Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking
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DOI: 10.1007/s10845-021-01789-w
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- D. Yu. Pimenov & A. Bustillo & T. Mikolajczyk, 2018. "Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1045-1061, June.
- Andres Bustillo & Danil Yu. Pimenov & Mozammel Mia & Wojciech Kapłonek, 2021. "Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 895-912, March.
- Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
- Deepam Goyal & Anurag Choudhary & B. S. Pabla & S. S. Dhami, 2020. "Support vector machines based non-contact fault diagnosis system for bearings," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1275-1289, June.
- Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
- Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
- Ivanna Baturynska & Kristian Martinsen, 2021. "Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 179-200, January.
- Shujie Liu & Yawei Hu & Chao Li & Huitian Lu & Hongchao Zhang, 2017. "Machinery condition prediction based on wavelet and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1045-1055, April.
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Cited by:
- Philipp Niemietz & Mia J. K. Kornely & Daniel Trauth & Thomas Bergs, 2022. "Relating wear stages in sheet metal forming based on short- and long-term force signal variations," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2143-2155, October.
- Zaher Salah & Esraa Abu Elsoud, 2023. "Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11," Future Internet, MDPI, vol. 15(8), pages 1-21, August.
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Keywords
Resilient manufacturing; Machine learning in blanking; Data driven process optimization;All these keywords.
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