Advances and Futuristic Trends in Machining Volume-I

Proceedings of the AIMTDR 2023
Herausgegeben:Jain, P. K.; Shunmugam, M. S.; Vashista, M.; Pratap, S.; Misra, J. P.

• Broschiertes Buch

Produktbeschreibung

This book presents select proceedings of the 9th International and 30th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2023). It discusses the latest advances in hybrid manufacturing process and technology, composites fabrication, non-traditional and advanced machining processes, energy beam processing, high performance cutting tools, micro and nano machining of glasses and ceramics, concurrent and reverse engineering, modeling of machining processes, intelligent machining, and super finishing technologies, among other areas. The contents of this book are useful for researchers and professionals in the various fields of mechanical engineering.

Produktdetails

• Lecture Notes in Mechanical Engineering
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-96-8368-0
• Seitenzahl: 240
• Erscheinungstermin: August 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9789819683680
• Artikelnr.: 74213877

Inhaltsangabe

Face Centered CCD-based RSM Modelling and Optimization of parameters for Milling Electrochemical Spark Micromachining of E-Glass Fiber Composites.- Advanced FE-based Hybrid Algorithms to Optimize Uncertain Multi response Process Parameters in Tungsten Heavy Alloy Machining.- Wear of coated carbide tool having different microstructure and composition in wall end-milling of SS304.- Machinability analysis of AISI 4140: A comparison between dry, flood, and vegetable oil-based EMQL machining performance.- Fabrication of Multiple Blind Holes on Glass Using Ultrasonic-Assisted ECDM Process.- Estimating Material Deformation Characteristics During Orthogonal Cutting Using Digital Image Correlation.- An Experimental Investigation of micro-hole generation on Ti-6Al-4V through electrochemical micromachining.- AHP-TOPSIS Integrated Approach for Parameters Selection in WEDM of Titanium Alloy.- Predictive Modeling of Surface Roughness Parameters and MRR during Turning of Inconel 625 with Coated Inserts using Artificial Neural Network.- Modelling and Parametric analysis for WEDM during machining of heat treated AZ31 alloy.- etc.