Optimization of cold rolling process recipes based on historical data

被引：0

作者：

Cuznar, Kristjan ^{[1
,2
]}

Glavan, Miha ^{[1
]}

机构：

[1] Jozef Stefan Inst, Ljubljana, Slovenia

[2] Univ Ljubljana, Fac Elect Engn, Ljubljana, Slovenia

来源：

2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON 2022) | 2022年

基金：

欧盟地平线“2020”;

关键词：

process optimization; big data; machine learning; modelling; identification; SYSTEMS;

D O I：

10.1109/MELECON53508.2022.9843127

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Cold rolling is a process in sheet metal production used to reduce thickness, standardise thickness, and ensure suitable mechanical properties of the workpiece. In order to ensure the appropriate product quality, it is extremely important to set the rolling mill correctly, which is usually determined by predefined recipes. In this paper, a decision support tool is proposed that enables recipe adjustment for individual workpieces. It is based on the analysis of historical process data and its impact on the associated key performance indicators (KPIs). The support tool includes a data-based process model, a multi-criteria optimization algorithm and a user interface.

引用

页码：1 / 6

页数：6

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