scholarly journals Application of hybrid Taguchi-Grey relational analysis (HTGRA) multi-optimization technique to minimize surface roughness and tool wear in turning AISI4340 steel

2021 ◽  
Vol 1913 (1) ◽  
pp. 012142
Author(s):  
Prashant D Kamble ◽  
Atul C Waghmare ◽  
Ramesh D Askhedkar ◽  
Shilpa B Sahare ◽  
Brij R Singh
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Grey Relational Analysis ◽  
Optimization Technique ◽  
Relational Analysis ◽  
Grey Relational ◽  
Aisi4340 Steel

Optimization of Reaming Process Parameters for Alloy Grey Cast Iron HT250 Using Grey Relational Analysis

2020 ◽  
Vol 866 ◽  
pp. 32-41
Author(s):  
C. Ye ◽  
X.R. Shi ◽  
L. Chen ◽  
Yong Guo Wang
Keyword(s):  
Tool Wear ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Hole Quality ◽  
Crater Wear ◽  
Relational Analysis ◽  
Grey Relational ◽  
Coated Carbide

Reaming is one of the finishing processes that has been widely applied in automotive industry. Reaming parameters were evaluated and optimized based on multiple performance characteristics including tool wear and hole quality. Taguchi’s L16, 4-level, 2-factor orthogonal array (OA) was conducted for this test. It was shown that crater wear and flank wear were seen on the tool surface. Furthermore, the crater wear was also of major significance. Hole quality was discovered to be mostly dependent upon cutting speed and feed rate. TiAlN coated carbide reamer shows the best performance with respect to the tool wear as well as hole quality. Grey relational analysis used as a multiple-response optimization technique found that feed rate was the more influential parameter than cutting speed. The goal of the experimental results was to obtain both minimum diametral error and the value of surface roughness by adopting the optimal combination of the reaming parameters.

scholarly journals Analysis and optimization of laser drilling process during machining of AISI 303 material using grey relational analysis approach

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
V. Chengal Reddy ◽  
Thota Keerthi ◽  
T. Nishkala ◽  
G. Maruthi Prasad Yadav
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Grey Relational Analysis ◽  
Pulse Frequency ◽  
Laser Drilling ◽  
Simultaneous Optimization ◽  
Fibre Laser ◽  
Drilling Process ◽  
Relational Analysis ◽  
Grey Relational

AbstractSurface roughness and heat-affected zone (HAZ) are the important features which influence the performance of the laser-drilled products. Understanding the influence of laser process parameters on these responses and identifying the cutting conditions for simultaneous optimization of these responses are a primary requirement in order to improve the laser drilling performance. Nevertheless, no such contribution has been made in the literature during laser drilling of AISI 303 material. The aim of the present work is to optimize the surface roughness (Ra) and HAZ in fibre laser drilling of AISI 303 material using Taguchi-based grey relational analysis (GRA). From the GRA methodology, the recommended optimum combination of process parameters is flushing pressure at 30 Pa, laser power at 2000 W and pulse frequency at 1500 Hz for simultaneous optimization of Ra and HAZ, respectively. From analysis of variance, the pulse frequency is identified as the most influenced process parameters on laser drilling process performance.

EXPERIMENTAL INVESTIGATION OF COCONUT OIL WITH NANOBORIC ACID DURING MILLING OF INCONEL 625 USING TAGUCHI-GREY RELATIONAL ANALYSIS

2020 ◽  
pp. 2150008
Author(s):  
T. MOHANRAJ ◽  
P. RAGAV ◽  
E. S. GOKUL ◽  
P. SENTHIL ◽  
K. S. RAGHUL ANANDH
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Flank Wear ◽  
Coconut Oil ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Individual Response ◽  
Relational Analysis ◽  
Grey Relational ◽  
Taguchi’S Design Of Experiments

This study is based on Taguchi’s design of experiments along with grey relational analysis (GRA) to optimize the milling parameters to minimize surface roughness, tool wear, and vibration during machining of Inconel-625 while using coconut oil as cutting fluid (CF). The experiments were conducted based on Taguchi’s L9 orthogonal array (OA). Taguchi’s S/N was used for identifying the optimal cutting parameter for individual response. Analysis of variance (ANOVA) was employed to analyze the outcome of individual parameters on responses. The surface roughness was mostly influenced by feed. Flank wear was influenced by speed and the vibration was mostly influenced by the depth of cut as well as speed. The multi-response optimization was done through GRA. From GRA, the optimal parameters were identified. Further, nanoboric acid of 0.5 and 0.9[Formula: see text]wt.% was mixed with coconut oil to enhance lubricant properties. Coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid minimizes the surface roughness and flank wear by 3.92% and 6.28% and reduces the vibration in the [Formula: see text]-axis by 4.85%. The coconut oil with 0.5[Formula: see text]wt.% of nanoboric acid performs better than coconut oil with 0.9[Formula: see text]wt.% of nano boric acid and base oil.

Analysis and Optimization of Wire EDM Process of Titanium by Using GRA Methodology

2019 ◽  
Vol 969 ◽  
pp. 678-684 ◽  
Author(s):  
Sarat Kumar Sahoo ◽  
A. Bara ◽  
A.K. Sahu ◽  
S.S. Mahapatra ◽  
D.S. Kiran ◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Orthogonal Array ◽  
Electrical Discharge Machining ◽  
Input Parameter ◽  
Optimization Technique ◽  
Research Work ◽  
Cutting Speed ◽  
Machining Process ◽  
Relational Analysis ◽  
Grey Relational

In this research work, an efficient optimization technique, grey relational analysis (GRA) has been used to for optimization of wire electrical discharge machining process of Titanium (grade 2) by considering multiple output parameters. This technique combines Taguchi’s orthogonal array with grey relational analysis for the design of the experiment. The central focus of this research is to achieve improved Kerf width, surface roughness and cutting speed. GRA method is implemented to decide the best input parameter that optimizes the output parameters. This study has been conducted by applying Taguchi’s L9 orthogonal array. Each experiment has been conducted in altered conditions of input variables. For the optimization of multiple criteria, GRA is suggested as a suitable technique for the optimization of complex interrelationships between multi-performance characteristics. By analysis of variance (ANOVA) it is found that the percentage of contribution of peak current on overall performance is maximum i.e.73.1%.

scholarly journals Quality Performance Evaluation of Thin Walled PLA 3D Printed Parts Using the Taguchi Method and Grey Relational Analysis

2020 ◽  
Vol 4 (2) ◽  
pp. 47 ◽  
Author(s):  
Kyriaki-Evangelia Aslani ◽  
Dimitrios Chaidas ◽  
John Kechagias ◽  
Panagiotis Kyratsis ◽  
Konstantinos Salonitis
Keyword(s):  
Surface Roughness ◽  
Wall Thickness ◽  
Grey Relational Analysis ◽  
Dimensional Accuracy ◽  
Optimization Techniques ◽  
Extraction Temperature ◽  
Quality Performance ◽  
Relational Analysis ◽  
3D Printed ◽  
Grey Relational

This paper investigates the quality performance of FDM 3D printed models with thin walls. The design of experiments method (DOE) was used and nine models of the same size were fabricated in a low-cost 3D printer using polylactic acid (PLA) material. Two limited studied parameters were considered (extraction temperature and wall thickness), each one having three levels. External X and Y dimensions were measured using a micrometer, as well as four surface roughness parameters (Ra, Rz, Rt, Rsm) with a surface tester. Two optimization techniques (the Taguchi approach and Grey relational analysis) were utilized along with statistical analysis to examine how the temperature and wall thickness affect the dimensional accuracy and the surface quality of the parts. The results showed that high extraction temperature and median wall thickness values optimize both dimensional accuracy and surface roughness, while temperature is the most important factor.

Performance analysis of process parameters on machining α–β titanium alloy in electrochemical micromachining process

2016 ◽  
Vol 232 (9) ◽  
pp. 1577-1589 ◽  
Author(s):  
T Geethapriyan ◽  
K Kalaichelvan ◽  
T Muthuramalingam ◽  
A Rajadurai
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Micromachining ◽  
Relational Analysis ◽  
Micro Tool ◽  
Grey Relational

Due to inherent properties of Ti-6Al-4V alloy, it is being used in the application of fuel injector nozzle for diesel engine, aerospace and marine industries. Since the electrochemical micromachining process involves the no heat-affected zone, no tool wear, stress- and burr-free process compared to other micromachining processes, it is widely used in the manufacturing field to fabricate complex shape and die. Hence, it is highly important to compute the optimum input parameters for enhancing the machining characteristics in such machining process. In this study, an attempt has been made to find the influence of the process parameters and optimize the parameters on machining α–β titanium alloy using Taguchi-grey relational analysis. Since applied voltage, micro-tool feed rate, electrolyte concentration and duty cycle have vital role in the process, these parameters have been chosen as the input parameters to evaluate the performance measures such as material removal rate, surface roughness and overcut in this study. From the experimental results, it has been found that micro-tool feed rate has more influence due to its importance in maintaining inter electrode gap to avoid micro-spark generation. It has also been found that lower electrolyte concentration with lower duty cycle produces lower surface roughness with better circularity on machining α–β titanium alloy. The optimum combination has been found using Taguchi-grey relational analysis and verified from confirmation test. It has also been inferred that the multi-response characteristics such as material removal rate, surface roughness and overcut can be effectively improved through the grey relational analysis.

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