Modeling hot strip rolling process under framework of generalized additive model

Abstract

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models (GAM) to generate a practical model with generalization and precision. Specifically, the proposed modeling method includes the following steps. Firstly, the influence factors are screened using mechanism knowledge and data-mining methods. Secondly, the unary GAM without interactions including cleaning the data, building the sub-models, and verifying the sub-models. Subsequently, the interactions between the various factors are explored, and the binary GAM with interactions is constructed. The relationships among the sub-models are analyzed, and the integrated model is built. Finally, based on the proposed modeling method, two prediction models of mechanical property and deformation resistance for hot-rolled strips are established. Industrial actual data verification demonstrates that the new models have good prediction precision, and the mean absolute percentage errors of tensile strength, yield strength and deformation resistance are 2.54%, 3.34% and 6.53%, respectively. And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

摘要

本研究在广义可加模型的框架下, 将工业大数据和过程机理分析相融合, 提出了一种新的建模 方法, 从而建立兼顾泛化能力和预测精度的实用模型。新的建模方法主要包括四个方面。首先, 利用 机理知识和数据挖掘方法对影响因素进行筛选。其次, 提出了一元无交互作用的广义可加模型的建模 步骤, 包括清理数据、建立子模型和验证子模型。随后, 研究了各影响因素间的交互作用, 构建了二 元有交互作用的广义可加模型。最后, 分析各子模型之间的关系, 并建立整体模型。基于本文提出的 建模方法, 建立了热轧带钢力学性能预测模型和变形抗力模型。实际工业数据验证表明新建立的模型 具有很好的预测精度, 抗拉强度、屈服强度和变形抗力的平均绝对误差分别为 2.54%、3.34%和6.53%。 实验结果表明, 本文提出的建模方法为工业过程建模提供了一种新的思路。