[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126679-en":3,"doc-seo-126679-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126679,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Predicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms","Hot rolled steel sheets such as SAPH440 must satisfy product specifications controlled mainly by mechanical properties. Yield strength, ultimate tensile strength, and elongation are measured and used to classify products into three grades: Class 1 (meets specification), Class 2 (moderate quality), and Class 3 (low). Because process variations can distort mechanical properties and increase setup time, machine learning is used to improve prediction accuracy. Experiments show random forest achieves 70.0% accuracy and a 70.0% macro average F-1 score, reducing initial setup and trial run costs by about 37,000 USD per grade.","Article  \nPredicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms  \nChaovarat Junpraduba and Krisada Asawarungsaengkulb,*  \nDepartment of Industrial Engineering, Faculty of Engineering, King Mongkut ’s University of Technology North Bangkok. 1518, Pracharat 1 Road, Wongsawang, Bangsue, Bangkok 10800, Thailand [E-mail:](E-mail: aChaovaratj1973@gmail.com)[ a](E-mail: aChaovaratj1973@gmail.com)[Chaovaratj1973@gmail.com](E-mail: aChaovaratj1973@gmail.com), b,*[krisada.a@eng.kmutnb.ac.th](krisada.a@eng.kmutnb.ac.th) (Corresponding author)  \nAbstract. The mechanical properties of the SAPH440 hot rolled steel sheet are mainly controlled to satisfy product specifications. Three mechanical properties including the yield strength, ultimate tensile strength, and elongation are measured and utilized in product classification. Based on these properties, the steel is classified into 3 grades: Class 1 (meets specification), Class 2 (moderate quality), and Class 3 (low) . However, various factors can affect the mechanical properties, leading to a long setup time for initial production runs. Therefore, this paper aims to improve the accuracy of these predictions by using machine learning algorithms. The results of experiments showed that the random forest algorithm had the best performance, with an accuracy of 70.0% and a macro average F-1 score of 70.0% . This more accurate prediction can reduce the initial setup time and save 37,000 USD per grade in trial run costs.  \nKeywords: Hot rolled steel sheet, mechanical properties, product classification, machine learning.  \nENGINEERING JOURNAL Volume 27 Issue 8 Received 15 February 2023  \nAccepted 21 August 2023 Published 31 August 2023 Online at [https://engj.org/](https://engj.org/)  \n[DOI:10.4186/ej.2023.27.8.51](DOI:10.4186/ej.2023.27.8.51)  \n1. Introduction  \nHot rolled steel is widely used in various industries such as construction, truck manufacturing, shelving, railroads, car parts, machinery, and container production due to its excellent weldability and mechanical properties [1] . Accurate control of mechanical properties such as elongation, yield strength, tensile strength, and impact energy is crucial in the steel industry. As a result, hot strip mills or hot rolling manufacturers focus on predicting and controlling these properties. Traditionally, prediction models have been obtained through simple or multiple linear regression. However, in recent years, machine learning (ML) algorithms have also been used for prediction purposes.  \nContinuous improvement in hot rolled steel sheet production has been a priority as new products often have specific mechanical property requirements based on their intended application. Currently, process control mainly focuses on the chemical composition of raw materials and rolling conditions. Four critical parameters are adjusted during the process control, including carbon equivalent, thickness, finishing temperature, and coiling temperature. The initial setup involves using scatter plots to determine the relationship between carbon equivalent and mechanical properties, and fine-tuning of process parameters to achieve optimal conditions. This process requires 20 to 25 initial trial runs (each a twenty-ton hot rolled coil) to meet customer specifications, leading to increased production costs and loss of production hours in the hot rolling process.  \nThe hot rolled steel process is illustrated in Fig. 1. The raw material for this process is a slab with a thickness of 220-250 mm. The process consists of the following 8 steps:  \n1) The slab is reheated in the reheating furnace to a temperature in the range of 1,200-1,250 °C,  \n2) The slab is rolled to reduce the thickness of the transfer bar [to about 30 mm. by](to about 30 mm. by) the roughing mill station,  \n3) The finishing rolling with transfer bar was passed to the finishing mill at a temperature in the range of 990- 1,056 °C,  \n4) Before entering the finishin","cbCaifKsNqjiYBu7","https://ap.wps.com/l/cbCaifKsNqjiYBu7","pdf",1028545,1,11,"English","en",105,"# Introduction\n## Mechanical property control in hot rolled steel\n## Hot rolled steel process overview\n## Motivation for machine learning prediction","[{\"question\":\"Which mechanical properties are used to classify SAPH440 hot rolled steel sheets?\",\"answer\":\"The study measures yield strength, ultimate tensile strength, and elongation, and uses them for product classification into three quality grades.\"},{\"question\":\"What are the three product classes used in the paper?\",\"answer\":\"Class 1 meets specification, Class 2 represents moderate quality, and Class 3 indicates low quality.\"},{\"question\":\"Why does the paper propose machine learning instead of traditional regression models?\",\"answer\":\"Machine learning is used because conventional linear or multiple linear regression predictions are inaccurate, which increases costs and setup time during initial production runs.\"}]","Predicting the Product Classification of Hot Rolled Steel Sheets Using Machine Learning Algorithms | 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mechanical properties are used to classify SAPH440 hot rolled steel sheets?","Question",{"text":75,"@type":76},"The study measures yield strength, ultimate tensile strength, and elongation, and uses them for product classification into three quality grades.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the three product classes used in the paper?",{"text":80,"@type":76},"Class 1 meets specification, Class 2 represents moderate quality, and Class 3 indicates low quality.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the paper propose machine learning instead of traditional regression models?",{"text":84,"@type":76},"Machine learning is used because conventional linear or multiple linear regression predictions are inaccurate, which increases costs and setup time during initial production 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