scholarly journals Multi-Response Optimization of cutting parameters in MQL assisted Turning of Haynes 25 alloy with Taguchi based Grey Relational Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Dhote ◽  
◽  
Rahul A Mali ◽  
Jitendra Katiyar ◽  
Gupta T V K ◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Particle Concentration ◽  
Minimum Quantity Lubrication ◽  
Cutting Parameters ◽  
Nano Particle ◽  
Relational Analysis ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters ◽  
Coated Carbide ◽  
Thrust Forces

Haynes 25 is a cobalt based superalloy gaining its importance in aerospace, heat treatment applications, chemical handling equipment, commercial gas turbine engines, bearing material, etc. This alloy is featured with low thermal conductivity, wear and corrosion resistance, strength with good resistance to oxidation at high temperatures. In the present study, optimization of process parameters in turning Haynes 25 alloy with uncoated and coated carbide tools under the minimum quantity lubrication (MQL) using Taguchi based grey relational analysis (GRA) method is attempted. The influence of cutting parameters and nano-particle concentration on surface roughness, tool wear, cutting and thrust forces are analyzed to improve the alloy machinability. The work also compares the responses obtained with uncoated and coated tool inserts and analyzes the effect of nano-particle concentration. Further, the experimental cutting and thrust forces are computed and validated using FE based DEFORM 3D software. The results obtained through simulation are in good accordance with experimental data within an average relative error of about 12 %.

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

scholarly journals Investigation of Drilling Parameters Using Grey Relational Analysis and Response Surface Methodology of Biaxial Glass Fibre Reinforced with Modified Epoxy Resin Composite

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
I Infanta Mary Priya ◽  
B. K. Vinayagam
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Fibre ◽  
Grey Relational Analysis ◽  
Cutting Parameters ◽  
Image Processing Technique ◽  
Tool Materials ◽  
Relational Analysis ◽  
Glass Fibre Reinforced ◽  
Grey Relational

This research work attempts to study the most prominent factor of the drilling operations performed on different thicknesses of biaxial glass fibre reinforced with graphene platelet nanopowder and epoxy composite using three different drills. Damages are induced in the workpieces to analyse the effect of changed cutting parameters and different tool materials for varied thicknesses of the plates during the drilling process. The resultant drilled hole exhibited surface irregularities that are measured using SURFCOM 1400G. The circularity deviations of the holes are measured using a coordinate measuring machine. Image-processing technique is used to calculate the area of maximum diameter of the damaged zone. Using these data, delamination at entry and exit is calculated. Utilizing response surface methodology and grey relational analysis, the varied operations are carried out and analysed with different tool materials for common cutting parameters. It is found that the lesser thickness workpiece with selected HSS drill under high speed and low feed rate is the best variable option.

scholarly journals Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 217 ◽  
Author(s):  
Yu Su ◽  
Guoyong Zhao ◽  
Yugang Zhao ◽  
Jianbing Meng ◽  
Chunxiao Li
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
Relational Analysis ◽  
304 Austenitic Stainless Steel ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

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