intelligent adaptive services for workplace-integrated learning on the shop floor

Intelligent Adaptive Services for

Workplace-Integrated Learning on

the Shop Floor

Carsten Ullrich

Associate Head

Educational Technology Lab (EdTec) at the

German Research Center for Artificial Intelligence (DFKI GmbH)

The Workplace is

Transforming

• Challenges for Europe's manufacturing industry:

– Accelerating innovation

– Shorter product cycles

– Ever increasing number of product variants

– Smaller batch sizes (batch size 1)

– … while keeping/increasing level of competitiveness

– … with fewer and fewer employees

23.05.2016Carsten Ullrich, Tempus Workshop

Towards Industry 4.0

tEnd of

18th Century

Start of

20th Century

First

Mechanical

Loom1784

1. Industrial Revolutionthrough introduction of

mechanical production

facilities powered by

water and steam

2. Industrial Revolutionthrough introduction of mass

production based on the division

of labor powered by

electrical energy

Start of

70ies

4. Industrial Revolutionbased on Cyber-Physical

Production Systems

today

010001101001010100100101010010010101

Industry 1.0

Industry 2.0

Industry 3.0

Industry 4.0

De

gre

e o

f C

om

ple

xit

y

3. Industrial Revolution electronics and IT and heavy-

duty industrial robots for a

further automation

of production

Wahlster, 201223.05.2016Carsten Ullrich, Tempus Workshop

Cyber-Physical

(Production) Systems

• Cyber-physical system– physical entity

– and its virtual representation

• Cyber-physical production system– classic production technology

– virtual representations of all its parts: product, machines, operator

• Not just physical interactions, but also software– Machine and product communicate with each other

– Decentralized: factories optimize and control manufacturing processes themselves

– The smart product, the smart machine and the augmented operator


Industry 4.0 / Smart

Manufacturing

• Transformation of Workplace is a reality, all

buzzwords aside

– Digitalization

– Internet of Things

• Seen as a change to transform the organization

of work


Human Operators at

Tomorrow’s Workplace

• Despite the increasing automation, human operators have place on shop floor with changed roles

• Technological innovation cannot be considered in isolation, but requires an integrated approach drawing from technical, organizational and human aspects.

• CPS and other new technologies increase complexity of– usage and maintenance of production lines

– control of the production process

Mastering this complexity and flexibility requires

• larger amounts of knowledge and deeper job expertise than ever before

• other forms of organizing work: teams that take responsibilities, operate independently

23.05.2016Carsten Ullrich, Tempus Workshop (Hirsch-Kreinsen, 2014)

Assistance- and Knowledge-Services

for Smart Production

• Information providing and training processes will become – more flexible

– integrated in the workplace

– individualized

• CPPS give access to the shop floor and its data

• Opportunity to build tools that– adapt themselves intelligently to the knowledge level and tasks of the human

operators

– integrate and connect the knowledge sources available in the company

– generate useful recommendations of actions

– enable recording of work processes and applied knowledge

– support the migration towards smart manufacturing

ADAPTION


APPsist Consortium

Ap

pli

ca

tio

n&

Va

lid

ati

on

Re

se

arc

h &

De

ve

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me

nt

Co

ns

ult

ing

*Subcontracts

*

Duration 1.1.2014-31.12.2016


Partly automated assembly

line

Support for maintenance

5-axis drill

Support for machine usage

Pilot Scenarios

Partner

Pilot Area

Pilot Scenario

Production line

Support for failure detection


3 manual assembly

stations

Main host computerMonitoring and analysis

SPSControlling the machines

Coarse control and

monitoring granularity

System detects status and

faults

Classification on level of

stations, not components

Activities

Preventive maintenance

Resolving disabled states

and faults

Manual assembly

Goal

Increasing the competence

level of target audience

Increase worker’s

understanding of process,

product, manufacturing

Automated processes

Machine user

Machine operator

(plus)

Machine operator

Co

mp

ete

nce

Pilot Study: Festo


Pilot study Festo: Refill Loctite


Aim: Assistance and

Knowledge Acquisition

• Support employee:

– Assistance: Depending on the context

• Reacting to the current situation on the shop floor, e.g.,

Loctite is empty

• Aim: Fullfill KPIs

– Learning: Depending on the employee

• Long-term development goals (e.g., working towards a new

job position)

• Aim: Learning


APPsist Architecture Overview

Learn

ing m

ate

rials

Conte

nt

Machine data

User data

Process data

APPsist

HUMAN-

MACHINE-

INTERACTION

HUMAN-

MACHINE-

INTERACTION

Assistance-

services

Knowledge-

acquisition-

services


Modelling the Maintenance Process

• Process models represent a complete and applicable description of steps required to perform a task

• Process models are formally defined (BPMN) and therefore– have a defined meaning

– can be executed by process engines

• Used as a basis for the intelligent assistance

Loctite empty

Get

required

items

Stop

station

Replace

materials

Start

station

Disposal


Overview: A few of the

APPsist Services

• Content-Delivery-Service (IAD)

• Content-Interaction-Service (IID)

• Machine-Information-Service (MID)

• User-Modell-Service (BMD)

• User-Context-Service (BKD)

• Performance-Support-Service (PSD)

• Process-Coordination-Service (PKI)

• Content-Selector (IhS)

• Measure-Selector (MD)

• …


Service Description

• Performance-Support-Service (PSD)– Guides the users through the assistance process.

• Process-Coordination-Service (PKI)– Instantiates and administers processes, reacting to incoming events and

coordinates other services relevant for current process.

• Content-Selector (IhS)– Retrieves content adapted to individual user and context based on rules

– Uses semantic knowledge repository for reasoning.

• Measure-Selector (MD)– Determines applicable assistance processes according to user and machine

state based on rules.

– Uses semantic knowledge repository for reasoning.


APPsist Ontology

• Describes relevant concepts for and their relationships

• User

• Content

• Manufacturing

• Representation in OWL (Semantic Web standard)

• Used for communication between services and for reasoning by intelligent services


User Model

• Connection to domain-model concepts• Concepts from domain-model are enriched with user specific

values– Number of executions (for process-steps)

– Number of views (for contents/documents)

– Number of usages (manufacturing/production objects)

• Relevant user properties• Workplace-groups

• Permissions

• „State“: main activity (KPI), secondary activities

• Development goals

• Mastered measures


Examples of Adaptivity for

Smart Manufacturing

• Adaptivity with respect to three parameters:

• Assistance: Depending on the context– Reacting to the current situation on the shop floor, e.g., Loctite is

empty

– Aim: Fullfill KPIs

• Learning: Depending on the employee– Reacting to recently occurring events (e.g., a large number of

correctly or incorrectly performed measures)

– Long-term development goals (e.g., working towards a new job position)

– Aim: Learning


If employee in “primary work activity” and asks for assistance, then select

measures relevant for current station und machine state:

Procedure:

1. AG= workplace unit to which employee is assigned to.

Determined through request to user-model-service.

2. S = stations of AG.

Determined through request to domain model:

Workplace-group has machines. A machine consists of stations. Sort the

stations according to priority of each station.

3. MZ = machine state of S, sorted according to priority of machine state.

Determined through request to machine-information-service.

4. M = Measures for MZ.

Determined through request to domain model:

Measures are applicable to states.

5. M_f = Those measures of M the employee is authorized to perform

(with/without assistance).

Determined through request to user model.

Result: M_f

Example 1: Select Measures

Examples

1. AG = (Production

of standard

cylinders)

2. Machine =

(DNC_DNCB_DS

BC, …) . Stations

= (S10, S20, …) .

Pri(DNC)=8

3. MZ =

(LociteEmpty,

GreaseFew, …)

4. M =

(ChangeLoctite,

ChangeGrease,

…)

5. M_f =

(ChangeLoctite)


If employee in secondary activity (time for learning) and asks for training, then select

measures that are relevant for development goals.

A goal setting interview has set the development goals: content L, and/or employment group

B, and/or production items P.

Procedure:

1. B = Employment group. Determined by query to user model.

2. M = Relevant measure for B. Determined through query to domain model.

3. M_n = M without mastered measures. Determined through query to user model.

Result = M_n with instruction that these measures will be relevant in the future and can be

practiced in a learning factory or read anytime (without using a machine).

Example 2: Select Measures


If employee in “primary work activity” and asks for information, then select content relevant for

current station und machine state:

Procedure:

1. Z = Currently relevant machine states and stations (see previous rules).

2. A = Currently relevant machines

3. I = Content about Z and content about A.

Result= Content I.

Example Rules: Select Content

If employee in secondary activity (time for learning) and asks for content, then select

content relevant for development goals.

A goal setting interview has set the development goals: content L, and/or employment

group B, and/or production items P.

Procedure:

1. I_1 = Content that covers one/several of the following: employment group B,

tasks of B, and/or production entities P.

2. I_BR = Content that describes production entities relevant for B.

3. M = Measures relevant for B.

4. I_M = Content that describes production entities used for performing M.

5. I_T = I_B + I_F + I_BR + I_P+ I_M

6. I_S = I_T with sorting that moves already seen content to back of queue.

Result: Content L + I_S, with L marked as obligatory.


DigiLernPro: Digital Learning Scenarios

for workplace-integrated knowledge and

performance support

• Enable easy creation of content about– problems and solutions

– work processes

• Content shows step-by-step solutions, illustrated by multi-media content

• Content creation by experts, workers, teachers

• Content creation supported by intelligent tool– Ensures all relevant information

is captured• What are typical problems?

How can they be detected? What is the solution?

• What are the pre-/post-conditions of this step?

• …


Content Creation in DigiLernPro 1/2


• During work, record each step– using mobile app

(tablet, 1)

– action cam (2) or in-build camera

• Describe precondition, main activity and post-condition

2 1

Content Creation in DigiLernPro 2/2


• Describe activities using pictures,

video and text

• Describe typical errors, safety

information, and further

relevant information

Result: Process Model including the relevant media:

Intelligent Authoring Support

• Machine data to compute pre- and post-conditions

• Context recognition (proximity to machine, entity

recognition) to suggest

(partial) work process

models to reuse as well

as additional relevant

information


ADAPTION: Maturity-model-

based Migration to CPPS

Develop a migration modell to support manufacturingcompanies to develop cyber-physical productionsystems

Status Quo Migrationspfad

Zeit

Reifegra

d in d

en D

imensio

nen

Technik

, O

rganis

ation, P

ers

onal

Industrie 4.0

Heute Zukunft

Qualifi-

kation

höhere …

• Vernetzung

• Komplexität

• Automatisierung

• Flexibilität

Tätigkeits-

profile

ERP

/PPS

ERP

/PPS

Fertigungs-

management

Hallen-

boden

ressourcen-

orientierte

Planung

produkt-

orientierte

Planung

Intelligente CPPS-KomponentenZentral geplante Produktionsanlagen

MES

• Technik

• Organisation

• Personal

Wirts

chaft

-

lichkeit

Umsetzungskonzept

Audits

MES

Reifegrad

• FESTO Lernzentrum Saar GmbH

• FESTO AG & Co. KG

• Bernhard & Reiner GmbH

• Lothar Schulz-Mechanik GmbH

• PROXIA Software AG

• Jacobi Eloxal GmbH

• DFKI GmbH, Center for Learning Technology

• Forschungsgebiet Industrie- und

Arbeitsforschung, Technische Universität

Dortmund (TU Do)

• Lehrstuhl für Produktionssysteme, Ruhr-

Universität Bochum (RUB)

• Gemeinsame Arbeitsstelle RUB/IGM, Ruhr-

Universität Bochum (RUB)

Laufzeit: 01/16-12/18


Thank you

Carsten Ullrich

[email protected]