Author: Prafull P Shirpurkar
Publisher: Educreation Publishing
ISBN:
Category : Education
Languages : en
Pages : 128

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Book Description
The book contains Optimization of Multi response of Turning Process Parameters by Using Tool Inserts, now a days mostly used optimization technique which is better than single response optimizing technique because all the output is affected at a time by all the input factors. The objective of this book is to determine the optimal setting of cutting parameters speed (N)m/min, depth of cut(d) mm, feed(f)mm/rev, Nose Radius(r)mm, variation amplitude(mm/sec2), vibration frequency(kHz) in Cutting tool inserts to minimize surface roughness (Ra) and to increase the Tool life. In this book the experiment has been carried out on CNC (SPINNER 15) lathe in dry, Wet and MQL (Minimum Quantity Lubrication) cutting Condition turning of a commercially used EN 24 grade steel as a work material and carbide insert tool (CNMG120408 CNMG120412). This book highlights use of Taguchi experiment design to optimize the multi response parameters on turning operation. For this experiment Taguchi design of experiment was carried out to collect the data for surface roughness and tool vibration. The results indicate the optimum values of the input factors and the results are conformed by a confirmatory test. This book describes use and steps of Taguchi design of experiments and orthogonal array to find a specific range and combinations of turning parameters like cutting speed, feed rate and depth of cut, Nose Radius and Cutting condition to achieve optimal values of response variables like surface roughness, tool life, material removal rate in turning of Split Bush of EN24 Material.

Author: Kaushik Kumar
Publisher: John Wiley & Sons
ISBN: 1119644607
Category : Technology & Engineering
Languages : en
Pages : 188

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Book Description
Optimization is central to any problem involving decision-making in engineering. Optimization theory and methods deal with selecting the best option regarding the given objective function or performance index. New algorithmic and theoretical techniques have been developed for this purpose, and have rapidly diffused into other disciplines. As a result, our knowledge of all aspects of the field has grown even more profound. In Optimization for Engineering Problems, eminent researchers in the field present the latest knowledge and techniques on the subject of optimization in engineering. Whereas the majority of work in this area focuses on other applications, this book applies advanced and algorithm-based optimization techniques specifically to problems in engineering.

Author: Nicolás Mancilla Cubides
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Modern production is faced with the challenges in reducing the environmental impacts related to machining processes. Turning process is a manufacturing process widely used with a vast application for creating engineering components. In this context, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This paper considers the quality of the products (surface finish) and the productivity rate of the turning manufacturing process to be both optimized. Product quality is quantified using surface roughness (R_a) and the productivity rate using material removal rate (MRR). We develop a predictive and optimization model by coupling artificial neural networks (ANN) and the Particle Swarm Optimization (PSO), a multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). To obtain the data, Aluminum is used to perform the turning process experiments, considering the cutting speed, feed rate, depth of cut and nose radius of the cutting tool as our design factors. We used the gathered data to train and develop the ANN model. The results predicted by the proposed models indicate good agreement between the predicted and experimental values, proving that the proposed ANN model is capable of predicting the surface roughness accurately. Then, the optimization model PSO has provided a Pareto Front for the optimal solution, determining the optimum machining parameters for minimum R_a and maximum MRR. This study has application in the real industry where the selection of optimal machining parameters helps to complete and manage conflicting objectives that constitute hurdles in the decision-making of the manufacturing plans.

Author: Ruturaj Jayant Kulkarni
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 240

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Book Description
In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.

Author: Melanie Mitchell
Publisher: MIT Press
ISBN: 9780262631853
Category : Computers
Languages : en
Pages : 226

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Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Author: Satish Pujari
Publisher: Springer
ISBN: 9811376433
Category : Technology & Engineering
Languages : en
Pages : 790

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Book Description
This book comprises select proceedings of the International Conference on Latest Innovations in Materials Engineering and Technology (ICLIET 2018). The book focuses on diverse engineering materials, their design and applications. The materials in discussion include those related to coatings, polymers, composites, tribology, acoustic insulators, lubricants, and cryogenics. The book also highlights emerging nano and micro materials, bio engineering materials, as well as new energy materials for solar cells and photovoltaic cells. This book will serve as an useful reference for students, researchers, and professionals working in the field of materials science and engineering.

Author: G. S. V. L. Narasimham
Publisher: Springer Nature
ISBN: 9811575576
Category : Technology & Engineering
Languages : en
Pages : 697

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Book Description
This book consists of peer-reviewed proceedings from the International Conference on Innovations in Mechanical Engineering (ICIME 2020). The contents cover latest research in all major areas of mechanical engineering, and are broadly divided into five parts: (i) thermal engineering, (ii) design and optimization, (iii) production and industrial engineering, (iv) materials science and metallurgy, and (v) multidisciplinary topics. Different aspects of designing, modeling, manufacturing, optimizing, and processing are discussed in the context of emerging applications. Given the range of topics covered, this book can be useful for students, researchers as well as professionals.

Author: J. Paulo Davim
Publisher: Springer
ISBN: 3319519611
Category : Technology & Engineering
Languages : en
Pages : 90

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Book Description
This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Author: Catalin I. Pruncu
Publisher: John Wiley & Sons
ISBN: 3527346945
Category : Technology & Engineering
Languages : en
Pages : 338

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Book Description
Discover the state-of-the-art in multiscale modeling and optimization in manufacturing from two leading voices in the field Modeling and Optimization in Manufacturing delivers a comprehensive approach to various manufacturing processes and shows readers how multiscale modeling and optimization processes help improve upon them. The book elaborates on the foundations and applications of computational modeling and optimization processes, as well as recent developments in the field. It offers discussions of manufacturing processes, including forming, machining, casting, joining, coating, and additive manufacturing, and how computer simulations have influenced their development. Examples for each category of manufacturing are provided in the text, and industrial applications are described for the reader. The distinguished authors also provide an insightful perspective on likely future trends and developments in manufacturing modeling and optimization, including the use of large materials databases and machine learning. Readers will also benefit from the inclusion of: A thorough introduction to the origins of manufacturing, the history of traditional and advanced manufacturing, and recent progress in manufacturing An exploration of advanced manufacturing and the environmental impact and significance of manufacturing Practical discussions of the economic importance of advanced manufacturing An examination of the sustainability of advanced manufacturing, and developing and future trends in manufacturing Perfect for materials scientists, mechanical engineers, and process engineers, Modeling and Optimization in Manufacturing will also earn a place in the libraries of engineering scientists in industries seeking a one-stop reference on multiscale modeling and optimization in manufacturing.

Author: Mauricio A. Álvarez
Publisher: Foundations & Trends
ISBN: 9781601985583
Category : Computers
Languages : en
Pages : 86

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Book Description
This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods.