

Source: Umbreit 2013

Automation in Production – Human Robot CollaborationState of the art - HRC - Technical application variants of human-robot collaboration

© Fraunhofer IFF

Source: Universal Robots

tare weight 29 kg

range 1300 mm

payload 10 kg

number of axes 6

installation option floor, wall, ceiling

Special features

Low price

Hand leading / Teaching by demonstration

Protection fenceless operation (static contact force < 150 N according to DIN EN ISO 10218:2016)

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - UR10

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Source: Bosch-APAS

tare weight 230 kg (wagon)

range 911 mm

payload 2 kg

number of axes 6

installation option Floor (mobile)

Special features

Integrated camera

Non-contact safety sensor (50 mm, but not on the gripper)

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - Bosch APAS

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Source: ABB

tare weight 38 kg ( control unit)

range 559 mm

payload 0,5 kg

number of axes 7

installation option table

Special features

Simple operation

Measurement of motor currents

Control and functions in PL = b

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - ABB-Yumi

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© Fraunhofer IFF

Source: Fanuc

tare weight 750 kg (with foot)

range 1813 mm

payload 35 kg

number of axes 6

installation option floor

Special features

Force-torque sensor in the foot present

Measurement and monitoring of motor currents

Clamping points are covered; structure covered with foam

plastic

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - FANUC CR-35i

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Source: KUKA Roboter GmbH

tare weight 22,3 kg

range 800 mm

payload 7 kg

number of axes 7

installation option floor, wall, ceiling

Special features

Intelligent - Industrial - Work - Assistant

Integrated stiffness controllers

High price

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - Kuka LBR iiwa 7 R800

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Source: MRK-Systeme

tare weight 127 kg

range 1423 mm

payload 5 kg

number of axes 6

installation option floor, ceiling

Special features

Low payload range with high range

Certified protection system possible without additional protective device

Automation in Production – Human Robot CollaborationState of the art - HRC - Product portfolio - KR 5 SI (MRK systems)

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Module for evaluating economic efficiency

Module for identification of automation potentials

Module for creating concepts

Potential identified?End

Technical feasibility?End

Start of the method

Module for creating a decision template

End of the method

Yes

Yes

No

No

Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Overview

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Economic efficiency

Quality Productivity

Ergonomics

Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Goal criteria

Source: Schmauder 2015

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Increasing process quality to reduce the time required

for reworking

Quality

Minimizing secondary activities

to increase the share of value-adding

activities

Productivity

Ergonomic workplace design to reduce days lost due

to illness

Ergonomics

Increasing efficiency and optimising the use of resources to

improve profitability

Economic efficiency

Intended changes to the target criteria

Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Intended changes to the target criteria

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Classification of the automation potential index

quality

ergonomics

productivity

Part of total valuation [%]

Evaluation [Points]

economic efficiency

xx

< 0,2

< 0,40

≥ 0,40 Measures are urgentlyneeded

Measures to bereviewed

Measures are not required

-

-

-

Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Classification of the automation potential index according to the traffic light system

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Classification of ratings

xx xx xx xx

Workplace 1 Workplace 2 Workplace 3 Workplace 4

0,50

0,38

0,54

0,26

Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Classification of ratings

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Method for decision support of HRC //Parameters of the assembly environment

No. Parameter Description

1 length of the assembly area

2 width of the assembly area

3 height of the assembly area

4 available media connections

5 average temperature range

6 type of assembly area

7 spatial flexibility for integration

8 usable and existing storage for a robot

9 traffic situation

10 other requirements

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position of the assembly system

coupling concepts

storage of the robot

material supply

robot

end-effector

media supply

sensors

detection safety measures

controltechnical application

variant

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Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Components of a human-robot collaboration

© Fraunhofer IFF

Material supply by employeesdefined position

infeedpaternoster

hose-

feed

magazine

preparationby robot

Safety measures tactile skinshift

mats

enabling

switchlight grids safety light

color

markingsintrinsic safety

Technical application

variant

safety assessed

monitored stop

hand

guidance

speed and distance

monitoringpower and force limitation

Coupling conceptsstationary -

outside

stationary -

insidelinear guidance mobile unit autonomous mobile unit

RobotUniversal Robots UR

10KUKA LBR IIWA 14 R820 SCHUNK-WA 4 P FANUC - CR 35-IA etc.

Position of the assembly

systemcurrent location new location flexible location

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Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Morphological approach to conception

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Investment costs - evaluation of the amount of the cost of acquisition

Operating costs - evaluation of the costs of day-to-day operation

Quality - evaluation of the quality of the work performed

Speed - evaluation of the speed for the cleaning process

Ergonomics - Evaluation of ergonomics for the worker

Maintenance friendly - evaluation of maintenance for the new system

Flexibility - evaluation of the flexible application possibilities

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Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Explanations of terms for evaluation criteria

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Possible emergency strategy - Evaluation of a possible emergency strategy in case of malfunctions

Start-up capability - Evaluation of the start-up capability after malfunctions, pauses, etc.

Teachability - evaluation of the instruction expenditure for new employees

Availability - Evaluation of the technical availability of the system

Area Requirement - Evaluation of the required area for the working system

Implementation period - evaluation of the period of implementation and realization

Process monitoring - evaluation of the applied process monitoring

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Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Explanations of terms for evaluation criteria

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evaluation scale

0 - not fulfilled

1 - insufficient

2 - satisfactory

3 - good

4 - very good

please note:

high costs are to be evaluated with a low score

high quality is to be evaluated with high acceptance points score

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Automation in Production – Human Robot CollaborationMethod for decision support of HRC - Explanations of terms for evaluation criteria

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Automation in Production – Human Robot CollaborationExamples - What can we learn from them? - Enrichment of coolant expansion tanks - AUDI

Source: Audi

© Fraunhofer IFF

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Automation in Production – Human Robot CollaborationExamples - What can we learn from them? - Door soundproofing - BMW

Source: BMW

© Fraunhofer IFF

© Atria Scandinvia

© Assa Abloy Romania

© Gern Glas,© BSH Haushaltsgeräte

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Automation in Production – Human Robot CollaborationExamples - What can we learn from them? - HRC also increasingly in small companies

© Fraunhofer IFF

Supermarket (Logistics)TS

M

TEAM ROOM

W2

W1 W3 W4 QCL

PO

Control worker

CW

CW

Information technology support

CW

W QCL PO - Master- WorkerQuality control loop

Plant operator M TS

Team speaker

CW ----

Automation in Production – Human Robot CollaborationSummary and Conclusion - Impacts

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Automation in Production – Human Robot Collaboration Focus points

Modern production will increasingly combine the strengths of people with the advantages of efficient technologies

The safety concept is an essential component of an HRC application and must be considered from the 1st planning step onwards

The early involvement of all employee groups has a sensitising and acceptance-promoting effect and ensures that all technical requirements are taken into account

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The Fraunhofer Institute for Factory Operation and Automation IFF Let us research your application together!

Fraunhofer Institute for Factory Operation and Automation IFF

Sandtorstraße 2239106 Magdeburg

www.iff.fraunhofer.de

© Fraunhofer IFF

Sebastian HäbererM.Sc.

Business UnitLogistics and Factory Systems

Telephone +49 391 4090-621sebastian.haeberer@iff.fraunhofer.de

© Fraunhofer IFF

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