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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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Impact of parameters on the process response: A Taguchi orthogonal analysis for laser engraving

TL;DR: In this paper, the impact of laser engraving process on Vanadis 10 was investigated and a mathematical model for both surface roughness (Ra) and depth (D) was established and estimated using linear regression.
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Recent developments in the reverse micro-electrical discharge machining in the fabrication of arrayed micro-features:

TL;DR: High aspect ratio arrayed micro-structures and textured surfaces are required in diversified applications such as electrical contacts, printing heads, electrodes for micro-batteries, injection nozz... as discussed by the authors.
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A review on applications of artificial intelligence in modeling and optimization of laser beam machining

TL;DR: It is shown that AI techniques are successfully capable of predicting and improving the features of the laser machined workpiece and can be used as a powerful tool to obtain a comprehensive model and optimal setting parameters of LBM.
Journal ArticleDOI

Material characterization and unconventional machining on synthesized Niobium metal matrix

TL;DR: In this article, the authors developed a niobium-based metal matrix alloy through sintering based powder metallurgy technique and the 2, 4 and 6 weight percentage of Titanium Carbide (TiC) is added to the alloy.
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Laser-assisted grinding of silicon nitride by picosecond laser

TL;DR: In this paper, a laser-assisted grinding process is developed to overcome the current technological constraints in the grinding of Si3N4, where ultra-short pulsed laser radiations are efficiently applied to create ablation, controlled thermal damages and enhance the material removal rate.
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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