Multiobjective optimisation of cutting parameters in machining –a sustainable approach

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

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Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

Engineering and Technology Journal ◽

10.30684/etj.v38i8a.928 ◽

2020 ◽

Vol 38 (8A) ◽

pp. 1143-1153

Author(s):

Yousif K. Shounia ◽

Tahseen F. Abbas ◽

Raed R. Shwaish

Keyword(s):

Surface Roughness ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Feed Rate ◽

Cutting Parameters ◽

Spindle Speed ◽

Depth Of Cut ◽

Non Linear ◽

Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

Engineering and Technology Journal ◽

10.30684/etj.v38i10a.1124 ◽

2020 ◽

Vol 38 (10A) ◽

pp. 1489-1503

Author(s):

Marwa Q. Ibraheem

Keyword(s):

Genetic Algorithm ◽

Tool Wear ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Cutting Speed ◽

Cutting Parameters ◽

Depth Of Cut ◽

Optimal Value ◽

Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

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