Machining of Hard Materials [electronic resource] : A Comprehensive Approach to Experimentation, Modeling and Optimization / by Manjunath Patel G. C., Ganesh R. Chate, Mahesh B. Parappagoudar, Kapil Gupta.
By: Patel G. C., Manjunath [author.]
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Contributor(s): Chate, Ganesh R [author.] | Parappagoudar, Mahesh B [author.] | Gupta, Kapil [author.] | SpringerLink (Online service).
Material type: 
BookSeries: Manufacturing and Surface Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: IX, 129 p. 39 illus., 19 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030401023.Subject(s): Manufactures | Metals | Machines, Tools, Processes | Metals and AlloysAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 670 Online resources: Click here to access online
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Springer Nature eBookSummary: This book presents the potential applications of hard materials as well as the latest trends and challenges in machining hard materials. Models for online monitoring to adjust parameters to obtain desired machining characteristics (i.e. reverse modelling) are discussed in this book. The conflicting requirements (i.e. maximize: material removal rate, roundness and minimize: surface roughness, dimensional ovality, co axiality, tool wear) in machining for industry personal is solved using advanced optimization tools. In addition, the framework for experimental modelling, predictive physic-based forward and reverse process models and optimization for better machining characteristics applicable to industry are proposed.
This book presents the potential applications of hard materials as well as the latest trends and challenges in machining hard materials. Models for online monitoring to adjust parameters to obtain desired machining characteristics (i.e. reverse modelling) are discussed in this book. The conflicting requirements (i.e. maximize: material removal rate, roundness and minimize: surface roughness, dimensional ovality, co axiality, tool wear) in machining for industry personal is solved using advanced optimization tools. In addition, the framework for experimental modelling, predictive physic-based forward and reverse process models and optimization for better machining characteristics applicable to industry are proposed.
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