|Title:||Ontology matchmaking of product ramp-up knowledge in manufacturing industries : how to transfer a cake-baking recipe between bakeries||Language:||English||Authors:||Willmann, Roland||Qualification level:||Doctoral||Advisor:||Kastner, Wolfgang||Issue Date:||2016||Number of Pages:||173||Qualification level:||Doctoral||Abstract:||
The manufacturing industry faces a shortening of product life cycles and decreasing lot sizes (lot size 1) which is particularly challenging with respect to the ability to perform an efficient and predictable ramp-up of new products. The deterministic ramp-up of new products in existing production systems is therefore mission-critical. The ramp-up of a new product starts with the transition of knowledge about the new product from the development production system to the mass-production system, and it ends when instances of the new product are produced and shipped at the latter with planned volume and with repeatable low number of defects. But manufacturing companies still struggle with the predictability of costs and duration as well as the ultimately recoverable yield for the ramp-up of new products. Unfortunately, today's multi-disciplinary ramp-up teams production systems lack a well-structured approach and a common multidisciplinary knowledge base in order to take systematically advantage of knowledge reuse from already performed processes, their capabilities and already produced forerunner products. This knowledge can be used in order to specify the production processes of the new product at the respective production system. In this thesis, a knowledge-based product ramp-up process (K-RAMP) and the underlying multidisciplinary ontology model are proposed in order to interconnect knowledge about a new product of an original production system (e.g., low volume pilot production or development line) with knowledge about the production of forerunner products of a target production system (e.g., high volume production). Based on the existing knowledge assets of forerunners-, subcomponents or already qualified process segments of the target production system, the introduced approach systematically helps to determine and to recommend opportunities for reuse in order to produce the new product. As novelty, the developed derivation logic does not only consider identities between product specifications or between specifications of capabilities of the production system but also similarities due to the generalization of information fragments. An implementation of the design is performed which is based on the Semantic Web. Domains, where alternative solutions need to be applied are also addressed which lead to a hybrid architecture of K-RAMP utilizing predominantly Semantic Web technologies with some imperatively programmed components. It is not the intention of K-RAMP to make production knowledge reusable. This challenge is subject to the respective enterprises within the scope of their management strategies concerning the modularity, scalability and compatibility of products and process segments. K-RAMP contributes with adequate ontology models being derived from existing industry standards and guidelines as well as with a structured, automatable process. Assuming the previously outlined premises as given, K-RAMP can automatically derive recommendations about production knowledge reuse in the scope of a product ramp-up scenario. Such recommendations may help to perform product ramp-up projects faster and with more deterministic results. This work is of particular interest for ICT-experts in the manufacturing industry who are facing challenges from the perspective of information science in order to improve the situation of product ramp-up projects. For the same target group, the work is also of interest with respect to the application of Semantic Web technologies in production environments in general. Due to the application of these technologies, the results of this work are also applicable as starting point for new research which is fostered by Industrie 4.0. This is specifically true for the unification and standardization of public product specifications or production service specifications along the supply chain and research on search engines for the same purpose. This work is therefore of particular interest for research on the application of the Semantic Web for mastering of digitalized supply chains in manufacturing industries as well as on knowledge representation in manufacturing industries for mastering decreasing product lifecycles.
|Keywords:||Semantic Web; Product ramp-up; Recommender system; Knowledge management; Cyber-physical production system; Design for manufacturing; Industrie 4.0; Digital supply chain management||URI:||https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-91889
|Library ID:||AC13409227||Organisation:||E183 - Institut für Rechnergestützte Automation||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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