{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T15:41:03Z","timestamp":1766504463161,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry for economic affairs and energy of Germany","award":["ZIM ZF4038207HB8"],"award-info":[{"award-number":["ZIM ZF4038207HB8"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation.<\/jats:p>","DOI":"10.3390\/s24051527","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T05:20:32Z","timestamp":1709011232000},"page":"1527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5348-0390","authenticated-orcid":false,"given":"Leroy","family":"Anozie","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bodo","family":"Fink","sequence":"additional","affiliation":[{"name":"WF Maschinenbau und Blechformtechnik GmbH & Co.KG, 48324 Sendenhorst, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7906-0038","authenticated-orcid":false,"given":"Christoph M.","family":"Friedrich","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany"},{"name":"Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, 45122 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Engels","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/S0924-0136(00)00508-2","article-title":"The influence of flow forming parameters and microstructure on the quality of a D6ac steel","volume":"103","author":"Jahazi","year":"2000","journal-title":"J. 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