Multi-objective Optimization for Minimum Surface Roughness and Maximum Wheel Life When External Cylindrical Grinding SKD11 Steel

Cellulose based hybrid composites are gaining popularity in the growing green communities. With extensive studies and increasing applications for future advancement, the need for an accurate and reliable guidance in machining this type of composites has increased enormously. Smooth and defect free machined surface are always the ultimate objectives. The present work deals with the study of machining parameters (i.e. spindle speed, feed rate and depth of cut) and their effects on machining performance (i.e. surface roughness and delamination) to establish an optimized setup of machining parameters in achieving multi objective machining performance. Cellulose based hybrid composites consist of jute (a bast fiber) and glass fiber embedded in polyester resins. Response Surface Methodology (RSM) using Box-Behnken Design (BBD) was chosen as the design of experiment approach for this study. Based on that experimental approach, 17 experimental runs were conducted. Mathematical model for each response was developed based on the experimental data. Adequacy of the models were analyzed statistically using Analysis of Variance (ANOVA) in determining the significant input variables and possible interactions. The multi objective optimization was performed through numerical optimization, and the predicted results were validated. The agreement between the experimental and selected solution was found to be strong, between 95% to 96%, thus validating the solution as the optimal machining condition. The findings suggest that feed rate was the main factor affecting surface roughness and delamination .

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Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide

Precision Engineering ◽

10.1016/j.precisioneng.2014.10.014 ◽

2015 ◽

Vol 40 ◽

pp. 131-138 ◽

Cited By ~ 19

Author(s):

Sanket N. Bhavsar ◽

S. Aravindan ◽

P. Venkateswara Rao

Keyword(s):

Surface Roughness ◽

Material Removal Rate ◽

Material Removal ◽

Focused Ion Beam ◽

Removal Rate ◽

Ion Beam ◽

Cemented Carbide ◽

Micro Milling ◽

Multi Objective Optimization ◽

Multi Objective

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Optimization Study on Turning Process by Using Taguchi-copras Method

E3S Web of Conferences ◽

10.1051/e3sconf/202130901010 ◽

2021 ◽

Vol 309 ◽

pp. 01010

Author(s):

Do Duc Trung ◽

Nguyen Huu Quang ◽

Tran Quoc Hoang ◽

Cao The Anh ◽

Nguyen Hong Linh ◽

...

Keyword(s):

Surface Roughness ◽

Removal Rate ◽

Multi Objective Optimization ◽

Turning Process ◽

Nose Radius ◽

Multi Objective ◽

Optimization Study ◽

Input Parameters ◽

Cutting Velocity ◽

Copras Method

In this article, a multi-objective optimization of turning process study is presented. Two output parameters of the turning process taken into consideration are surface roughness and Material Removal Rate (MRR). Taguchi method has been applied to design the experimental matrix with four input parameters including nose radius, cutting velocity, feed rate and cutting depth. Copras method has been employed to solve the multi-objective optimization problem. Finally, the optimal values of the input parameters have been determined to simultaneously ensure the two criteria of the minimum surface roughness and the maximum MRR.

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Multi-objective optimization of SKD11 Steel Milling Process by Reference Ideal Method

International Journal of Geology ◽

10.46300/9105.2021.15.1 ◽

2021 ◽

Vol 15 ◽

pp. 1-16

Author(s):

Do Duc Trung

Keyword(s):

Surface Roughness ◽

Cutting Force ◽

Cutting Speed ◽

Milling Process ◽

Multi Objective Optimization ◽

Small Surface ◽

Multi Objective ◽

Input Parameters ◽

Machining Productivity ◽

Ideal Method

For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is SKD11 steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study.

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

Engineering review ◽

10.30765/er.1511 ◽

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.

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