[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123136-en":3,"doc-seo-123136-105":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},123136,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Quality prediction for milling processes - automated parametrization of an end-to-end machine learning pipeline","Machine tools increasingly generate internal data streams that enable edge-computing-based analytics for data-driven quality monitoring. However, effective data modeling and data handling require specialized human expertise and significant domain effort for data preparation, feature extraction, and model development. This paper proposes an automated parametrization approach for an end-to-end machine learning pipeline to identify and tune suitable feature extraction and selection methods. The approach automatically generates high-performing quality prediction models and is evaluated on four real-world milling datasets to confirm transferability.","Production Engineering (2023) 17:237–245  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1740-022-01173-4  \nQuality prediction for milling processes: automated parametrization of an end‑to‑end machine learning pipeline  \nAlexander Fertig1 · Christoph Preis1 · Matthias Weigold1  \nReceived: 13 September 2022 / Accepted: 21 November 2022 / Published online: 29 November 2022 © The Author(s) 2022  \nAbstract  \nThe application of modern edge computing solutions within machine tools increasingly empowers the recording and further processing of internal data streams. The datasets derived by contextualized data acquisition form the basis for the development of novel data-driven approaches for quality monitoring. Nevertheless, for the desired data-driven modeling and data handling, heavily specialized human resources are required. Additionally, domain experts are indispensable for adequate data preparation. To reduce the manual effort regarding data analysis and modeling this paper presents a new approach for an automated parametrization of an end-to-end machine learning pipeline (MLPL) to develop and select the best-performing quality prediction models for usage in machining production. This supports domain experts with a lack of specific knowledge of data science to develop well-performing models for machine learning-based quality prediction of milled workpieces. The results show that the presented algorithm enables the automated generation of data-driven models at high prediction performances to use for quality monitoring systems. The algorithm’s performance is tested and evaluated on four real-world datasets to ensure transferability.  \nKeywords Quality prediction · Machine learning · Milling · Machine tool data  \n1 Introduction  \nIn production with machine tools it is increasingly possible to record and process the internally processed data at high frequencies. By means of context-sensitive data acquisition [11], it is becoming feasible to automatically evaluate the obtained high-quality datasets and to utilize these for developing new data-driven approaches to process optimization and quality monitoring. Nevertheless, for the desired data-driven modeling and data handling, heavily specialized human resources are required [24]. Therefore, it is important to use the domain knowledge of experts for adequate data  \nChristoph Preis and Matthias Weigold contributed equally to this work.  \n* Alexander Fertig[a.fertig@ptw.tu-darmstadt.de](a.fertig@ptw.tu-darmstadt.de)  \nMatthias Weigold  \n[m.weigold@ptw.tu-darmstadt.de](m.weigold@ptw.tu-darmstadt.de)  \n1 Institute of Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, Hessen, Germany  \npreparation and to automate the subsequent development of ML-based predictive models as well as possible.  \nBased on the results from Fertig et al. [12] this paper presents a new approach for an automated parametrization of an end-to-end machine learning pipeline (MLPL) to develop quality prediction models for usage in machining production. The presented algorithm provides an individual identification and parameterization of appropriate methods for feature extraction and selection. The obtained findings are used to build models for the prediction of the manufactured workpiece quality. This enables domain experts with a lack of specific knowledge of data science to develop automatically predictive models based on an acquired production dataset. Further these models can be used in machine learning based quality monitoring systems.  \n2 State of the art  \nIn the field of quality prediction of machined workpieces, there are some research publications, which, as shown in Fertig et al. [12], can be divided into the three categories machining theory-based approaches, designed experiments  \napproaches, and artificial intelligence approaches according to Benardos and Vosniakos [4] . The artificial intelligence-based approaches co","cbCaiipjXoXORqUP","https://ap.wps.com/l/cbCaiipjXoXORqUP","pdf",909695,1,9,"English","en",105,"# Introduction\n# State of the art\n# Machine learning pipeline\n# Automated parametrization approach\n# Experimental evaluation\n# Conclusion","[{\"question\":\"What problem does the paper address in quality prediction for milling processes?\",\"answer\":\"It addresses the heavy manual effort and specialized expertise needed for data preparation, feature extraction, selection, and development of machine-learning models for predicting milled workpiece quality.\"},{\"question\":\"What is the proposed method in the paper?\",\"answer\":\"The paper introduces an algorithm that automates parametrization of an end-to-end machine learning pipeline, including individual identification of appropriate feature extraction and selection methods to build quality prediction models.\"},{\"question\":\"How is the method validated and how is its transferability assessed?\",\"answer\":\"The algorithm is tested and evaluated on four real-world datasets, focusing on high prediction performance and confirming transferability across datasets.\"}]","Quality prediction for milling processes - automated parametrization of an end-to-end machine learning pipeline | PDF",1785814806,23,{"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},"quality-prediction-for-milling-processes-automated-parametrization-of-an-end-to-end-machine-learning-pipeline","",{"@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/quality-prediction-for-milling-processes-automated-parametrization-of-an-end-to-end-machine-learning-pipeline/123136/",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},"What problem does the paper address in quality prediction for milling processes?","Question",{"text":75,"@type":76},"It addresses the heavy manual effort and specialized expertise needed for data preparation, feature extraction, selection, and development of machine-learning models for predicting milled workpiece quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the proposed method in the paper?",{"text":80,"@type":76},"The paper introduces an algorithm that automates parametrization of an end-to-end machine learning pipeline, including individual identification of appropriate feature extraction and selection methods to build quality prediction models.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the method validated and how is its transferability assessed?",{"text":84,"@type":76},"The algorithm is tested and evaluated on four real-world datasets, focusing on high prediction performance and confirming transferability across datasets.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]