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An Ontology for Learning Services
on the Shop Floor
Carsten Ullrich
Associate Head
Educational Technology Lab (EdTec)
at t...
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...
Artificial Intelligence in Education
• Intelligent Tutoring Systems and
Adaptive Learning Environments
provide adaptive an...
Related Work: Support
• Smart and adaptive environments for workplace-based
learning and manufacturing are highly relevant...
Related Work: Ontologies
• Ontologies: very restricted areas such as
– Toolpath planning (Xu, Wang, & Rong, 2006)
– Fixtur...
Description of the Ontology
• Blueprint to describe the general concepts
(types) and their relationships relevant for
teac...
State
• The state describes the state of a domain entity and distinguishes
between
– machine state, which describes the st...
Organizational Unit
• Organizational unit and its specializations
describe the structure of a company:
– organization
– lo...
Production Item
• Describes entities used for or created during the production of
goods:
– Equipment: Equipment includes t...
Processmodel Entity
• Describes processes that occur in the target
domain:
– Process: A sequence of states, which is trigg...
Task
• Describes the task of human operator such as
– maintenance,
– repair,
– operations / management,
– ...
Carsten Ullr...
Relations (Properties)
• requires: a machine state requires a procedure.
• produces: a production step produces a produced...
Usage of the Ontology
• To build up a domain model, i.e., :
a formal representation of the shop floor at a
specific site o...
Reasoning and Querying
• If a domain model is stored in a semantic
database, then information can be automatically
inferre...
Application in APPsist
• Using the ontology, we defined domain models
representing three companies:
– A small-sized compan...
Content Metadata
• Existing content can be described by the ontology
– learning materials,
– documentation (user manuals, ...
Machine Information
• Machines on the shop floor use a multitude of sensors
for managing the production process
• The sens...
Content and Procedure
Selection
• Assistance and knowledge services use metadata to
select content adequate for the curren...
Carsten Ullrich, An Ontology for the Shop Floor
Carsten Ullrich, An Ontology for the Shop Floor
Conclusion
• An ontology that captures the entities and their
interrelationships relevant for describing and reasoning
abo...
Thank you
Carsten Ullrich
carsten.ullrich@dfki.de
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An Ontology for Learning Services on the Shop Floor

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An ontology expresses a common understanding of a domain that serves as a basis of communication between people or systems, and enables knowledge sharing, reuse of domain knowledge, reasoning and thus problem solving. In Technology-Enhanced Learning, especially in Intelligent Tutoring Systems and Adaptive Learning Environments, ontologies serve as the basis of adaptivity and personalization. For mathematics learning and similarly structured domains, ontologies and their usage for adaptive learning are well understood and established. This contribution presents an ontology for the industrial shop floor (the area of a factory where operatives assemble products) and illustrates its usage in several learning services.

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An Ontology for Learning Services on the Shop Floor

  1. 1. An Ontology for Learning Services 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, An Ontology for 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, – and simultaneously to improve work-related knowledge, skills, and capabilities Carsten Ullrich, An Ontology for the Shop Floor
  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, An Ontology for the Shop Floor
  5. 5. 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 highly structured domains such as mathematics, physics and computer science • Domain model: Information about the taught concepts and their interrelationships  Often represented as an ontology • Well established for mathematics, etc. • Limited existing work for manufacturing Carsten Ullrich, An Ontology for the Shop Floor Domain Model Learner Model Pedagogical Model
  6. 6. Related Work: Support • Smart and adaptive environments for workplace-based learning and manufacturing are highly relevant (Mavrikios, Papakostas, Mourtzis, & Chryssolouris, 2013) (Koper, 2014) • Existing work focuses on very specific areas – assembly, to increase process quality (Stoessel, Wiesbeck, Stork, Zaeh, & Schuboe, 2008) (Stork, Stößel, & Schubö, 2008), – collaboration between machine and operator (Sebanz, Bekkering, & Knoblich, 2006) (Lenz, et al., 2008), – control (Bannat, et al., 2009) – monitoring (Stork genannt Wersborg, Borgwardt, & Diepold, 2009). – controlling information flow to avoid cognitive overload (Lindblom & Thorvald, 2014) • Results (methods) cannot be compared or transferred Carsten Ullrich, An Ontology for the Shop Floor
  7. 7. Related Work: Ontologies • Ontologies: very restricted areas such as – Toolpath planning (Xu, Wang, & Rong, 2006) – Fixture design (Ameri & Summers, 2008). T – Information collected during the lifespan of a product (Wahlster, 2013). • Thousands of standards and specifications – Technical committee ISO/TC 184 for instance, has published more than 800 ISO standards that describe different aspects of automation systems and integration. – Very technical focus, • None of this work is on the required level of abstraction for learning on the shop floor: a domain description that focuses on the learning needs of the operators. • The potential of such descriptions shown for – generating assembling instructions automatically from product lifecycle data (Stoessel, Wiesbeck, Stork, Zaeh, & Schuboe, 2008), – supporting the transfer of practical knowledge (Blümling & Reithinger, 2015), – providing manufacturing assembly assistance (Alm, Aehnelt, & Urban, 2015). • However, none of these works has described the employed ontologies in sufficient detail to judge their general applicability nor to enable reuse Carsten Ullrich, An Ontology for the Shop Floor
  8. 8. Description of the Ontology • Blueprint to describe the general concepts (types) and their relationships relevant for teaching and learning on the shop floor • Highest level of the ontology: – state, – organizational unit, – production item, – function, – process model entity Carsten Ullrich, An Ontology for the Shop Floor
  9. 9. State • The state describes the state of a domain entity and distinguishes between – machine state, which describes the state of a production machine – software state, which describes the state of a software. – States have a priority, indicating their severity (10, highly critical, to 1, not critical) • Machines states are divided into specific sub-states (adapted from (Kasikci, 2010)): – Up state is the state of a machine characterized by the fact that it can perform a required function: • operating state, which describes when a machine is performing as required, and • idle state, which describes a machine which is in an up-state and non-operating, during non-required time. – Disabled state is the state of a machine characterized by its inability to perform a required function: • External disabled state, for describing a machine in an up-state, but lacking required external resources or is disabled due to planned actions other than maintenance • Internal disabled state, the state of a machine characterized by an inability to perform a required function Carsten Ullrich, An Ontology for the Shop Floor
  10. 10. Organizational Unit • Organizational unit and its specializations describe the structure of a company: – organization – location – department – department unit – workplace unit – workplace – employee Carsten Ullrich, An Ontology for the Shop Floor
  11. 11. Production Item • Describes entities used for or created during the production of goods: – Equipment: Equipment includes the entire technical apparatus used during production as well as property (plot of land), buildings, etc. In contrast to materials, equipment is not consumed. • building • plot of land • accessory (a mechanical device that supports the production process, but does not perform the production itself.) • production machine (a stationary device directly involved in the production process.). – Produced artifact: The marketed object that is the result of the production process, with the specializations subassembly, semi- finished product, product, and part. – Material: Materials are physical entities consumed in the production of the finished product, with the specializations consumable, auxiliary material, and raw material. Carsten Ullrich, An Ontology for the Shop Floor
  12. 12. Processmodel Entity • Describes processes that occur in the target domain: – Process: A sequence of states, which is triggered by an event and leads to a final state: • domain process (overarching process performed for the realization of the product, such as the production process and business process) • course of action (a process that is performed by a human, such as a work procedure). – Process element: The states of the process, such as an action (for course of action) or a production step (for the production process). Carsten Ullrich, An Ontology for the Shop Floor
  13. 13. Task • Describes the task of human operator such as – maintenance, – repair, – operations / management, – ... Carsten Ullrich, An Ontology for the Shop Floor
  14. 14. Relations (Properties) • requires: a machine state requires a procedure. • produces: a production step produces a produced artifact • executes: a production machine executes a production step • has part: expresses that some entity is part of another entity. This relation can be applied to many of the types. • has task: an employee has tasks • has step: a process ‘has the step’ process element • has state: a production machine has a machine state • has procedure: a task has a procedure (meaning that a task can be achieved by following a series of actions). • is part of: the inverse relation of has part • realizes: a production machine realizes a production process. Carsten Ullrich, An Ontology for the Shop Floor
  15. 15. Usage of the Ontology • To build up a domain model, i.e., : a formal representation of the shop floor at a specific site of a company • Requires specific subtypes and instances of the types. • For example: Lenovo in its production department P1 in Guandong produces the laptop X •  Requires the instance Lenovo of the concept organization, the instance Guandong of location, the instance P1 of department, and the instance X of product. Using the property produces, one can specify that X is produced in P1. Carsten Ullrich, An Ontology for the Shop Floor
  16. 16. Reasoning and Querying • If a domain model is stored in a semantic database, then information can be automatically inferred • For instance: From X is produced in P1 we can infer that X is a product of Lenovo and located in Guandong • Similar functionality is achieved by appropriate queries • Heavily used by assistance and knowledge services Carsten Ullrich, An Ontology for the Shop Floor
  17. 17. Application in APPsist • Using the ontology, we defined domain models representing three companies: – A small-sized company produces complex customer- specific tools and devices for car manufacturers – A medium-sized company produces customer-specific welding and assembly lines for car manufacturers. – A large-sized company produces pneumatic and electric controllers for the automation of assembly- lines, which are used in customer-specific products as well as in their own production Carsten Ullrich, An Ontology for the Shop Floor
  18. 18. Content Metadata • Existing content can be described by the ontology – learning materials, – documentation (user manuals, programming handbooks), – order-related information (parts lists, wiring diagrams). • It allows to specify the production items a piece of content refers to, the target-audience, etc. • Other services use this information to perform information retrieval tasks in different contexts Carsten Ullrich, An Ontology for the Shop Floor
  19. 19. Machine Information • Machines on the shop floor use a multitude of sensors for managing the production process • The sensor values trigger actions of the human operators • However, low-level senor data is too specific to be interpreted by support software • In APPsist, a machine information service translates the sensor data into instances of the type machine state and propagates the instances to the supporting services. • This way, the learning supporting services can be set up to react to high-level, abstract machine states and thus can be more easily reused with new machines. Carsten Ullrich, An Ontology for the Shop Floor
  20. 20. Content and Procedure Selection • Assistance and knowledge services use metadata to select content adequate for the current context (described by the instances) • Example: content selector service in APPsist: – Its rules inspect the organizational units to determine which employees are assigned to the production machine, and uses information specified about the relevant production items to find content relevant for the employee in the specific situation. • See my talk on Sunday, 11:30-12:30 Session FP 30.4 • The rules do not encode information about the specific company in which they are used (the instances of the domain)  Transferable between companies Carsten Ullrich, An Ontology for the Shop Floor
  21. 21. Carsten Ullrich, An Ontology for the Shop Floor
  22. 22. Carsten Ullrich, An Ontology for the Shop Floor
  23. 23. Conclusion • An ontology that captures the entities and their interrelationships relevant for describing and reasoning about the shop floor from an educational perspective. • Used – to represent the shop floor of three different companies – as a basis for a number of reusable learning services, i.e., functionally highly specialized software applications for supporting problem solving and learning of human operators • Lightweight (no axioms) • Process of describing a specific shop currently is manual work. Not scalable. Future work: Using information extraction methods for automatization Carsten Ullrich, An Ontology for the Shop Floor
  24. 24. Thank you Carsten Ullrich carsten.ullrich@dfki.de
  • ssuser6eac64

    Feb. 22, 2018

An ontology expresses a common understanding of a domain that serves as a basis of communication between people or systems, and enables knowledge sharing, reuse of domain knowledge, reasoning and thus problem solving. In Technology-Enhanced Learning, especially in Intelligent Tutoring Systems and Adaptive Learning Environments, ontologies serve as the basis of adaptivity and personalization. For mathematics learning and similarly structured domains, ontologies and their usage for adaptive learning are well understood and established. This contribution presents an ontology for the industrial shop floor (the area of a factory where operatives assemble products) and illustrates its usage in several learning services.

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