Scholarlink

<p>Mechanical properties are critical to the quality of hot-rolled steel pipe products. Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control. To address this, an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process, providing a complete dataset for mechanical property prediction. The adaptive bandwidth kernel density estimation (ABKDE) method was proposed to adjust bandwidth dynamically based on data density. Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties. The proposed method was deployed in a large-scale steel plant, which demonstrated superior prediction interval performance compared to lower upper bound estimation, mean variance estimation, and extreme learning machine-adaptive bandwidth kernel density estimation, achieving a prediction interval normalized average width of 0.37, a prediction interval coverage probability of 0.94, and the lowest coverage width-based criterion of 1.35. Notably, shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Explainable machine learning for predicting mechanical properties of hot-rolled steel pipe

  • 期刊论文

  • 发表日期：

    2025年2月24日

查看全文
  • Jing-dong Li,
  • You-zhao Sun,
  • Xiao-chen Wang,
  • Quan Yang,
  • Guo-dong Liu,
  • Hao-tang Qie,
  • Feng-xia Li

摘要

Mechanical properties are critical to the quality of hot-rolled steel pipe products. Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control. To address this, an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process, providing a complete dataset for mechanical property prediction. The adaptive bandwidth kernel density estimation (ABKDE) method was proposed to adjust bandwidth dynamically based on data density. Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties. The proposed method was deployed in a large-scale steel plant, which demonstrated superior prediction interval performance compared to lower upper bound estimation, mean variance estimation, and extreme learning machine-adaptive bandwidth kernel density estimation, achieving a prediction interval normalized average width of 0.37, a prediction interval coverage probability of 0.94, and the lowest coverage width-based criterion of 1.35. Notably, shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.