{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T21:40:32Z","timestamp":1723326032288},"reference-count":13,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.<\/jats:p>","DOI":"10.3390\/data7120175","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T07:18:48Z","timestamp":1670397528000},"page":"175","source":"Crossref","is-referenced-by-count":1,"title":["Convolutional-Based Encoder\u2013Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5"],"prefix":"10.3390","volume":"7","author":[{"given":"Tobias","family":"Schlagenhauf","sequence":"first","affiliation":[{"name":"WBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstra\u00dfe 12, 76131 Karlsruhe, Germany"}]},{"given":"Jan","family":"Wolf","sequence":"additional","affiliation":[{"name":"WBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstra\u00dfe 12, 76131 Karlsruhe, Germany"}]},{"given":"Alexander","family":"Puchta","sequence":"additional","affiliation":[{"name":"WBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstra\u00dfe 12, 76131 Karlsruhe, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Tziolas, T., Papageorgiou, K., Theodosiou, T., Papageorgiou, E., Mastos, T., and Papadopoulos, A. (2022). Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset. Eng. Proc., 18.","key":"ref_1","DOI":"10.3390\/engproc2022018023"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.promfg.2020.05.059","article-title":"Anomaly detection in milling tools using acoustic signals and generative adversarial networks","volume":"48","author":"Cooper","year":"2020","journal-title":"Procedia Manuf."},{"doi-asserted-by":"crossref","unstructured":"Guang, L., Fu, Y., Chen, D., Shi, L., and Zhou, J. (2020). Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals. Sensors, 20.","key":"ref_3","DOI":"10.3390\/s20174896"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1177\/0263092316644090","article-title":"Fault diagnosis studies of face milling cutter using machine learning approach","volume":"35","author":"Madhusudana","year":"2016","journal-title":"J. Low Freq. Noise. Vib. Act. 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