The steelmaking process parameter optimization with a surrogate model based on convolutional neural networks and the firefly algorithm

Yung Chun Liu, Ming Huwi Horng, Yung Yi Yang, Jian Han Hsu, Yen Ting Chen, Yu Chen Hung, Yung Nien Sun, Yu Hsuan Tsai

研究成果: Article同行評審

摘要

High‐strength low‐alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model to build such a surrogate model. We then applied three bio‐inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9000 test samples used for building the surrogate model and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

原文English
文章編號4857
期刊Applied Sciences (Switzerland)
11
發行號11
DOIs
出版狀態Published - 2021 六月 1

All Science Journal Classification (ASJC) codes

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程

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