A design of experiments based on the Normal-boundary-intersection method to identify optimum machine settings in manufacturing processes
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
Finding the appropriate machine settings for a given manufacturing process is an important issue in industrial production. A set of minimum and maximum machine settings correspond to the lower and upper quality limits that are specified for the produced product, and by this define the boundaries of all appropriate machine settings. This paper shows that these boundaries are the solution of a multi‐objective optimisation problem, which is called the optimum machine settings problem. However, for most processes there is no mathematical model of the manufacturing process available, which maps the setting parameters on the quality key figures in a way that allows to compute the optimisation problem. In this case, experiments may provide the required empirical data simultaneously while executing the optimisation procedure. Using a case study on heat sealing in industrial packaging, the paper shows, how to develop a design of experiments based on the Normal‐boundary‐intersection method (NBI), and how to generate the Pareto‐frontier by executing test according to this test plan. It addresses the specific limitations inherent in solving an optimisation problem by experiments. The behaviour of the method towards discrete and binary objectives and constraints is discussed.
Details
Original language | English |
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Number of pages | 9 |
Publication status | Published - Sept 2023 |
Peer-reviewed | Yes |
Symposium
Title | 93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics |
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Abbreviated title | GAMM 2023 |
Conference number | 93 |
Duration | 30 May - 2 June 2023 |
Website | |
Degree of recognition | International event |
Location | Technische Universität Dresden |
City | Dresden |
Country | Germany |
External IDs
Mendeley | 2497a8b7-1d32-3ec8-a2d4-8fb91b0821ff |
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