Design of economically-optimized manufacturing process sequences using cross-process models. (May 2021)

• Record Type: Journal Article
• Authors: Beckers, Alexander; Stauder, Lars; Grünebaum, Timm; Barth, Sebastian; Bergs, Thomas
• Is Part Of: CIRP journal of manufacturing science and technology. Volume 33(2021)
• Page Start: 15
• Page End: 29
• Publication Date: 2021-05
• Subjects: Economic optimization of manufacturing process sequences -- Cross-process design -- Economic and technological relationships in process sequences -- Metamodeling -- Serial production; Manufacturing processes -- Periodicals; 670.5
• DOI: 10.1016/j.cirpj.2021.02.008
• Languages: English
• ISSNs: 1755-5817

Abstract

Abstract: Manufacturing companies have to design their manufacturing process sequences so that the product requirements are fulfilled cost-efficiently. It is important that process sequences are designed holistically and that the technological and economic relationships between individual processes are taken into account. It is particularly important to consider these relationships in serial production, because technology experts often design manufacturing processes one process at a time. While the different processes are linked at higher planning levels, the relationships between the manufacturing processes are insufficiently considered. At the design phase, the economic potential of cross-process optimization could be tapped into but remains unused. An approach for the design of economically-optimized manufacturing process sequences is presented in this paper. It describes how technological relationships in a process sequence can be modeled using cross-process models. The approach includes a newly developed model which can be used to economically evaluate process sequences. Within this model, process parameter-dependent variables of the individual manufacturing processes and temporal as well as technological relationships between the processes are considered in order to optimize manufacturing process sequences. A case study shows the potential of cross-process optimization.