[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121325-en":3,"doc-seo-121325-105":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},121325,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Data-Driven Welding Quality Assessment - Leveraging IoT and Machine Learning in Industrial Practice","The paper investigates how data analytics and machine learning improve welding quality in Tecnomulipast srl, an Italian SME producing food machine components. The firm mechanized its laser welding using an IoT-enabled photographic control system supported by regional funding for digital transformation. An internal data analytics infrastructure enables predictive modeling. Using 1,000 observations with process variables such as laser power, pulse time, gas parameters, temperature, and penetration depth, supervised learning models predict weld bead width (LC). Model evaluation uses MSE, RMSE, MAE, MAPE, and R², while ensemble methods and feature importance identify key drivers like laser power, gas flow, and trajectory accuracy.","Munich Personal RePEc Archive  \nData-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice  \nMagaletti, Nicola and Notarnicola, Valeria and Di Molfetta, Mauro and Mariani, Stefano and Leogrande, Angelo  \nLUM Enterprise srl, LUM Enterprise srl, LUM Enterprise srl, LUM Enterprise srl, LUM Enterprise srl  \n24 April 2025  \nOnline at [https://mpra. ub. uni-muenchen. de/124548/](https://mpra. ub. uni-muenchen. de/124548/)  \n[MPRA Paper No. 124548](MPRA Paper No. 124548) , [posted 30 Apr 2025 13:47 UTC](posted 30 Apr 2025 13:47 UTC)  \nData-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice  \nNicola Magaletti, LUM Enterprise s.r.l., Casamassima, Italy, [magaletti@lumenterprise.it](magaletti@lumenterprise.it)[ ](magaletti@lumenterprise.it)Valeria Notarnicola, LUM Enterprise s.r.l., Casamassima, Italy, [notarnicola@lumenterprise.it](notarnicola@lumenterprise.it)[ ](notarnicola@lumenterprise.it)Mauro di Molfetta, LUM Enterprise s.r.l., Casamassima, Italy, [dimolfetta@lumenterprise.it](dimolfetta@lumenterprise.it)[ ](dimolfetta@lumenterprise.it)Stefano Mariani, LUM Enteprise s.r.l., Casamassima, Italy, [ste.mariani.23@gmail.com](ste.mariani.23@gmail.com)[ ](ste.mariani.23@gmail.com)Angelo Leogrande, LUM Enterprise s.r.l., Casamassima, Italy, [leogrande.cultore@lum.it](leogrande.cultore@lum.it)  \nThe paper investigates the deployment of data analytics and machine learning to improve welding quality in Tecnomulipast srl, a small-to-medium sized manufacturing firm located in Puglia, Italy. The firm produces food machine components and more recently mechanized its laser welding process with the introduction of an IoT-enabled system integrating photographic control. The investment, underwritten by the Apulia Region under PIA (Programmi Integrati di Agevolazione) allowed Tecnomulipast to not only mechanize its production line but also embark upon wider digital transformation. This involved the creation of internal data analytics infrastructures that have the capability to underpin machine learning and artificial intelligence applications. This paper addresses a prediction of weld bead width (LC) with a dataset of 1,000 observations. Input variables are laser power (PL), pulse time (DI), frequency (FI), beam diameter (DF), focal position (PF), travel speed (VE), trajectory accuracy (TR), laser angle (AN), gas flow (FG), gas purity (PG), ambient temperature (TE), and penetration depth (PE) . The parameters were exploited to build and validate some supervised machine learning algorithms like Decision Trees, Random Forest, K-Nearest Neighbors, Support Vector Machines, Neural Networks, and Linear Regression. The performance of the models was measured by MSE, RMSE, MAE, MAPE, and R². Ensemble methods like Random Forest and Boosting performed the highest. Feature importance analysis determined that laser power, gas flow, and trajectory accuracy are the key variables. This project showcases the manner in which Tecnomulipast has benefited from public investment to introduce digital transformation and adopt data-driven strategies within Industry 4.0.  \nKeywords: Tecnomulipast, laser welding, machine learning, digital transformation, Industry 4.0.  \n1. Introduction  \nThe increasing adoption of artificial intelligence and data analytics within production operations represents a paradigm shift for how production efficiency and quality are tracked and optimized. Although big industries have driven such developments, their application within small-to-medium enterprises (SMEs) of traditional industries like food machinery production is sparse and underresearched. This research bridges that gap by examining how a Southern Italian SME, Tecnomulipast srl, adopted a data-driven approach for predicting and controlling weld quality within an IoT-enabled laser welding machine. The main research question for this research is: To what extent can machine learning models effectively predic","cbCaikcRrQInUrpO","https://ap.wps.com/l/cbCaikcRrQInUrpO","pdf",1001266,1,13,"English","en",105,"# Introduction\n## Literature Review\n## Data and Variables\n## Machine Learning Regression Results\n## Network Analysis Results\n## Conclusions","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to predict weld bead width (LC) using real-time process information from an IoT-enabled laser welding system to improve welding quality.\"},{\"question\":\"What data and input variables are used for prediction?\",\"answer\":\"It uses a dataset of 1,000 observations with laser power, pulse time, frequency, beam diameter, focal position, travel speed, trajectory accuracy, laser angle, gas flow, gas purity, ambient temperature, and penetration depth.\"},{\"question\":\"Which machine learning methods perform best and why?\",\"answer\":\"Ensemble methods such as Random Forest and Boosting achieve the highest performance based on metrics including MSE, RMSE, MAE, MAPE, and R².\"}]","Data-Driven Welding Quality Assessment - Leveraging IoT and Machine Learning in Industrial Practice | PDF",1785735072,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"data-driven-welding-quality-assessment-leveraging-iot-and-machine-learning-in-industrial-practice","",{"@graph":36,"@context":85},[37,54,68],{"@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/data-driven-welding-quality-assessment-leveraging-iot-and-machine-learning-in-industrial-practice/121325/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"The study aims to predict weld bead width (LC) using real-time process information from an IoT-enabled laser welding system to improve welding quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and input variables are used for prediction?",{"text":80,"@type":76},"It uses a dataset of 1,000 observations with laser power, pulse time, frequency, beam diameter, focal position, travel speed, trajectory accuracy, laser angle, gas flow, gas purity, ambient temperature, and penetration depth.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods perform best and why?",{"text":84,"@type":76},"Ensemble methods such as Random Forest and Boosting achieve the highest performance based on metrics including MSE, RMSE, MAE, MAPE, and R².","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]