[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120651-en":3,"doc-seo-120651-105":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},120651,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning - Research","Industry 4.0 and Smart Manufacturing increasingly rely on artificial intelligence as manufacturing and assembly lines generate rapidly growing volumes of sensor, camera, vision-system, and barcode data. Human interpretation and processing of such product-type data becomes ineffective at scale. This paper studies semi-automatic assembly batch production product-change processes and evaluates how product type changes affect Overall Equipment Effectiveness (OEE), predicting future OEE values using supervised machine learning with decision trees.","[https://doi.org/10.3311/PPme.21320](https://doi.org/10.3311/PPme.21320)  \nCreative Commons Attribution b  \n|81  \nPeriodica Polytechnica Mechanical Engineering, 67(1), pp. 81–86, 2023  \nPredicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning  \nPéter Dobra1*, János Jósvai2  \n1 Doctoral School of Multidisciplinary Engineering Sciences, Széchenyi István University, Egyetem tér 1., H-9026 Győr, Hungary  \n2 Department of Vehicle Manufacturing, Széchenyi István University, Egyetem tér 1., H-9026 Győr, Hungary  \n* Corresponding author, e-mail: [dobra.peter@sze.hu](dobra.peter@sze.hu)  \nReceived: 11 October 2022, Accepted: 22 November 2022, Published online: 19 December 2022  \nAbstract  \nNowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage ofArtificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the volume of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is no longer possible effectively. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of product type changes on the Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree technology, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.  \nKeywords  \nOEE, machine learning, decision tree, assembly line, prediction  \n1 Introduction  \nIn order to plan the assembly sequence of products properly and to use the available resources in the right way, it is necessary to know the efficiency of the production units. It is advisable to predict the change in efficiency, which can move in either a negative or positive direction in addition to the stagnant situation, because these have an impact on the financial profitability of the factory. Higher efficiency requires less manpower, less manpower generates lower costs (e.g., variable costs, etc.) . Overall Equipment Effectiveness (OEE) is the most common efficiency Key Performance Indicator (KPI) in industrial practice today [1] . OEE, as a standard and best practice indicator, also includes downtime spent on product changes during manufacturing and assembly [2] . Despite Single Minute Exchange of Die (SMED), One Minute Exchange of Die (OMED) and One Touch Exchange of Die (OTED) used in day-to-day practice, the number and duration of changeover in batch-type assembly is still significant. These widely used methods analyze and optimize the process of product changes even during assembly operation in the case of tool changes. Therefore, it is important to predict future OEE values as accurately as possible.  \nThe paper is organized as follows. Section 2 focuses on the relevant scientific work regarding to machine learning and OEE. Following, Section 3 revealed decision tree technology as applied machine learning with industrial prediction example. Section 4 concludes the paper.  \n2 Machine learning and OEE  \nArtificial Intelligence (AI) encompasses machine learning which can support the predictive analytics in exploiting hidden correlations and make estimation [3]. Machine learning methods are used in industrial manufacturing applications, process characterization, fault detection, quality improvement, predictive maintenance, decision support system and production scheduling [4–7] . When industrial data is processed, data preparation and cleaning are essential.  \n2.1 Applied machine learning methods  \nThere are numerous machine learning methods among others regression, cl","cbCaiajbx9UzychD","https://ap.wps.com/l/cbCaiajbx9UzychD","pdf",879871,1,6,"English","en",105,"# Introduction\n# Machine learning and OEE\n## Applied machine learning methods\n## OEE and type change at the assembly lines\n# Conclusion","[{\"question\":\"What problem does the paper address in smart manufacturing?\",\"answer\":\"It addresses how rapidly increasing product-type data from sensors and vision systems cannot be effectively interpreted by humans, requiring data-mining support from machine learning.\"},{\"question\":\"How is the impact of product type changes on OEE predicted?\",\"answer\":\"The study uses supervised machine learning, specifically decision tree technology, to predict how product type changes influence OEE values.\"},{\"question\":\"What evidence is used to validate the results?\",\"answer\":\"The paper reports data and conclusions derived from a real industrial environment, and the obtained predictive performance is presented as practically proven.\"}]","Predicting the Impact of Product Type Changes on Overall Equipment Effectiveness Through Machine Learning - 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