Open Access

ARTICLE

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

Seyoung Kim¹, Jeonghoon Choi¹, Kwang Ryel Ryu^2,*

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. https://doi.org/10.32604/iasc.2022.021752

Received 13 July 2021; Accepted 16 August 2021; Issue published 26 October 2021

Abstract

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.

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