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.

Autorentext

Dr. Manjunath Patel GC is an Assistant Professor in the Mechanical Engineering Department at PES Institute of Technology and Management, Shivamogga, India. He obtained Ph.D. in Mechanical Engineering with specialization in Manufacturing from National Institute of Technology Karnataka, Surathkal, India in 2015. Modelling and Optimization of Advanced Metal Casting, Welding and Machining Processes are the areas of Interest and specialization. Currently, he is doing research in advanced machining,  and Hydrid Casting Processes.

Mr. Ganesh R Chate is an Assistant Professor in Mechanical Engineering Department at KLS Gogte Institute of Technology Belagavi, Karnataka State, India. He holds a Master's degree in Production Management from KLS Gogte Institute of Technology, Belagavi.His research areas include manufacturing process, 3D printing, CAD and automation.

Prof. Mahesh B Parappagoudar joined Indian Institute of Technology, Kharagpur in 2004 as a research scholar, in the mechanical engineering department under the quality improvement program funded by MHRD, Govt. of India. Further, he obtained his PhD degree in Mechanical Engineering from Indian Institute of Technology, Kharagpur - 721302, India in 2008. Presently he is working as the principal and professor in Padre Conceicao College of Engineering, GOA, INDIA. His total experience (Industry, Teaching,Research, and Administration) extends over a period of 28 years. His research interests include applicationof statistical and soft computing tools in manufacturing and industrial engineering.

Prof. Kapil Gupta is an Associate Professor in the Dept. of Mechanical and Industrial Engineering Technology at the University of Johannesburg. He obtained Ph.D. in mechanical engineering with specialization in Avanced Manufacturing from Indian Institute of Technology Indore, India in 2014. Advanced machining processes, sustainable manufacturing, precision engineering and gear technology are the areas of his interest and specialization. Currently, he is doing research in advanced/modern machining,  sustainable manufacturing and gear engineering.

Inhalt

1.      Introduction to Hard Materials and Machining Methods of Hard Materials (Introduction to the hard materials, classification of hard materials, academic to industrial usage of hard materials, machining methods of hard materials, properties,

challenges for machining of hard materials, tool materials, geometry and propertiies, advantages and limitations of machining of hard materials).

2.      Machining of Hard Materials

(Studies on hard turning process will be reviewed for process condition, process variables, process variations, microstructures, and application).

3.      Statistical Modelling and Analysis of Hard Materials Machining Process (Introduction to statistical design of experiments, full factorial design, central composite design, Box-Behnken design, response surface methodology, analysis of process variables on machining characteristics (tool wear, surface roughness, material removal rate, cylindricity and so on), developing empirical relationship expressed machining characteristics as a function of process variables such as depth of cut, nose radius, cutting speed, feed rate and so on. Further, prediction accuracy of developed models are tested for practical utility with random experimental cases. Supporting microstructures on the machined surfaces are discussed as well).

4.      Intelligent Modelling and Analysis of Hard Materials Machining Process

(Introduction to artificial intelligence and applications of artificial intelligence tools in manufacturing and machining. Advantages of artificial intelligence tools over statistical modeling tools, classification of artificial intelligence tools and their advantages and limitations, development of artificial neural network, genetic algorithms and their hybrid combination tools for hard turning process. Comparison of statistical and artificial intelligence models for predictions. Discussion on how the artificial intelligence tools can be applied for online monitoring process for desired machining characteristics).

5.      Optimization of Hard Materials Machining Process

(Discussion on introduction to traditional optimization techniques, and limitations in solving complex optimization problems in machining. To cope up review of new advanced optimization tools applied for different manufacturing sectors such as GA, PSO, TLBO, JAYA, ABC, and So on. Selection of operating parameters of these advanced optimization tools are also discussed. Framework for utilization of advanced tools for solving optimization problems in hard turning process. Importance of principal component analysis to solve multi-objective optimization task is also discussed. The optimization task conducted by GA, PSO, JAYA, and TLBO are tested for both machining accuracy with practical experiments and computational efficiency. The tested machined parts are evaluated with reference to microstructural features)