Optimization of Laser Cut Quality Characteristics Considering Material Removal Rate Based on Pareto Concept

2014 ◽  
Vol 657 ◽  
pp. 216-220
Author(s):  
Miloš Madić ◽  
Miroslav Radovanović ◽  
Laurenţiu Slătineanu ◽  
Oana Dodun
Keyword(s):  
Mathematical Models ◽  
Material Removal Rate ◽  
Laser Cutting ◽  
Material Removal ◽  
Removal Rate ◽  
Quality Characteristics ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cut Quality ◽  
Pareto Fronts

Stainless steels are one of the most important engineering materials widely used in the industry. This paper presents multi-objective optimization of CO2 laser cutting of stainless steel considering different cut quality characteristics and material removal rate (MRR). Laser cutting experiment trials were conducted based on Taguchis L27 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Using obtained experimental data, six mathematical models for the prediction of surface roughness, kerf width, kerf taper angle, width of heat affected zone, dross height and MRR were developed using artificial neural network (ANN). The developed mathematical models were taken as objective functions for the multi-objective optimization using genetic algorithm based on Pareto concept. As a result of multi-objective optimization, five 2-D Pareto fronts were generated covering all combinations of cut quality characteristics and MRR. It was observed that the mathematical relationships in the Pareto fronts between MRR and cut quality characteristics are in some cases linear and in another nonlinear.

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 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.

Multi-Objective Optimization of CNC Turning Machining Parameters

2014 ◽  
Vol 1016 ◽  
pp. 172-176 ◽  
Author(s):  
Sharad Kumar Pradhan ◽  
Surendra Kumar Saini
Keyword(s):  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Cnc Turning ◽  
Multi Objective ◽  
Machining Parameter

An experimental investigation into CNC turning operation on Brass C36000 alloy as work piece material which is widely used for various industrial applications is performed. Multi objective optimization is carried out to find out the influencing machining parameters among spindle speed (rpm), feed (mm per revolution) and depth of cut (mm) for CNC turning of Brass C36000 alloy with surface finish and Material Removal Rate as performance parameters using Taguchi method. Taguchi orthogonal array [L27(33)] is used for the experimental design. All experiments are conducted using EMCO Concept Turn 250 machine tool with carbide insert cutting tool. The optimization result shows that feed is the most significant turning machining parameter for surface roughness while depth of cut has high influence on material removal rate followed by spindle speed during CNC turning of Brass C36000 alloy. Above results is further validated using ANOVA approach.

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