{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T19:04:14Z","timestamp":1769972654723,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"EU DAT4Zero","doi-asserted-by":"publisher","award":["958363"],"award-info":[{"award-number":["958363"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The complexity of products increases considerably, and key functions can often only be realized by using high-precision components. Microgears have a particularly complex geometry and thus the manufacturing requirements often reach technological limits. Their geometric deviations are relatively large in comparison to the small component size and thus have a major impact on the functionality in terms of generating unwanted noise and vibrations in the final product. There are still no readily available production-integrated measuring methods that enable quality control of all produced microgears. Consequently, many manufacturers are not able to measure any geometric gear parameters according to standards such as DIN ISO 21771. If at all, only samples are measured, as this is only possible by means of specialized, sensitive, and cost-intensive tactile or optical measuring technologies. In a novel approach, this paper examines the integration of an acoustic emission sensor into the hobbing process of microgears in order to predict process parameters as well as geometric and functional features of the produced gears. In terms of process parameters, radial feed and tool tumble are investigated, whereas the total profile deviation is used as a representative geometric variable and the overall transmission error as a functional variable. The approach is experimentally validated by means of the design of experiments. Furthermore, different approaches for feature extraction from time-continuous sensor data and different machine-learning approaches for predicting process and geometry parameters are compared with each other and tested for suitability. It is shown that structure-borne sound, in combination with supervised machine learning and data analysis, is suitable for inprocess monitoring of microgear hobbing processes.<\/jats:p>","DOI":"10.3390\/a16040183","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T04:37:15Z","timestamp":1679978235000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["In-Process Monitoring of Hobbing Process Using an Acoustic Emission Sensor and Supervised Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Vivian","family":"Schiller","sequence":"first","affiliation":[{"name":"wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]},{"given":"Sandra","family":"Klaus","sequence":"additional","affiliation":[{"name":"wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]},{"given":"Ali","family":"Bilen","sequence":"additional","affiliation":[{"name":"wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0481-4613","authenticated-orcid":false,"given":"Gisela","family":"Lanza","sequence":"additional","affiliation":[{"name":"wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","unstructured":"Arndt, O., and Hennchen, S. (2011). Wertsch\u00f6pfungs-und Wettbewerberanalyse f\u00fcr den Spitzencluster MicroTEC S\u00fcdwest. Prognos AG Bremen D\u00fcsseldorf, 8."},{"key":"ref_2","first-page":"30","article-title":"Mikroantriebe fur prazise Positionieranwendungen","volume":"42","author":"Slatter","year":"2003","journal-title":"Antriebstechnik"},{"key":"ref_3","unstructured":"(2009). VDI 2731: Microgears\u2014Basic Principles Part 1, VDI Verein Deutscher Ingenieure e.V., Beuth."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Klocke, F., and Brecher, C. (2016). Zahnrad-und Getriebetechnik, Carl Hanser Verlag GmbH & Co. KG.","DOI":"10.3139\/9783446431409.fm"},{"key":"ref_5","unstructured":"Gravel, G. (2009). Kongress zur Getriebeproduktion, Congress Centrum W\u00fcrzburg, FVA GmbH."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1524\/teme.2009.0925","article-title":"Koordinatenmesstechnik als Schl\u00fcsseltechnologie der Fertigungsmesstechnik Coordinate Metrology as a Key Technology in Production Measurement","volume":"76","author":"Hirsch","year":"2009","journal-title":"tm-Technisches Messen"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0141-6359(84)90072-2","article-title":"Effect of hob wear on the sounds emitted in the gear hobbing process","volume":"6","year":"1984","journal-title":"Precis. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7349","DOI":"10.1109\/TIE.2021.3102443","article-title":"Deep Spatial-Temporal Feature Extraction and Lightweight Feature Fusion for Tool Condition Monitoring","volume":"69","author":"Li","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Eversheim, W., Pfeifer, T., and Weck, M. (2006). 100 Jahre Produktionstechni, Springer.","DOI":"10.1007\/3-540-33316-9"},{"key":"ref_10","first-page":"161","article-title":"Feasibility of tool condition monitoring on micro-milling using current signals","volume":"14","author":"Ogedengbe","year":"2011","journal-title":"AU JT"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00170-013-5258-5","article-title":"Sparse representation and its applications in micro-milling condition monitoring: Noise separation and tool condition monitoring","volume":"70","author":"Zhu","year":"2014","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/10910344.2013.780541","article-title":"In-Process Tool Condition Monitoring Using Acoustic Emission Sensor in Microendmilling","volume":"17","author":"Prakash","year":"2013","journal-title":"Mach. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"219","DOI":"10.4028\/www.scientific.net\/AMM.508.219","article-title":"Micro-Vibration Mechanism of Micro-Gears Fault Diagnosis Based on Fault Characteristics and Differential Evolution Wavelet Neural Networks","volume":"508","author":"Su","year":"2014","journal-title":"AMM"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"M\u00f6ser, M., and Kropp, W. (2010). K\u00f6rperschall, Springer.","DOI":"10.1007\/978-3-540-49048-7"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"M\u00f6ser, M. (2018). K\u00f6rperschall-Messtechnik, Springer.","DOI":"10.1007\/978-3-662-56621-3"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1016\/j.ijmachtools.2008.01.011","article-title":"A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations","volume":"48","author":"Marinescu","year":"2008","journal-title":"Int. J. Machine Tools Manuf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/0890-6955(92)90040-N","article-title":"Tool monitoring of small drills with acoustic emission","volume":"32","author":"Kutzner","year":"1992","journal-title":"Int. J. Machine Tools Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sturm, A., and F\u00f6rster, R. (1990). Maschinen-und Anlagendiagnostik, Springer.","DOI":"10.1007\/978-3-322-99814-9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1007\/s00170-012-4106-3","article-title":"A two-step feature selection method for monitoring tool wear and its application to the coroning process","volume":"64","author":"Yum","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.triboint.2015.07.024","article-title":"A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission","volume":"92","author":"Maia","year":"2015","journal-title":"Tribol. Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s00287-015-0879-8","article-title":"Integration von IT in die Automatisierungstechnik","volume":"38","author":"Papenfort","year":"2015","journal-title":"Informatik Spektrum"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103","DOI":"10.20855\/ijav.2016.21.1400","article-title":"An Experimental Study on Gear Diagnosis by Using Acoustic Emission Technique","volume":"21","author":"Erkaya","year":"2016","journal-title":"IJAV"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jmapro.2017.04.003","article-title":"Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy","volume":"27","author":"Wang","year":"2017","journal-title":"J. Manuf. Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0041-624X(97)00052-8","article-title":"Application of acoustic emission sensor for monitoring machining processes","volume":"36","author":"Inasaki","year":"1998","journal-title":"Ultrasonics"},{"key":"ref_25","unstructured":"Wantzen, K. (2020). Methode zur Entwicklung Merkmalsbasierter Zustands\u00fcberwachungssysteme Mittels der K\u00f6rperchallmesstechnik. [Doctoral Dissertation, Karlsruher Institut f\u00fcr Technologie]."},{"key":"ref_26","unstructured":"Motor & Gear Engineering Inc (2023, February 06). Gear Hobbing: Introduction, Working, Advantages, and Applications. Available online: https:\/\/www.motorgearengineer.com\/blog\/gear-hobbing-introduction-working-advantages-applications\/."},{"key":"ref_27","first-page":"31","article-title":"Machining quality prediction using acoustic sensors and machine learning","volume":"63","author":"Carrino","year":"2020","journal-title":"Proceedings"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Klocke, F. (2018). Fertigungsverfahren 1, Springer.","DOI":"10.1007\/978-3-662-54207-1"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.cirpj.2021.06.016","article-title":"Balancing the trade-off between measurement uncertainty and measurement time in optical metrology using design of experiments, meta-modelling and convex programming","volume":"35","author":"Gauder","year":"2021","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_30","unstructured":"QASS (2023, January 03). Analyzer4D Handbuch. Available online: https:\/\/www.qass.net\/downloads\/Handbuch_Analyzer4D_V1.7.1.pdf."},{"key":"ref_31","unstructured":"Bruker Alicona (2023, January 03). Optische Koordinatenmessmaschine f\u00fcr Komplexe Geometrien. Available online: https:\/\/www.alicona.com\/de\/produkte\/cmm\/."},{"key":"ref_32","unstructured":"Mikut, R. (2008). Data Mining in der Medizin und Medizintechnik, KIT Scientific Publishing."},{"key":"ref_33","unstructured":"(2013). Zylinderr\u00e4der\u2014ISO-Toleranzsystem: Teil 1: Definitionen und Zul\u00e4ssige Werte f\u00fcr Abweichungen an Zahnflanken. Standard No. ISO 1328-1:2013."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3361","DOI":"10.1007\/s00170-019-03686-2","article-title":"Tool wear monitoring and prediction based on sound signal","volume":"103","author":"Liu","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.procir.2017.03.271","article-title":"Model-Based Online Tool Monitoring for Hobbing Processes","volume":"58","author":"Klocke","year":"2017","journal-title":"Procedia CIRP"},{"key":"ref_36","unstructured":"Le\u00f3n, F.P., and J\u00e4kel, H. (2019). Singale und Systeme, De Gruyter."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kale, A.P., Wahul, R.M., Patange, A.D., Soman, R., and Ostachowicz, W. (2023). Development of Deep Belief Network for Tool Faults Recognition. Sensors, 23.","DOI":"10.3390\/s23041872"},{"key":"ref_38","first-page":"177","article-title":"Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms","volume":"136","author":"Patil","year":"2023","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Patange, A.D., Pardeshi, S.S., Jegadeeshwaran, R., Zarkar, A., and Verma, K. (2022). Augmentation of Decision Tree Model Through Hyper-Parameters Tuning for Monitoring of Cutting Tool Faults Based on Vibration Signatures. J. Vib. Eng. Technol., 1\u201319.","DOI":"10.1007\/s42417-022-00781-9"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/4\/183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:04:47Z","timestamp":1760123087000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/4\/183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["a16040183"],"URL":"https:\/\/doi.org\/10.3390\/a16040183","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}