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2021 | Buch

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Socio-Inspired Optimization Methods for Advanced Manufacturing Processes

verfasst von: Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni

Verlag: Springer Singapore

Buchreihe : Springer Series in Advanced Manufacturing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

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Inhaltsverzeichnis

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

Titel: Socio-Inspired Optimization Methods for Advanced Manufacturing Processes
verfasst von: Apoorva Shastri
Aniket Nargundkar
Anand J. Kulkarni
Copyright-Jahr: 2021
Verlag: 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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