Rules for Adaptive Learning and

Assistance 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

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

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.

• Industry 4.0 and other new technologies increase complexity of– usage and maintenance of production lines

– control of the production process

• Employee under constant pressure– to solve problems occurring on the

shop floor as fast as possible,

– to improve work-related knowledge, skills, and capabilities

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

(Hirsch-Kreinsen, 2014)

Assistance- and Knowledge-Services

for Smart Production

• Information providing and training processes have to become – more flexible

– integrated in the workplace

– individualized

• 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

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

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

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

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

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Artificial Intelligence in Education

• Intelligent Tutoring Systems and

Adaptive Learning Environments

provide adaptive and

contextualized support of learners

• Significant body of research on

adaptive support in university and

highly structured domains such

as mathematics, physics and

computer science

• Service-oriented architectures for

learning

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Domain Model

Learner Model

Pedagogical Model

Adaptivity in Smart

Manufacturing

• Main activity: Fulfill Key Performance Indicators (KPI) Assistance: Depending on the contexta) Reacting to the current situation on the shop floor, e.g.,

Loctite is empty

• Secondary activity: Time for Learning Learning: Depending on the employeeb) Reacting to recently occurring events (e.g., a large number

of correctly or incorrectly performed measures)

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

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

If employee is in state “main work activity” and asks for assistance, then

select work procedures relevant for current station und machine state:

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

Determined through request to user-model-service.

2. S = sort(stations ∪ installation) of AG. Determined by querying

domain model: There, each workplace unit is assigned to work with

specific installations. An installation consists of stations. Sort the

stations according to priority of each station.

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

state. Determined through request to machine-information-service.

4. P = Procedures for MS. Determined through query of domain

model: Procedures are applicable to machine states.

5. P_a = those procedures of M the employee is authorized to

perform (with or without assistance). Determined through request

to user model.

Result: P_a

Select Measures, Main Activity

Examples

1. WU = (Production

of standard

cylinders)

2. I =

(DNC_DNCB_DSB

C, …) . Stations =

(S10, S20, …).

Pri(DNC)=8

3. MS = (LociteEmpty,

GreaseFew, …)

4. P = (ChangeLoctite,

ChangeGrease, …)

5. P_a =

(ChangeLoctite)

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

If the employee is in state secondary activity (“time for learning”) and asks for

procedures, then select procedures relevant to development goals (content C_A,

and/or position PO, and/or production items PI_A).

1. PO = agreed future position of employee. Determined by query to user model.

2. P = relevant work procedures for PO. Determined through query to domain

model: Each position has tasks, and work procedures perform tasks.

3. P_U = P without mastered procedures. Determined through query to user model

(which keeps track of mastered procedures).

Result = P_U.

Select Measures, Secondary Activity

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

If the employee is in state “main work activity” and asks for information, then select

content relevant for the stations assigned to and their machine states:

1. WU = workplace unit to which employee is assigned to; P = position of

employee. Determined through request to user-model-service.

2. S, MS = Machine states and stations/installations relevant for WU (see

previous rule)

3. I = Content about S∪MS for target-group = P or without target-group.

Determined by querying domain model, which contains metadata that relates

content to domain model entities and specifies its target-groups, if any.

Result = Content I.

For instance: operation manuals, circuit diagrams, and other content that provides

information about the current situation enabling the employee to overcome

occurring problems.

Select Content, Main Activity

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

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

content relevant to current work history (machines and procedures worked with). Development

goals: content C_A, and/or position PO, and/or production items PI_A.

1. PI = production items with which employee has worked with in the last four weeks, P_S the

procedures that she performed successfully and P_N those not performed successfully.

This information is stored in the learner-record-service.

2. C_P_N = content about P_N and production items used by P_N, with already seen content

sorted to the back (this information is stored in the learner-record-service).

3. C_P_S = content about P_S or about production items used by P_S or about PI.

4. C_P = Content that covers one/several of the following: position PO, tasks of PO, or

production entities PI_A.

5. C_PI_PO = Content that describes production entities relevant for PO.

6. C_P_PO = Content that describes production entities used for performing procedures

relevant for PO.

7. C_T = C_P_S ∪ C_P ∪ C_PI_PO ∪ C_P_PO, with already seen content sorted to the

back.

Result: Content C_P_N + C_A + C_T, with duplicates removed.

Select Content, Secondary Activity

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Example

• John Doe: – assembly worker, workplace group “assembly of standard cylinders”

– Cleared for “refill adhesive”.

– Development goals: learn about produced product (the standard cylinder ABC); prepare for performing the maintenance task “replace grease barrel”.

• Fiona Smith– machine operator, workplace group “assembly of standard cylinders”

– Cleared for all maintenance procedures.

– Development goals: Learn about Industry 4.0, standard cylinder ABC; prepare for a customer meeting

• During their shift, adhesive & grease drop to low levels. Support:– Procedures for John: “refill adhesive”, followed by procedure for less important tasks, such as

cleaning the work environment.

– Procedures for Fiona: “refill adhesive” and “replace grease barrel”, followed by less important procedures.

– Content for John: security information and adhesive specification sheet

– Content for Fiona: layout of stations and technical documentation.

• Time for learning. – Procedure for John: “replace grease barrel”

– Procedures for Fiona: maintenance procedures of the installations of the customer.

– Content for John: general technical information about the standard cylinder, a video showing how it is used in other machines, and general information about site

– Content for Fiona: course on Industry 4.0 and specific technical information about the cylinder

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Conclusion

• Support of problem solving and learning on the shop floor by adaptive services

• First steps into researching adaptivity on the shop floor on formal level

• Evaluation: System Usability Scale (Brooke, 1996) and Think Aloud Protocol– 6 employees of each industry partner received a number

of tasks to solve using the system

– SUS: average score of 86.9

– Think-aloud protocols did not show any problematic points

– Only first steps, further evaluations underway

Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor

Thank you

Carsten Ullrich

carsten.ullrich@dfki.de