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 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 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.

scholarly journals Tool Wear Rate Prediction by using Optimization Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 1271-1277
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Optimization Techniques ◽  
Machining Process ◽  
Machining Parameters ◽  
Tungsten Carbides ◽  
Electro Discharge Machining ◽  
Pulse On Time

Electro discharge machining is a non-traditional machining process used for machining hard-to-machine materials, such as various grades of titanium alloys, heat-treated alloy steels, composites, tungsten carbides, and so forth. These materials are hard to machine with customary machining procedures like drilling, milling and hence electro-discharge machining is used to machine such materials to get better quality and efficiency. These materials are generally utilized in current industries like die making industries, aeronautics, nuclear industries, and medical fields. This type of machining is thermalbased, and machining takes place due to repetitive electric sparks that generate between workpiece and tool. Both tools and workpieces are inundated in a dielectric liquid, which has two primary functions. In the first place, it behaves like a medium between the work metal and the tool. Second, it is a flushing agent to expel the machined metal from the machined zone. Machining parameters like a pulse on time, current, wire feed the tool and gap voltage affect the output responses like surface roughness and material removal rate. The material removal rate is a significant parameter that determines machining efficiency. Surface roughness is also a vital parameter that decides machining quality. A lot of research has been conducted to determine the optimum parameters for obtaining the best results. In the present work, a comprehensive review of different types of EDM and the effect of various machining parameters on the surface roughness, material removal rate, and other response parameters has been done.

Effect of Dressing Parameters on Material Removal Rate when Surface Grinding SKD11 Tool Steel

2021 ◽  
Vol 1020 ◽  
pp. 60-67
Author(s):  
Thi Hong Tran ◽  
Thanh Danh Bui ◽  
Nguyen Anh Tuan ◽  
Vu Trung Tuyen ◽  
Luu Anh Tung ◽  
...  
Keyword(s):  
Tool Steel ◽  
Material Removal Rate ◽  
Material Removal ◽  
Optimization Problem ◽  
Removal Rate ◽  
Optimal Solution ◽  
Surface Grinding ◽  
Machining Process ◽  
The Real ◽  
The Difference

Nowadays, surface grinding is one of the most common of metal finishing methods. The efficiency of this process is affected by the so-called process parameters such as dressing feed rate (S), rough dressing depth (ar), rough dressing times (nr), fine dressing depth (af), fine dressing times (nf), and non-feeding dressing (nnon). etc. In this paper, the optimization of dressing parameters in surface grinding SKD11 tool steel is studied. The aim of the study is to find the most appropriate value set of dressing parameters to maximize the material removal rate (MRR). In order to solve the problem, the Taguchi method is used. Based on an orthogonal array L16(44x22), sixteen experiments have been conducted. By analyzing the experimental results, an optimal solution of such optimization problem has been solved, presenting the most appropriate dressing parameters as follows: ar = 0.015 mm, nr = 2 times, af = 0.005 mm, nf = 0 times, nnon = 0 times, S = 1.6 m/min. The discovered technology mode has been applied to the real machining process and the outcome shows out a much better result in comparison with default setting modes, that the difference between the model values and the real values of the roughness average is controlled within 3.87% of the ranges.

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