Journal ArticleDOI
Laser beam machining—A review
Avanish Kumar Dubey,Vinod Yadava +1 more
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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.read more
Citations
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RSM Based Modeling for Surface Roughness Prediction in Laser Machining
TL;DR: In this paper, the impact of cutting speed, frequency and duty cycle on surface roughness was investigated. And it was found that only a high cutting speed can produce good surface rouness coupled with high duty cycle regardless of frequency.
Book ChapterDOI
Materials and methods for the microfabrication of microfluidic biomedical devices
TL;DR: The materials used for the construction of microfluidic devices have been diverse, ranging from traditional materials, such as silicon and glass, to newer polymeric materials as discussed by the authors, and the methods for microfabrication have included lithography, casting, injection molding and hot embossing.
Journal ArticleDOI
An Investigation on Nd:YAG Laser Cutting of Al 6061 T6 Alloy Sheet
Claudio Leone,Silvio Genna,Alessandra Caggiano,Vincenzo Tagliaferri,Vincenzo Tagliaferri,R. Molitierno +5 more
TL;DR: In this paper, a 150W multimode pulsed Nd:YAG laser was used to cut 1 mm-thick 6061-T6 sheets with cutting speed up to 700mm/min, obtaining narrow kerfs.
Journal ArticleDOI
Experimental analysis of ductile-brittle transitions for parallel and intersecting micro-slot grinding in BK-7 glass
TL;DR: In this paper, the authors investigated the material removal mechanism and analyzed the damage occurrence for parallel as well as intersecting micro-slot grinding of BK7 glass using a modified polycrystalline diamond micro-tool at wide range of feed rate keeping tool rotational speed and depth of cut constant.
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.
Journal ArticleDOI
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).