[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128378-en":3,"doc-seo-128378-105":31,"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":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},128378,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning","Micro-machining relies on accurate tool wear monitoring to optimize machining parameters and reduce production costs. This research evaluates four machine learning approaches for predicting tool wear during milling, using in-process sensor measurements rather than interrupting machining. Data come from a face milling experiment on stainless steel (AISI 303), divided into stair-like stages, with a 3 mm tungsten carbide tool. Three sensor types—acoustic emission, accelerometers, and axis currents—are combined, and the best model achieves an F1-score of 73% across five wear classes using an Extra Trees classifier.","Tool Wear Monitoring Using Multi-Sensor Time Series and Machine Learning  \nJonathan Dreyer 1 ,2[0009−0007−8665−3094], Stefano Carrino 1[0000−0001−5171−6541], Hatem Ghorbel 1[0000−0001−5501−9807], and Paul Cotofrei2[0000−0002−4103−5467]  \n1 Haute Ecole Arc Ingénierie, University of Applied Sciences and Arts Western  \nSwitzerland (HES-SO), St. Imier, Switzerland {jonathan.dreyer,stefano.carrino,[hatem.ghorbel}@he-arc.ch](hatem.ghorbel}@he-arc.ch)  \n2 Information Management Institute, University of Neuchatel, Neuchatel, Switzerland  \n{jonathan.dreyer,[paul.cotofrei}@unine.ch](paul.cotofrei}@unine.ch)  \nAbstract. In the milling process of micro-machining, the optimization process is one of the keys to reduce production cost. By monitoring the tool wear and detecting when it is no longer acceptable, the machining process can be adjusted more accurately. This research explores four approaches using different machine learning models to predict machining tool wear during the milling process. The study is based on a dataset created with a face milling operation on stainless steel (AISI 303) round material. The machining is divided into a number of stairs and is performed with a 3mm tungsten carbide. Three different types of sensors are used to measure the wearing process, with acoustic emission, accelerometers and axis currents. The better approach achieved a f1-score of 73% on five classes with a Extra Trees Classifier.  \nKeywords: Tool wear monitoring · Milling machining · Multi-sensors time series · Machine learning.  \n1 Introduction  \nIn this article, we explore the possibility of improving machining performance by monitoring and evaluating the tool wear without interrupting the machining process. To be able to perform the tool wear detection, a mix method is used by implementing a state of the art methodology and compare it with other algorithms not used in papers. This can be achieved by measuring in-process with sensors, which help to improve the productivity and the control of the manufacturing [6] . The custom production implies a reduction in the demand for large volumes of production, and the needs of small-sized machine tools satisfy the requirements for micro manufacturing which enables the miniaturization of products and high accuracy [9] .  \nThe computer numerical control machining field includes several types of machining such as turning, milling and grinding. The first one is a machining technique where a cutting tool moves longitudinally while the workpiece rotateson itself. The second one is a machining process which uses a rotating cutter  \n2 J. Dreyer et al.  \ntool to remove material by moving the cutter tool into a workpiece. The last one is a machining process which uses a circular abrasive wheel to remove material from the surface while creating a smooth surface texture. In this study, we will focus on the milling process with a micro-milling machining.  \nThe low consumption of these micro-machining machine has been possible by scaling down the dimension [22] . This size reduction enables the reduction of the moving masses and a higher dynamic of motion [9] . The high-speed machining (HSM) is of benefit for micro machining and reduces the requirement of cooling during the milling, because a major part of heating is dissipated by chips. The HSM also helps to increase the productivity by speeding up the milling speed and the cutting speed [13] .  \nActually, the prediction of tool wear in micro milling context with sensors is challenging. To address this problem, we propose various approaches based on several machine learning classification models, including Convolutional Neural Networks (CNN) . The machine learning algorithms can enhance the tool wearing detection by analyzing large volumes of datasets and detecting hidden patterns. The objective is to study different machine learning techniques to improve the tool wear detection without interrupting the machining process, by measuring in-process with external sen","cbCaiiHz2IT9qW3w","https://ap.wps.com/l/cbCaiiHz2IT9qW3w","pdf",1241649,2,1,13,"English","en",105,"# Abstract\n# Introduction\n# State of the art\n## Tool wear sensing and prediction\n## Sensor data and machine learning approaches (dataset to model evaluation)\n# Results and discussion\n# Conclusion","[{\"question\":\"What problem does the study address in micro-milling?\",\"answer\":\"The study targets accurate tool wear detection during micro-milling without interrupting the machining process, aiming to support better optimization and cost reduction.\"},{\"question\":\"How is tool wear data collected in this research?\",\"answer\":\"The dataset is created from a face milling operation on stainless steel (AISI 303) and is split into multiple stair-like machining stages, measuring wear with acoustic emission, accelerometers, and axis currents.\"},{\"question\":\"Which machine learning approach performs best and what metric is reported?\",\"answer\":\"The best approach uses an Extra Trees Classifier and achieves an F1-score of 73% across five tool-wear classes.\"}]","Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning | 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problem does the study address in micro-milling?","Question",{"text":76,"@type":77},"The study targets accurate tool wear detection during micro-milling without interrupting the machining process, aiming to support better optimization and cost reduction.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is tool wear data collected in this research?",{"text":81,"@type":77},"The dataset is created from a face milling operation on stainless steel (AISI 303) and is split into multiple stair-like machining stages, measuring wear with acoustic emission, accelerometers, and axis currents.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning approach performs best and what metric is reported?",{"text":85,"@type":77},"The best approach uses an Extra Trees Classifier and achieves an F1-score of 73% across five tool-wear 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