Abstract
Machining sequence planning for milling, also called operation sequence planning, can be considered one of the most important tasks of manufacturing process planning. Computer-Aided Process Planning (CAPP) is one of the application areas of machining sequence planning and is also an important interface between computer-aided design and computer-aided manufacturing. The planning tasks are multidimensional, but they are often handled in a linear way, which is one of the problems of conventional CAPP systems. This problem leads to limited solution space. The solution can be far away from the optimum or even not represented in reality due to resections caused by technical reasons. Multiple planning tasks cannot be combined in every way. They are restricted by the technological properties of the machining process, which makes the solution even more complicated. In contrast to the conventional approach, this paper generates valid sequences of operations based on a graph (Hamiltonian path) using a Simulated Annealing algorithm. Simulated Annealing is meta-heuristic, which finds global extrema within a graph by approximation. The algorithm’s goal is to minimize the number of setups and tool changes. To evaluate the validity of the method, the Simulated Annealing algorithm was tested on parts with known experimental machining sequence optimum.
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Acknowledgements
The IGF project 21808 BR/2 of the Bundesvereinigung Logistik (BVL) e.V. is funded via the AiF as part of the program to promote joint industrial research (IGF) by the Federal Ministry of Economics and Energy based on a resolution of the German Bundesta.
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Langula, S., Erler, M., Brosius, A. (2023). An Efficient Method for Automated Machining Sequence Planning Using an Approximation Algorithm. In: Liewald, M., Verl, A., Bauernhansl, T., Möhring, HC. (eds) Production at the Leading Edge of Technology. WGP 2022. Lecture Notes in Production Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-18318-8_72
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