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Titlebook: Welding and Cutting Case Studies with Supervised Machine Learning; S. Arungalai Vendan,Rajeev Kamal,Akhil Garg Book 2020 Springer Nature S

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發(fā)表于 2025-3-21 18:27:27 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Welding and Cutting Case Studies with Supervised Machine Learning
編輯S. Arungalai Vendan,Rajeev Kamal,Akhil Garg
視頻videohttp://file.papertrans.cn/1023/1022099/1022099.mp4
概述Combines mechanical, metallurgical, arc physics, manufacturing, electrical and data analytics expertise from an interdisciplinary perspective.Introduces supervised learning for parametrical interdepen
叢書名稱Engineering Applications of Computational Methods
圖書封面Titlebook: Welding and Cutting Case Studies with Supervised Machine Learning;  S. Arungalai Vendan,Rajeev Kamal,Akhil Garg Book 2020 Springer Nature S
描述.This book presents machine learning as a set of pre-requisites, co-requisites, and post-requisites, focusing on mathematical concepts and engineering applications in advanced welding and cutting processes. It describes a number of advanced welding and cutting processes and then assesses the parametrical interdependencies of two entities, namely the data analysis and data visualization techniques, which form the core of machine learning. Subsequently, it discusses supervised learning, highlighting Python libraries such as NumPy, Pandas and Scikit Learn programming. It also includes case studies that employ machine learning for manufacturing processes in the engineering domain. The book not only provides beginners with an introduction to machine learning for applied sciences, enabling them to address global competitiveness and work on real-time technical challenges, it is also a valuable resource for scholars with domain knowledge..
出版日期Book 2020
關(guān)鍵詞Advanced Welding Processes; Machine Learning; Supervised Learning; Cutting Processes; Parametric Analysi
版次1
doihttps://doi.org/10.1007/978-981-13-9382-2
isbn_softcover978-981-13-9384-6
isbn_ebook978-981-13-9382-2Series ISSN 2662-3366 Series E-ISSN 2662-3374
issn_series 2662-3366
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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發(fā)表于 2025-3-21 20:56:04 | 只看該作者
Welding and Cutting Case Studies with Supervised Machine Learning978-981-13-9382-2Series ISSN 2662-3366 Series E-ISSN 2662-3374
板凳
發(fā)表于 2025-3-22 03:33:41 | 只看該作者
Engineering Applications of Computational Methodshttp://image.papertrans.cn/w/image/1022099.jpg
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發(fā)表于 2025-3-22 04:48:35 | 只看該作者
https://doi.org/10.1007/978-981-13-9382-2Advanced Welding Processes; Machine Learning; Supervised Learning; Cutting Processes; Parametric Analysi
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Supervised Machine Learning in Magnetically Impelled ARC BUTT Welding (MIAB),The MIAB welding process was initially investigated by the E. O. Paton Electric Welding Institute during the 1950s. It was later developed for commercial applications by Kuka Welding systems, who named it the Magnet arc process.
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Supervised Machine Learning in Friction Stir Welding (FSW),The friction stir welding process was invented and patented by The Welding Institute (TWI), England in 1991 [.]. FSW process is a solid-state welding technique that is considered as a suitable joining technique for different weld type configurations such as T joints, butt joints, lap joints and butt joints.
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Supervised Machine Learning in Cold Metal Transfer (CMT),ing controlled process permits the material transfer with the nominal flow of current thereby reducing power consumption and material wastage. Constant retraction of filler wire at very short intervals is one of the major sequential steps in CMT process.
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