传统的性能预测和优化方法多基于经验和机理,未充分考虑到数据中蕴含的价值.如何挖掘钢材性能与相关工艺参数之间的线性非线性传递关系,实现高精度的性能预测和工艺优化是目前的研究热点之一.以热轧板带制造全过程的高维工艺质量数据集为基础,提出了一种融合机器学习性能预测模型和沙普利加和解释(Shapley additive explanation,SHAP)框架的热轧带钢性能优化方法.该方法首先以最大互信息系数(maximal information coefficient,MIC)相关性评价指标从高维的工艺数据中筛选与机械性能指标存在显著影响关系的有效变量;然后通过对比基于多输出支持向量回归模型(multiple output support vector regression,MS-VR)、支持向量回归模型(support vector regression,SVR)和随机森林的性能预测模型的预测精度,选取最优性能预测模型;最后,基于SHAP解释框架和最优预测模型进行工艺参数评价,度量各工艺参数对最终性能的量化影响,并通过对操作变量按SHAP分析的结果进行调整,以验证性能优化的效果.实验结果表明,本文提出的性能优化方法可显著按需求改善性能指标,对于钢铁生产过程的机械性能管控具有指导意义.
Mechanical properties optimization method of hot strip based on SHAP framework
Traditional mechanical properties prediction and optimization methods are mostly based on experience and mechanisms,and do not fully consider the value contained in the data.One of the cur-rent research hot spots is how to explore the linear and nonlinear transfer relationship between steel performance and related process parameters,construct high-precision performance prediction models,and achieve process optimization.Based on the high-dimensional process quality dataset of the throughout manufacturing process of hot rolled strip,a performance optimization method for hot rolled strip steel was proposed that integrated machine learning performance prediction model and Shapley additive explanation(SHAP)interpretation framework.This method first uses maximal information coefficient(MIC)metrics to select effective variables that have a significant impact on mechanical performance indicators from high-dimensional process parameters dataset.Then,by comparing the prediction accuracy of performance prediction models based on multiple output support vector regres-sion(MSVR),support vector regression(SVR),and random forest,the optimal performance predic-tion model is selected.Finally,based on the SHAP interpretation framework and optimal prediction model,process parameter evaluation is conducted to measure the quantitative impact of each process parameter on the final performance,and the operational variables are adjusted according to the results of SHAP analysis to verify the effectiveness of performance optimization.The experimental results in-dicate that the performance optimization method proposed in this paper can significantly improve per-formance indicators according to demand,and has guiding significance for mechanical performance control in steel production processes.
hot stripmechanical properties predictionmachine learningShapley additive explana-tions(SHAP)mechanical properties optimization
万方数据
维普
