[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126014-en":3,"doc-seo-126014-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126014,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","PREDICTION OF MILLING MACHINE FAILURES USING MACHINE LEARNING MODEL","This study developed a random forest machine learning model for predicting milling machine failure using five input parameters: air temperature, process temperature, rotational speed, torque, and tool wear. A random forest classifier splits the dataset into 97% for training and 3% for testing, producing predictions via majority voting. Results show strong performance with accuracy, precision, recall, and F1 values of 0.9853, 0.7129, 0.8276, and 0.7660. Confusion matrix outcomes quantify correct and incorrect predictions for both no-failure and failure classes. The approach supports preventive actions such as maintenance schedule adjustment and timely repairs, reducing unplanned downtime.","LAUTECH Journal of Engineering and Technology 18 (1) 2024: 117-123  \nPREDICTION OF MILLING MACHINE FAILURES USING MACHINE  \nLEARNING MODEL  \n1*Olorunfemi B. J., 1Obisesan O. M., 2Oginni O. T. and 1Olumoroti I. A.  \n1Department of Mechanical Engineering, Federal University Oye-Ekiti, Nigeria. 2Department of Mechanical Engineering, Bamidele Olumilua University of Education, Science and Technology,  \nIkere-Ekiti, Ekiti State, Nigeria.  \nCorresponding author’s email: [oginni.olarewaju@bouesti.edu.ng](oginni.olarewaju@bouesti.edu.ng)  \nABSTRACT  \nThis study developed a random forest machine learning model for predicting milling machine failure using five input parameters, which include air temperature, process temperature, rotational speed, torque, and tool wear. The model utilized a random forest classifier to split the data into 97% for training and 3% for testing, generating final results based on majority votes. The random forest model effectively predicts milling machine failure with high precision and accuracy, as demonstrated by its performance metrics, including accuracy, precision, recall, and F1 values of 0.9853, 0.7129, 0.8276, and 0.7660, respectively. The confusion matrix analysis shows the model correctly predicted 2884 no machine failures as true positives (TP) out of 2899 no machine failure targets and 15 no machine failures as false positives (FP). In addition, the model predicted 72 machine failure targets as true negatives (TN) out of 101 machine failure targets, leaving 29 as false negatives (FN). The model aids in predicting machine failure likelihood and implementing preventative measures such as pre-emptive investigation, maintenance schedule adjustments, and repairs. This improves the efficiency and productivity of milling machine operations, reducing unplanned downtime.  \nKeywords: Accuracy, Downtime, Performance, Positive, Operation  \nINTRODUCTION  \nMaintenance is an integral part of the production strategy for the overall success of an organization. It is expected that equipment of this century should be more computerized and reliable, in addition to being vastly more complex. Further computerization of equipment would significantly increase the importance of software maintenance, approaching, if not equal to, hardware maintenance (Gala et al., 2016; Achour et al., 2017) . This century sees more emphasis on maintenance with respect to such areas as the human factor, quality, safety, and costeffectiveness. New thinking and new strategies are required to realize potential benefits and turn them into profitability. All in all, profitable operations will be the ones that have employed modern thinking to evolve an equipment management strategy that takes effective advantage of new  \ninformation, technology, and methods (Herath et al., 2021) .  \nPredictive maintenance (PM) is a method to monitor the status of machinery to prevent expensive failures from occurring and to perform maintenance when it is required. From visual inspection, which is the oldest method, PM has evolved to automated methods that use advanced signal processing techniques (Benmouiza and Cheknane, 2013) . Traditionally, maintenance creates a trade-off situation in which one must choose between maximizing the useful life of a part at the risk of machine downtime (run-to-failure) and maximizing up-time through early replacement of potentially good parts (time-based PM), which has been demonstrated to be ineffective for most equipment components considered flawed and unreliable in  \nrecent years (Rahimikhoob, 2010; Chen and Li, 2014) . PM breaks these tradeoffs by empowering companies to minimize maintenance and forecasting it ahead of time. Adoption of PM allows for the maximization of the useful life of assets by reducing the frequency of maintenance activities, avoiding unplanned breakdowns, and eliminating unnecessary preventive maintenance. This results insubstantial time and cost savings and higher system reliability. To implement a PM ap","cbCaifrw6B9tw54k","https://ap.wps.com/l/cbCaifrw6B9tw54k","pdf",1006715,5,1,7,"English","en",105,"# Abstract\n# Introduction\n## Predictive maintenance and condition monitoring\n## Trade-offs in maintenance strategies\n## Role of IoT and machine learning","[{\"question\":\"Which inputs are used to predict milling machine failures?\",\"answer\":\"The model uses five parameters: air temperature, process temperature, rotational speed, torque, and tool wear.\"},{\"question\":\"How are the training and testing datasets prepared in the random forest model?\",\"answer\":\"Data are split into 97% for training and 3% for testing, and final decisions are made using majority votes.\"},{\"question\":\"What performance does the model achieve for failure prediction?\",\"answer\":\"Reported metrics include accuracy 0.9853, precision 0.7129, recall 0.8276, and F1 0.7660.\"}]","PREDICTION OF MILLING MACHINE FAILURES USING MACHINE LEARNING MODEL | PDF",1785902561,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"prediction-of-milling-machine-failures-using-machine-learning-model","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/prediction-of-milling-machine-failures-using-machine-learning-model/126014/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which inputs are used to predict milling machine failures?","Question",{"text":77,"@type":78},"The model uses five parameters: air temperature, process temperature, rotational speed, torque, and tool wear.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How are the training and testing datasets prepared in the random forest model?",{"text":82,"@type":78},"Data are split into 97% for training and 3% for testing, and final decisions are made using majority votes.",{"name":84,"@type":75,"acceptedAnswer":85},"What performance does the model achieve for failure prediction?",{"text":86,"@type":78},"Reported metrics include accuracy 0.9853, precision 0.7129, recall 0.8276, and F1 0.7660.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]