Towards predictive quality in production by applying a flexible process-independent meta-model

作     者：Junjie Liang Lukas Pelzer Kai Müller Simon Cramer Christoph Greb Christian Hopmann Robert H. Schmitt 

作者机构：RWTH Aachen University Laboratory for Machine Tools and Production Engineering Campus-Boulevard 30 52074 Aachen Germany RWTH Aachen University Institute for Plastics Processing Seffenter Weg 201 52074 Aachen Germany RWTH Aachen University Institut für Textiltechnik Otto-Blumenthal-Straße 1 52074 Aachen Germany Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany 

出 版 物：《Procedia CIRP》 

年 卷 期：2021年第104卷

页      面：1251-1256页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

主　　题：Predictive Quality Process Data Production Data Meta-Model Data Analytics 

摘      要：Predictive Analytics is increasingly applied as a basis for decision-making in product and process optimization of manufacturing companies. In this context, Predictive Quality enables companies to make data-driven predictions of product quality, with data integration being one of the most significant challenges. This paper provides insights into exemplary applications of a flexible process-independent meta-model to integrate data for multi-step manufacturing processes. Three application use cases from different production domains are presented, demonstrating the meta-model’s applicability for Predictive Quality applications in manufacturing companies without restrictions regarding the product or manufacturing process.