[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126600-en":3,"doc-seo-126600-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},126600,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models - Research Report","Steel surface texture control is essential in strip steel galvanizing and temper rolling to satisfy customer requirements. Traditional stylus measurements are slow and non-representative, using only limited samples and preventing mid-coil adjustments. This work develops a faster, non-contact laser-based online sensing approach and improves the mapping from raw laser reflection data to an accurate Ra surface roughness metric. By comparing deep learning and non-deep learning models with a close-form transformation, the study evaluates methods for better surface texture control in temper strip manufacturing and supports real-time process feedback and closed-loop control potential.","arXiv :2307 .03723v 1 [ cs .LG] 6 Jul 2023  \nSteel Surface Roughness Parameter Calculations Using Lasers  \nand Machine Learning Models  \nAlex Milne 1* and Xianghua Xie 1  \n1 Department of Computer Science, Swansea University, Swansea, UK.  \n*Corresponding author(s). E-mail(s): [alexander.milne@hotmail.co.uk](alexander.milne@hotmail.co.uk) ; Contributing [authors:](authors: X.Xie@swansea.ac.uk)[ X.Xie@swansea.ac.uk](authors: X.Xie@swansea.ac.uk);  \nAbstract  \nControl of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques o􀀋er non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their e􀀋ective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into signi􀀌cantly a more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing.  \nKeywords: Surface roughness, Machine Learning, Temper rolling, on-line measurement, Time Extrinsic Series Regression (TSER)  \n1 Introduction  \nTemper rolling is a critical process in strip steel production, which involves cold-rolling the steel to improve its mechanical and surface properties. For high-value products like automotive paneling, meeting customer demands extends beyond achieving favorable mechanical properties and necessitates speci􀀌c surface properties to facilitate better press performance [14] and ensure paint quality [2] .  \nLine operators have the ability to modify various parameters, including roll selection, roll force, and speed, in order to in􀀍uence the steel's microstructure and meet customer requirements. The selection of rolls in the temper mill plays a  \ncrucial role in imparting surface texture onto the steel. The rolls have a surface texture imparted by electric discharged texturing (EDT), transferring this surface texture onto the steel, while other parameters a􀀋ect how this texture transfer happens. The process for feedback to be given to the line operators relating to if the surface texture has been applied to customer speci􀀌cations is slow. Additionally, surface measurements are taken post-production manually using a stylus device, and only on a small section at the head or tail of the coil. This causes two main issues. a) the samples may not be representative of the entire coil surface [7] . b) the feedback does not allow mid-coil process adjustments. This can result in  \nproducing coils that do not meet customer requirements. Therefore, the slow feedback provided is only bene􀀌cial for subsequent coils, leaving the inadequate coil with a reduced value and forcing re-production of the product, potentially multiple times in a slow iterative process until the customer requirements are met. Given the prevalence of just-in-time manufacturing [27], the delayed feedback-driven re-production can cause downstream delays and the possibility of costly line stoppages for customers if replacement steel is not manufactured and delivered promptly.  \nAccess to fast monitoring on-line allows operators to perform real-time adjustments for line parameters and provides measurements for the entire surface. It also opens the door for real-time closed-loop high-precision control systems to automate the temper mill parameters to create steel for customer requirements. Cheri et al. [8] propose an on-line intelligent control met","cbCaic7rOLSE6gFX","https://ap.wps.com/l/cbCaic7rOLSE6gFX","pdf",6978029,1,18,"English","en",105,"# Introduction\n## Temper rolling and the need for accurate surface texture\n## Limitations of stylus-based post-production measurement\n## Online laser measurement and surface profile estimation\n## Motivation for machine learning-based transformation\n# Methods and model comparison\n## Deep learning vs non-deep learning approaches","[{\"question\":\"Why is controlling steel surface roughness during temper rolling important?\",\"answer\":\"Customer requirements in galvanizing and temper rolling depend on surface texture for downstream press performance and paint quality. Meeting these needs requires accurate roughness control.\"},{\"question\":\"What problems do traditional stylus measurements create?\",\"answer\":\"They are slow and non-contact replacement is not immediate, so operators cannot adjust parameters mid-coil. They also measure only small coil sections, which may not represent the whole strip.\"},{\"question\":\"How does the laser-based online approach support improved manufacturing control?\",\"answer\":\"It captures scattered light intensities from the steel surface to estimate angles, which are then used to compute surface gradients, profiles, and statistics. This enables faster, full-strip monitoring for real-time adjustments and potential closed-loop control.\"}]","Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models - Research Report | PDF",1785933638,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"steel-surface-roughness-parameter-calculations-using-lasers-and-machine-learning-models-research-report","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/steel-surface-roughness-parameter-calculations-using-lasers-and-machine-learning-models-research-report/126600/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is controlling steel surface roughness during temper rolling important?","Question",{"text":76,"@type":77},"Customer requirements in galvanizing and temper rolling depend on surface texture for downstream press performance and paint quality. Meeting these needs requires accurate roughness control.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problems do traditional stylus measurements create?",{"text":81,"@type":77},"They are slow and non-contact replacement is not immediate, so operators cannot adjust parameters mid-coil. They also measure only small coil sections, which may not represent the whole strip.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the laser-based online approach support improved manufacturing control?",{"text":85,"@type":77},"It captures scattered light intensities from the steel surface to estimate angles, which are then used to compute surface gradients, profiles, and statistics. This enables faster, full-strip monitoring for real-time adjustments and potential closed-loop control.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]