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Laser beam machining—A review

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TLDR
The experimental and theoretical studies of LBM show that process performance can be improved considerably by proper selection of laser parameters, material parameters and operating parameters, and the trend for future research is outlined.
Abstract
Laser beam machining (LBM) is one of the most widely used thermal energy based non-contact type advance machining process which can be applied for almost whole range of materials. Laser beam is focussed for melting and vaporizing the unwanted material from the parent material. It is suitable for geometrically complex profile cutting and making miniature holes in sheetmetal. Among various type of lasers used for machining in industries, CO2 and Nd:YAG lasers are most established. In recent years, researchers have explored a number of ways to improve the LBM process performance by analysing the different factors that affect the quality characteristics. The experimental and theoretical studies show that process performance can be improved considerably by proper selection of laser parameters, material parameters and operating parameters. This paper reviews the research work carried out so far in the area of LBM of different materials and shapes. It reports about the experimental and theoretical studies of LBM to improve the process performance. Several modelling and optimization techniques for the determination of optimum laser beam cutting condition have been critically examined. The last part of this paper discusses the LBM developments and outlines the trend for future research.

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Journal ArticleDOI

Development of a high-power blue laser (445 nm) for material processing

TL;DR: To the best of the authors' knowledge, this is the first blue laser with a power as high as 250 W, and the characteristics of the steel after laser irradiation were determined, validating the potential of this high-power blue laser for material processing, such as heat treatment and cladding.
Journal ArticleDOI

Micromachining and surface processing of the super-hard nano-polycrystalline diamond by three types of pulsed lasers

TL;DR: In this article, three types of pulsed lasers were tested: nanosecond near-infrared, near-ultraviolet, and femtosecond NIR laser for rough shaping of super-hard nano-polycrystalline diamond.
Journal ArticleDOI

Multiple quality optimization in laser cutting of difficult-to-laser-cut material using grey-fuzzy methodology

TL;DR: In this paper, the laser cutting performance of 1mm Duralumin sheet with the aim to improve quality of cut by simultaneously optimising multiple performances such as cut edge surface roughness, kerf taper and kerf width was investigated.
Journal ArticleDOI

A comprehensive review on metallic implant biomaterials and their subtractive manufacturing

TL;DR: In this paper , a review of non-degradable and degradable metallic implant biomaterials such as stainless steel (SS), titanium (Ti), cobalt (Co)-based, nickel-titanium (NiTi), and magnesium (Mg)-based alloys is presented.
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Laser surface texturing and its contribution to friction and wear reduction: a brief review

TL;DR: In this article, the authors made people aware of the increase usage of laser system in surface texturing process and showed that the realization of the textured surface helps to reduce friction and wear which can increase the life span of a material.
References
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Journal Article

The Design and Analysis of Experiments

TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Book

Quality Engineering Using Robust Design

TL;DR: This book offers a complete blueprint for structuring projects to achieve rapid completion with high engineering productivity during the research and development phase to ensure that high quality products can be made quickly and at the lowest possible cost.
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Predicting surface roughness in machining: a review

TL;DR: In this article, the authors present the various methodologies and practices that are being employed for the prediction of surface roughness, including machining theory, experimental investigation, designed experiments and artificial intelligence (AI).
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