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It focuses on predicting the location of milled holes in the next bumper beam, where hole positions must meet strict tolerance limits. A neural network, an LSTM, and random forest models are trained on historical measurements. Experiments show similar predictive capability with slight dominance for LSTM and random forest, enabling early detection of tolerance violations and process improvement, while some holes remain difficult due to data information limitations.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-machine-learning-prediction-models-for-quality-control-a-case-study-from-the-automotive-industry/291024/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-machine-learning-prediction-models-for-quality-control-a-case-study-from-the-automotive-industry/291024.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What manufacturing quality problem does the study address?","Question",{"text":112,"@type":113},"The study addresses quality control of bumper beams in the automotive industry by predicting the location of milled holes that must satisfy strict tolerance limits.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning models are implemented in the case study?",{"text":117,"@type":113},"The paper implements a standard neural network, an LSTM (long short-term memory) network, and a random forest algorithm, trained on historical time series data.",{"name":119,"@type":110,"acceptedAnswer":120},"How do the experimental results support the effectiveness of the approach?",{"text":121,"@type":113},"The experiments indicate all models have similar predictive capabilities, with a slight dominance for LSTM and random forest, allowing some holes to be predicted with good quality and tolerance violations to be detected earlier for better process control.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291024,1790300927,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","Computational Management Science (2023) 20:14 [https://doi.org/10.1007/s10287-023-00448-0](https://doi.org/10.1007/s10287-023-00448-0)  \nORIGINAL PAPER  \nUsing machine learning prediction models for quality control: a case study from the automotive industry  \nMohamed Kais Msakni1 · Anders Risan1 · Peter Schütz1  \nReceived: 3 June 2022 / Accepted: 1 March 2023 / Published online: 16 March 2023 © The Author(s) 2023  \nAbstract  \nThis paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.  \nKeywords Neural network · Random forest · Quality control · Manufacturing Mathematics Subject Classification 68T07 · 68T09 · 62H10  \nAnders Risan and Peter Schütz contributed equally to this work.  \n* Mohamed Kais Msakni[kais.msakni@ntnu.no](kais.msakni@ntnu.no)  \nAnders Risan  \n[andris@stud.ntnu.no](andris@stud.ntnu.no)  \nPeter Schütz  \n[peter.schutz@ntnu.no](peter.schutz@ntnu.no)  \n1 Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Torgarden, 7491 Trondheim, Norway  \n1 Introduction  \nThe emergence of the fourth industrial revolution, Industry 4.0, is primarily driven by advancements in information, communication, and intelligence technologies that can improve production flexibility, efficiency, and productivity in industry (Ibarra et al. 2018). While the definition of Industry 4.0 is broad, there are several key concepts associated with it, such as smart factories, the Internet of Things (IoT), cloud computing, cyber-physical systems, and Big Data Manufacturing (Santos et al. 2017) . IoT technology enables to connect manufacturing resources, like sensors, machines, and other equipment, enabling interconnection between components and reducing human intervention. This also allows real-time, high-accuracy monitoring of product quality, equipment, and production processes. Real-time data flow can help identify problems early on and provide better visibility into the flow of materials and products. In addition, cloud computing makes data available to other systems with powerful resources, such as servers, storage, and software (Lee and Lee 2015) . As many manufacturers have large amounts of data that go unused, cloud computing is seen as a way to transform the traditional manufacturing business model into an effective collaboration, helping manufacturers align business strategies and product innovation and create smart networks (Xu 2012) . The amount of data collected from various systems and objects is growing at an exponential rate and is commonly referred to as Big Data. This concept is characterized by high dimensionality and high complexity due to the variety of formats, semantics, and quality of sensors and processes generating the data (Wuest et al. 2016) . As a key concept in smart factories, Big Data can impact Industry 4.0 in three ways: enabling self-diagnosis, forecasting, and control (Tao et al. 2","cbCaigMIr4p3CqD4","https://ap.wps.com/l/cbCaigMIr4p3CqD4","pdf",2919852,28,"English","# Abstract\n## Introduction","[{\"question\":\"What manufacturing quality problem does the study address?\",\"answer\":\"The study addresses quality control of bumper beams in the automotive industry by predicting the location of milled holes that must satisfy strict tolerance limits.\"},{\"question\":\"Which machine learning models are implemented in the case study?\",\"answer\":\"The paper implements a standard neural network, an LSTM (long short-term memory) network, and a random forest algorithm, trained on historical time series data.\"},{\"question\":\"How do the experimental results support the effectiveness of the approach?\",\"answer\":\"The experiments indicate all models have similar predictive capabilities, with a slight dominance for LSTM and random forest, allowing some holes to be predicted with good quality and tolerance violations to be detected earlier for better process control.\"}]","Using machine learning prediction models for quality control: a case study from the automotive industry | PDF",1789643201,71]