Experimental study and optimization of cutting parameters in machining of super alloy with hybrid ultrasonic method

2017 ◽  
Vol 5 (3) ◽  
pp. 199-216 ◽  
Author(s):  
Dipesh Popli ◽  
Meenu Gupta
Keyword(s):  
Experimental Study ◽  
Ultrasonic Method ◽  
Cutting Parameters ◽  
Optimization Of Cutting Parameters ◽  
Super Alloy

Optimization of Cutting Parameters Considering Surface Roughness When Milling AISI 304 Steel Using Coated Wc-Co and Tialn / Wc-Co Coated

2019 ◽  
Author(s):  
Murilo Pereira Lopes ◽  
Jose Rubens Gonçalves Carneiro ◽  
Gilmar Cordeiro da Silva ◽  
Carlos Eduardo Santos ◽  
Ítalo Bruno dos Santos
Keyword(s):  
Surface Roughness ◽  
Cutting Parameters ◽  
Aisi 304 ◽  
Aisi 304 Steel ◽  
Optimization Of Cutting Parameters

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

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.

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

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.

An Experimental Study of Cutting Force on Large Cutting Depth Turning Titanium Alloy with CBN Tool

2014 ◽  
Vol 800-801 ◽  
pp. 237-240
Author(s):  
Li Fu Xu ◽  
Ze Liang Wang ◽  
Shu Tao Huang ◽  
Bao Lin Dai
Keyword(s):  
Experimental Study ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Cbn Tool ◽  
Cutting Experiment ◽  
Rough Turning

In this paper, the cutting experiment was used to study the influence of various cutting parameters on cutting force when rough turning titanium alloy (TC4) with the whole CBN tool. The results indicate that among the cutting speed, feed rate and cutting depth, the influence of the cutting depth is the most significant on cutting force; the next is the feed rate and the cutting speed is at least.

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