During the last few years, the prediction and optimization of cutting process parameters in various machining processes have received more attention than ever before, from research community all over the world. The soft computing techniques which are used for predicting and optimizing the cutting process parameters offer a number of advantages over the traditional numerical methods. In this book, the effect of the cutting process parameters in boring tool with and without impact dampers has been studied. The impact dampers made up of Copper, Phosphor Bronze, Brass, Gun Metal, EN8, Cast Iron and Aluminium were used and the cutting parameters and their effect on cutting responses in boring tool with and without dampers have been studied experimentally. Then the experimental values obtained were modelled for prediction with the help of Artificial Neural Network (ANN). The optimum set of readings was determined using Response Surface Methodology (RSM) and the interaction effect cutting parameter on cutting responses were discussed using Analysis of Variance (ANOVA) method.
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Ramesh K received his PhD degree in Mechanical Engineering from Anna University Chennai, Tamil Nadu, India in 2013. He is currently working as Assistant Professor (Senior Grade) of Mechanical Engineering at Government College of Technology, Coimbatore, Tamil Nadu, India for a period of 13 years (2005-2018).
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -During the last few years, the prediction and optimization of cutting process parameters in various machining processes have received more attention than ever before, from research community all over the world. The soft computing techniques which are used for predicting and optimizing the cutting process parameters offer a number of advantages over the traditional numerical methods. In this book, the effect of the cutting process parameters in boring tool with and without impact dampers has been studied. The impact dampers made up of Copper, Phosphor Bronze, Brass, Gun Metal, EN8, Cast Iron and Aluminium were used and the cutting parameters and their effect on cutting responses in boring tool with and without dampers have been studied experimentally. Then the experimental values obtained were modelled for prediction with the help of Artificial Neural Network (ANN). The optimum set of readings was determined using Response Surface Methodology (RSM) and the interaction effect cutting parameter on cutting responses were discussed using Analysis of Variance (ANOVA) method. 284 pp. Englisch. Bestandsnummer des Verkäufers 9783330006478
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Anbieter: moluna, Greven, Deutschland
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ramesh K.Ramesh K received his PhD degree in Mechanical Engineering from Anna University Chennai, Tamil Nadu, India in 2013. He is currently working as Assistant Professor (Senior Grade) of Mechanical Engineering at Government Colleg. Bestandsnummer des Verkäufers 385707130
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - During the last few years, the prediction and optimization of cutting process parameters in various machining processes have received more attention than ever before, from research community all over the world. The soft computing techniques which are used for predicting and optimizing the cutting process parameters offer a number of advantages over the traditional numerical methods. In this book, the effect of the cutting process parameters in boring tool with and without impact dampers has been studied. The impact dampers made up of Copper, Phosphor Bronze, Brass, Gun Metal, EN8, Cast Iron and Aluminium were used and the cutting parameters and their effect on cutting responses in boring tool with and without dampers have been studied experimentally. Then the experimental values obtained were modelled for prediction with the help of Artificial Neural Network (ANN). The optimum set of readings was determined using Response Surface Methodology (RSM) and the interaction effect cutting parameter on cutting responses were discussed using Analysis of Variance (ANOVA) method. Bestandsnummer des Verkäufers 9783330006478
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Taschenbuch. Zustand: Neu. Studies on Cutting Responses in Boring Process Using Impact Dampers | Detailed investigation on various damping materials | K. Ramesh (u. a.) | Taschenbuch | 284 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783330006478 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 114834450
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -During the last few years, the prediction and optimization of cutting process parameters in various machining processes have received more attention than ever before, from research community all over the world. The soft computing techniques which are used for predicting and optimizing the cutting process parameters offer a number of advantages over the traditional numerical methods. In this book, the effect of the cutting process parameters in boring tool with and without impact dampers has been studied. The impact dampers made up of Copper, Phosphor Bronze, Brass, Gun Metal, EN8, Cast Iron and Aluminium were used and the cutting parameters and their effect on cutting responses in boring tool with and without dampers have been studied experimentally. Then the experimental values obtained were modelled for prediction with the help of Artificial Neural Network (ANN). The optimum set of readings was determined using Response Surface Methodology (RSM) and the interaction effect cutting parameter on cutting responses were discussed using Analysis of Variance (ANOVA) method.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 284 pp. Englisch. Bestandsnummer des Verkäufers 9783330006478
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