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Optimization of Heat Treatment Scheduling for Hot Press Forging Using Data-Driven Models

Seyoung Kim1, Jeonghoon Choi1, Kwang Ryel Ryu2,*

1 Department of Information Convergence Engineering, Pusan National University, Busan, 46241, Korea
2 Department of Computer Science and Engineering, Pusan National University, Busan, 46241, Korea

* Corresponding Author: Kwang Ryel Ryu. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 207-220.


Scheduling heat treatment jobs in a hot press forging factory involves forming batches of multiple workpieces for the given furnaces, determining the start time of heating each batch, and sorting out the order of cooling the heated workpieces. Among these, forming batches is particularly difficult because of the various constraints that must be satisfied. This paper proposes an optimization method based on an evolutionary algorithm to search for a heat treatment schedule of maximum productivity with minimum energy cost, satisfying various constraints imposed on the batches. Our method encodes a candidate solution as a permutation of heat treatment jobs and decodes it such that the jobs are grouped into batches satisfying all constraints. Each candidate schedule is evaluated by simulating the heating and cooling processes using cost models for processing time and energy consumption, which are learned from historical process data. Simulation experiments reveal that the schedules built using the proposed method achieve higher productivity with lower energy costs than those built by human experts.


Cite This Article

S. Kim, J. Choi and K. Ryel Ryu, "Optimization of heat treatment scheduling for hot press forging using data-driven models," Intelligent Automation & Soft Computing, vol. 32, no.1, pp. 207–220, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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