Multi-objective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using Grey-Based Taguchi Method

2020 ◽  
pp. 584-593
Author(s):  
Tran Thi Hong ◽  
Do The Vinh ◽  
Tran Vinh Hung ◽  
Tran Ngoc Giang ◽  
Nguyen Thanh Tu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Surface Grinding ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi Objective Optimization of Surface Roughness and Workpiece Surface Temperature in Turning Operation

2014 ◽  
Vol 554 ◽  
pp. 376-380
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Surface Temperature ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Workpiece Surface ◽  
Multi Objective ◽  
Controllable Factors ◽  
Single Objective

This paper presents the optimization of multiple performance characteristics (surface roughness and workpiece surface temperature) based on the Taguchi method. Three controllable factors of the turning process were studied at three levels. The single objective optimization was conducted using Taguchi method. The multiple Signal-to-Noise (MSNR) value was used to correspond to multi objective cases. The optimum combination of cutting parameters was obtained based on the highest value of MSNR.

scholarly journals Combining Taguchi method and DEAR method for multi-objective optimization of grinding process

2021 ◽  
Vol 309 ◽  
pp. 01220
Author(s):  
Do Duc Trung ◽  
Nguyen Huu Quang ◽  
Dang Quoc Cuong ◽  
Nguyen Hong Linh ◽  
Nguyen Van. Tuan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Removal Rate ◽  
Cutting Speed ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Pareto Chart ◽  
Multi Objective ◽  
Input Parameters ◽  
Experimental Process

In this paper, a study on multi-objective optimization of the cylindrical grinding process is presented. The experimental material used in this study is X12M steel. The two output parameters of the grinding process considered in this study are surface roughness and material removal rate (MRR). The cutting mode parameters including cutting speed, feed rate, and cutting depth have been selected as input parameters of the experimental process. Experimental matrix by Taguchi method has been used to design a matrix with 27 experiments. Analysis of experimental results by Pareto chart has determined the effect of input parameters on output parameters. The Data Envelopment Analysis-based Ranking (DEAR) method has been applied to determine the values of input parameters to simultaneously ensure the two criteria of minimum surface roughness and maximum MRR. Finally, the development direction for further studies has also been recommended in this study.

scholarly journals COMBINATION OF TAGUCHI METHOD, MOORA AND COPRAS TECHNIQUES IN MULTI-OBJECTIVE OPTIMIZATION OF SURFACE GRINDING PROCESS

2020 ◽  
pp. 1-9
Author(s):  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Optimization Problem ◽  
Removal Rate ◽  
Optimization Techniques ◽  
Surface Grinding ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Multi Objective

This study presentes a combination method of several optimization techniques and Taguchi method to solve the multi-objective optimization problem for surface grinding process of SKD11 steel. The optimization techniques that were used in this study were Multi-Objective Optimization on basis of Ratio Analysis (MOORA) and Complex Proportional Assessment (COPRAS). In surface grinding process, two parameters that were chosen as the evaluation creterias were surface roughness (Ra) and material removal rate (MRR). The orthogonal Taguchi L16 matrix was chosen to design the experimental matrix with two input parameters namely workpiece velocity and depth of cut.  The two optimization techniques that mentioned above were applied to solve the multi-objective optimization problem in the grinding process. Using two above techniques, the optimized results of the cutting parameters were the same. The optimal workpiece velocity and cutting depth were 20 m/min and 0.02 mm. Corresponding to these optimal values of the workpiece velocity and cutting depth, the surface roughness and material removal rate were 1.16 µm and 86.67 mm3/s. These proposed techniques and method can be used to improve the quality and effectiveness of grinding processes by reducing the surface roughness and increasing the material removal rate.

Multi-Objective Optimization of Surface Roughness and Cutting Forces in Hard Milling Using Taguchi and Response Surface Methodology

2018 ◽  
Vol 773 ◽  
pp. 220-224 ◽  
Author(s):  
Ngoc Chien Vu ◽  
Shyh Chour Huang ◽  
Huu That Nguyen
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Taguchi Method ◽  
Response Surface ◽  
Cutting Forces ◽  
Machining Process ◽  
Process Conditions ◽  
Multi Objective Optimization ◽  
Hard Milling ◽  
Multi Objective

Cutting forces and surface roughness are important output parameters affecting the machining performance and quality of any machined surface in hard milling. In order to obtain the best surface quality and highest productivity, the input-cutting parameters need be considered and chosen properly whenever hard milling is involved. Therefore, in this paper, an attempt is made to conduct the multi-objective optimization of the surface roughness (Ra) and the resultant cutting force (Ft) in hard milling of SKD61 steel by Taguchi method and Response Surface Methodology (RSM). Values of the input parameters for milling tests are chosen through the stability lobe diagram of a machine tool simulated by the use of Cutpro software. The Taguchi method is used for designing all of the milling experiments. The values of Ra and Ft are measured by a Surftest SJ-400 and a dynamometer, respectively, and then analysis of variance is conducted to find out the effect of machining process conditions on Ra and Ft. In order to get the low Ft and Ra, a multi-objective optimization is implemented with the use of the desirability function. The results reveal that the optimized machining conditions for Ra and Ft are a cutting speed of 100 m/min, a feed rate of 0.015 mm/tooth, and a depth of cut of 0.44 mm, with predicted Ra of 0.206 µm and Ft of 66.58 N.

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