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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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A review on the role of laser textured surfaces on boiling heat transfer

TL;DR: Laser texturing can effectively obtain the desired surface features on metals through melting and solidification, or through laser-based additive manufacturing techniques as discussed by the authors, which can be both beneficial and durable.
Journal ArticleDOI

Laser-assisted bending of Titanium Grade-2 sheets: Experimental analysis and numerical simulation

TL;DR: In this article, the influence of the operational parameters, laser power, scan speed, number of passes, on bending angles and fillet radii of the metal substrates is analyzed.
Journal ArticleDOI

Modelling of process parameters in laser polishing of steel components using ensembles of regression trees

TL;DR: The aim of this work is to develop a soft computing tool for surface roughness prediction of laser polished components, which has been proven that the results of an ensemble, which is a combination of several models, are better than single methods in many applications.
Journal ArticleDOI

Recovery of nonseparability in self-healing vector Bessel beams

TL;DR: In this article, the authors measured and quantified the degree of nonseparability between the two degrees of freedom when propagating behind various obstructions, which were generated digitally, and showed that the local entanglement can be recovered by suitable choice of the enveloping wave function.
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Effect of surface texturing by femtosecond laser on tantalum carbide ceramics for solar receiver applications

TL;DR: In this paper, the effect of surface texturing by femtosecond laser machining on Tantalum carbide was investigated, and laser-induced microstructural and optical property changes were characterized in two samples with different starting roughness and for four laser treatments each, as a function of the accumulated laser fluence.
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.
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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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