Laser Processing with Material Removal

2022 ◽  
pp. 39-54
2000 ◽  
Author(s):  
J. Sun ◽  
J. P. Longtin ◽  
P. M. Norris

Abstract Silica aerogels are unique nanostructured materials that possess many distinctive qualities, including extremely low densities and thermal conductivities, very high surface-area-to-volume ratios, and large strength-to-weight ratios. Aerogels, however, are very brittle, and are not readily shaped using traditional machining operations. Ultrafast laser processing may provide an alternative for precision shaping and machining of these materials. This paper discusses investigations of ultrafast laser machining of aerogels for material removal and micromachining. The advantages of ultrafast laser processing include a minimal thermal penetration region and low processing temperatures, precision removal of material, and good-quality feature definition. In this work, an amplified femtosecond Ti:sapphire laser system is used to investigate the breakdown threshold, material removal rate, and specific issues associated with laser processing of aerogels, as well as recommendations for further investigations for these unique materials.


2021 ◽  
Vol 144 ◽  
pp. 107445
Author(s):  
Nengru Tao ◽  
Genyu Chen ◽  
Licheng Fan ◽  
Biao Wang ◽  
Mingquan Li ◽  
...  

Author(s):  
Amritpal Singh ◽  
Rakesh Kumar

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.


Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.


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