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2021 | Book

Socio-Inspired Optimization Methods for Advanced Manufacturing Processes

Authors: Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni

Publisher: Springer Singapore

Book Series : Springer Series in Advanced Manufacturing

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About this book

This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods.

Table of Contents

Frontmatter
Chapter 1. Introduction to Advanced Manufacturing Processes and Optimization Methodologies
Abstract
Manufacturing can be defined as the application of mechanical, physical, and chemical processes to convert the geometry, properties, and/or shape of raw material into finished parts or products. This includes all intermediate processes required for the production and integration of the final product. Manufacturing involves interrelated activities which include product design, material selection, production process planning, production, quality assurance, management and marketing of products.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 2. A Brief Review of Socio-inspired Metaheuristics
Abstract
There are several deterministic and approximation algorithms proposed so far. As the problem complexity grows the approximation algorithms have proven to be computationally cheaper as compared to the earlier ones. The approximation algorithms could be classified as bio-inspired algorithms, swarm-based algorithms and physical & chemical based algorithms. The notable bio-inspired algorithms are Genetic Algorithms, Differential Evolution, Artificial Immune System, etc.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 3. Multi Cohort Intelligence Algorithm
Abstract
Multi-Cohort Intelligence (Multi-CI) algorithm has been proposed by Shastri and Kulkarni in [14]. The algorithm implements intra-group and inter-group learning mechanisms. It focuses on the interaction amongst different cohorts. The performance of the algorithm was validated by solving 75 unconstrained test problems with dimensions up to 30.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 4. Optimization of Electric Discharge Machining (EDM)
Abstract
Electric Discharge Machining (EDM) is an electro-thermal, Non-Traditional Machining (NTM) process in which electrical energy is used to generate spark between tool & workpiece and thus material is removed. EDM is mainly used to machine high strength temperature resistant materials and alloys with intricate geometries and is a quite popular NTM process in the machining industry.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 5. Optimization of Abrasive Water Jet Machining (AWJM)
Abstract
Abrasive Water Jet Machining (AWJM) is an advanced version of Abrasive Jet Machining (AWJ) which employs water as the carrier medium for abrasive particles. The AWJM process can machine complex shapes and importantly, doesn’t generate heat concentrated zones. Work piece thickness, nozzle diameter, standoff distance and traverse speed are the typical process parameters/variables for AWJM. Kerf taper angle and surface roughness are performance responses as they indicate the geometry and surface finish of machined component, respectively.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 6. Optimization of Micro Milling Process
Abstract
Micro-Milling refers to a basic end-milling process using tools up to 1 mm in diameter. The geometry that can be produced by micro-end-milling is more flexible than those produced by lithography and other traditional micro manufacturing techniques. Furthermore, a wide range of materials could be processed using micro end milling. This chapter is based on the optimization of process parameters of micro milling performed on polymethyl methacrylate (PMMA) workpiece.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 7. Optimization of Micro Drilling Process
Abstract
Mechanical micro-drilling is one of the most widely used methods among several micro-hole making methods because of its least dependency on the material properties. Various factors such as tool diameter, spindle speed, tool helix angle, twist angle and feed rate determine the hole quality, and thus, they have to be chosen very carefully. Controlling burr formation in micro holes is significant as it causes deterioration of surface quality which reduces product durability and precision, assembly problems, wear and tear on the surface, etc.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 8. Optimization of Micro Drilling of CFRP Composites for Aerospace Applications
Abstract
In this chapter, variations of Cohort Intelligence (CI) algorithm have been applied for the minimization of cutting forces in \({\text{x}},\;{\text{y }}\;{\text{and }}\;{\text{z }}\) directions induced in micro drilling of carbon fiber reinforced plastic (CFPR) composite materials for aerospace applications.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Chapter 9. Optimization of Micro-turning Process
Abstract
The micro-turning processes have received a significant attention in the production of micro components with a diversity of materials including brass, aluminium, stainless steel, etc. Cutting speed, feed and depth of cut are the general process parameters/variables for micro turning process and surface roughness, flank wear, MRR, machining time are the typical process responses.
Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni
Backmatter
Metadata
Title
Socio-Inspired Optimization Methods for Advanced Manufacturing Processes
Authors
Apoorva Shastri
Aniket Nargundkar
Anand J. Kulkarni
Copyright Year
2021
Publisher
Springer Singapore
Electronic ISBN
978-981-15-7797-0
Print ISBN
978-981-15-7796-3
DOI
https://doi.org/10.1007/978-981-15-7797-0

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