[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122972-en":3,"doc-seo-122972-105":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},122972,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","In-situ identiﬁcation of material batches using machine learning for machining operations","Differences in machinability among batches of the same work material can degrade product quality and raise manufacturing costs when batch-specific effects are ignored. A smart in-situ service is proposed to identify material batches during subtractive manufacturing by using a supervised machine-learning model. The model analyzes machine control signals, with emphasis on torque data, for batch classification. Validation uses cutting experiments with five batches under varied cutting conditions, training and optimizing multiple classifiers to reach close to 90% accuracy, including strong results with a Support Vector Machine.","In-situ identiﬁcation of material batches using machine learning for machining operations  \nBenjamin Lutz1,2 · Dominik Kisskalt1 · Andreas Mayr1 · Daniel Regulin2 · Matteo Pantano2 · Jörg Franke1  \nReceived: 28 February 2020 / Accepted: 19 November 2020 / Published online: 26 December 2020 © The Author(s) 2020  \nAbstract  \nIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the inﬂuence of the material batch in process optimization models, the batch needs to be efﬁciently identiﬁed. Thus, a smart service is proposed for in-situ material batch identiﬁcation. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classiﬁcation. The proposed approach is validated by cutting experiments with ﬁve different batches of the same speciﬁed material at various cutting conditions. Using this data, multiple classiﬁcation models are trained and optimized. It is shown that the investigated batches can be correctly identiﬁed with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.  \nKeywords Process monitoring · Machine learning · Material batches · Machining  \nIntroduction  \nIn metalworking, the material properties of different batches might vary with signiﬁcant impact on the respective metalworking process. These observations can be explained by deviations in the material’s manufacturing procedure among different suppliers as well as among batches from batch production at a single supplier. In the material’s manufacturing process, various factors, such as the chemical composition, the fabrication procedure, or the heat treatment might deviateslightly within their tolerances. These effects lead to small changes of the material’s properties, such as microstructure, grain size, and hardness, which directly impact a material’s machinability (Schneider 2002) .  \nIn their study, Goppold et al. (2018) investigate batches of metal sheets from multiple vendors, speciﬁed as the same material, ﬁnding deviations in their chemical compositions.  \nB Benjamin Lutz [benjamin.lutz@faps.fau.de](benjamin.lutz@faps.fau.de)  \n1 Institute for Factory Automation and Production Systems, Friedrich-Alexander-University Erlangen-Nürnberg, Egerlandstraße 7-9, 91058 Erlangen, Germany  \n2 Siemens AG, Otto-Hahn-Ring 6, 81739 Munich, Germany  \nThese deviations strongly inﬂuence the laser cutting process but can be compensated by batch-speciﬁc adaption of cutting parameters (Goppold et al. 2018) . Similarly, it is found that during the hardening process of gear pinions, small ﬂuctuations of the copper content within the material’s tolerance among different batches signiﬁcantly inﬂuence their hardenability (Šuchmann and Martinek 2014) .  \nIn subtractive manufacturing processes, different process behaviors among batches of the same speciﬁed material can be observed as well (Jemielniak and Kosmol 1995) . While one batch of the raw material might be easy to machine with a given set of cutting parameters, a different batch might show unstable machining, increased tool wear, or even tool breakage. Thus, when optimizing a subtractive manufacturing process with regard to the machinability of one material batch, non-ideal behavior can be expected when machining a batch with different machinability, using the same parameters found before. However, as the material deviations resulting in these differences in machinability are within the given tolerance of the speciﬁed material, they cannot be distinguished without further investigation. Thus, without additional knowledge, each produced material batch from ea","cbCaitPUkkN2FaYU","https://ap.wps.com/l/cbCaitPUkkN2FaYU","pdf",966546,1,11,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"为什么需要对材料批次进行在制识别？\",\"answer\":\"同一材料的不同批次在可加工性上可能存在差异。若不考虑这些偏差，可能降低产品质量并增加制造成本，因此需要在工艺优化模型中高效识别批次。\"},{\"question\":\"该方法如何利用机器学习完成批次分类？\",\"answer\":\"方法使用监督式机器学习模型分析机床控制信号，尤其是扭矩数据，用于对不同材料批次进行分类。\"},{\"question\":\"实验是如何验证该批次识别方案的？\",\"answer\":\"通过切削实验对同一种材料的五个批次在不同切削条件下进行测试。基于实验数据训练并优化多个分类模型，结果表明批次识别准确率接近90%。\"}]","In-situ identiﬁcation of material batches using machine learning for machining operations | PDF",1785813964,28,{"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},"in-situ-identification-of-material-batches-using-machine-learning-for-machining-operations","",{"@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/in-situ-identification-of-material-batches-using-machine-learning-for-machining-operations/122972/",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","2026-08-04",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},"为什么需要对材料批次进行在制识别？","Question",{"text":75,"@type":76},"同一材料的不同批次在可加工性上可能存在差异。若不考虑这些偏差，可能降低产品质量并增加制造成本，因此需要在工艺优化模型中高效识别批次。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"该方法如何利用机器学习完成批次分类？",{"text":80,"@type":76},"方法使用监督式机器学习模型分析机床控制信号，尤其是扭矩数据，用于对不同材料批次进行分类。",{"name":82,"@type":73,"acceptedAnswer":83},"实验是如何验证该批次识别方案的？",{"text":84,"@type":76},"通过切削实验对同一种材料的五个批次在不同切削条件下进行测试。基于实验数据训练并优化多个分类模型，结果表明批次识别准确率接近90%。","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"]