scholarly journals Multi-Objective Optimization and Predictive Modeling of Surface Roughness and Material Removal Rate in Turning Using Grey Relational and Regression Analysis

2012 ◽  
Vol 38 ◽  
pp. 1606-1627 ◽  
Author(s):  
Ashok Kumar Sahoo ◽  
Achyuta Nanda Baral ◽  
Arun Kumar Rout ◽  
B.C. Routra
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Predictive Modeling ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Grey Relational

scholarly journals Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

2020 ◽  
Vol 41 (1) ◽  
pp. 34-49
Author(s):  
Sandip B. Gunjal ◽  
Padmakar J. Pawar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Bee Colony ◽  
Abrasive Finishing

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario.

scholarly journals Multi-objective optimization of drilling parameters for orthopaedic implants

2020 ◽  
pp. 002029402094712
Author(s):  
Parvesh Antil ◽  
Sundeep Kumar Antil ◽  
Chander Prakash ◽  
Grzegorz Królczyk ◽  
Catalin Pruncu
Keyword(s):  
Experimental Study ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

Titanium (Ti) and its alloys have gained immense popularity as biomaterials in recent years. Their excellent specific strength makes them outstanding materials for orthopaedic applications. However, in the orthopaedic application, precise micro-drilling (i.e. implants inserts) is required, which is very challenging for these materials. To overcome this issue, the present research proposes an experimental study corroborated with a multi-objective optimization by simulating the drilling under electric discharge machining of Ti-6Al-4V. Taguchi’s methodology–based L9 orthogonal array was used for the experimental study. Voltage, current, pulse on and pulse off were used as the input parameters for the experimental investigation. In order to achieve suitable precise drilling, the material removal rate and surface finish were used as response parameters. Here, by optimizing parameters of the precise drilling, it is possible to obtain high material removal rate and better surface finish simultaneously. The Grey relational analysis was adopted to analyse the output quality characteristics. The optimized results generated through the Grey relational analysis are highly accurate with respect to the experimental outcomes.

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.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF TURNING PROCESS USING A COMBINATION OF TAGUCHI AND VIKOR METHODS

2021 ◽  
pp. 1-6
Author(s):  
Nhu-Tung Nguyen ◽  
Van Thien Nguyen ◽  
Dung Hoang Tien ◽  
Duc Trung Do
Keyword(s):  
Cutting Force ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Optimization Problem ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Nose Radius ◽  
Multi Objective ◽  
Cutting Velocity

This study presents the solving process of the multi-objective optimization problem using VIKOR method (Vlse Kriterijumska Optimizacija Kompromisno Resenje, in Serbian) when turning the EN 10503 steel. The cutting velocity, feed rate, depth of cut, and insert nose radius were chosen as the input parameters with three levels of each parameter. Taguchi L9 orthogonal array was used to design the experimental matrix with nine experiments. By the combination of Taguchi and VIKOR methods, the multi-objective optimization problem was successfully solved with optimal values (cutting velocity of 78.62 m/min, feed rate of 0.08 mm/rev, cutting depth of 0.5 mm, and insert nose radius of 0.4 mm. Using these the optimized input parameters, the surface roughness, cutting force and vibration component amplitudes (in X, Y, Z directions), and material removal rate (MRR) were 0.621 µm, 191.084 N, 300.162 N, 51.727 N, 4.465 µm, 7.492 µm, 10.118 µm, and 60.009 mm3/s, respectively. This proposed method could be used to improve the quality and effectiveness of turning processes by improving the surface quality, reducing the cutting force and vibration amplitudes, and increasing the material removal rate.

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