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Rules for Adaptive Learning and
Assistance on the Shop Floor
Carsten Ullrich
Associate Head
Educational Technology Lab (Ed...
The Workplace is
Transforming
• Challenges for Europe's manufacturing industry:
– Accelerating innovation
– Shorter produc...
Human Operators at
Tomorrow’s Workplace
• Despite the increasing automation, human operators have
place on shop floor  wi...
Assistance- and Knowledge-Services
for Smart Production
• Information providing and training processes have to become
– mo...
Partly automated assembly
line
Support for maintenance
5-axis drill
Support for machine usage
Pilot Scenarios
Partner
Pilo...
3 manual assembly
stations
Main host computer
Monitoring and analysis
SPS
Controlling the machines
Coarse control and
moni...
Artificial Intelligence in Education
• Intelligent Tutoring Systems and
Adaptive Learning Environments
provide adaptive an...
Adaptivity in Smart
Manufacturing
• Main activity: Fulfill Key Performance Indicators (KPI)
 Assistance: Depending on the...
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 sta...
If the employee is in state secondary activity (“time for learning”) and asks for
procedures, then select procedures relev...
If the employee is in state “main work activity” and asks for information, then select
content relevant for the stations a...
If employee is in state secondary activity (“time for learning”) and asks for content, then select
content relevant to cur...
Example
• John Doe:
– assembly worker, workplace group “assembly of standard cylinders”
– Cleared for “refill adhesive”.
–...
Conclusion
• Support of problem solving and learning on the shop
floor by adaptive services
• First steps into researching...
Thank you
Carsten Ullrich
carsten.ullrich@dfki.de
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Rules for Adaptive Learning and Assistance on the Shop Floor

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Today’s shop floor, the area of a factory where operatives assemble products, is a complex and demanding work environment. The employed and produced technology becomes ever more complex, and employees are responsible for an increasing amount of tasks. As a consequence, the employee is under constant pressure to solve problems occurring on the shop floor as fast as possible, and simultaneously to improve his work-related knowledge, skills, and capabilities.
This paper gives an account how adaptive technology can support the employee on the shop floor in these respects. It describes the organizational requirements to take into consideration and describes a set of rules that support the employee in problem solving and knowledge acquisition.

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Rules for Adaptive Learning and Assistance on the Shop Floor

  1. 1. 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)
  2. 2. 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
  3. 3. 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)
  4. 4. 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
  5. 5. 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
  6. 6. 3 manual assembly stations Main host computer Monitoring and analysis SPS Controlling 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 Competence Pilot Study: Festo Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor
  7. 7. 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
  8. 8. Adaptivity in Smart Manufacturing • Main activity: Fulfill Key Performance Indicators (KPI)  Assistance: Depending on the context a) Reacting to the current situation on the shop floor, e.g., Loctite is empty • Secondary activity: Time for Learning  Learning: Depending on the employee b) 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
  9. 9. Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor
  10. 10. Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor
  11. 11. Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor
  12. 12. Carsten Ullrich, Rules for Adaptive Learning and Assistance on the Shop Floor
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  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. Thank you Carsten Ullrich carsten.ullrich@dfki.de
  • MichaelRoll1

    Oct. 31, 2016

Today’s shop floor, the area of a factory where operatives assemble products, is a complex and demanding work environment. The employed and produced technology becomes ever more complex, and employees are responsible for an increasing amount of tasks. As a consequence, the employee is under constant pressure to solve problems occurring on the shop floor as fast as possible, and simultaneously to improve his work-related knowledge, skills, and capabilities. This paper gives an account how adaptive technology can support the employee on the shop floor in these respects. It describes the organizational requirements to take into consideration and describes a set of rules that support the employee in problem solving and knowledge acquisition.

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