Regression of surface roughness
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This paper presents optimization results of the Al6061 surface roughness in turning ultra-precision based on the central composite design method (CCD) and the grey wolf optimization algorithm (GWO). The experimental matrix is built with three independent variables including spindle speed, feed rate and depth of cut. With the experimental data, the roughness regression model is established.
7p 
viaburame
14-03-2025
3
1
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This experimental study investigated the effects of milling conditions on the finished surface roughness. With four controllable factors-three levels (cutting velocity, feedrate, radial depth of cut, and cutter helix angle), the nineteen experiments were performed with performance measurements of surface roughness.
10p lucastanguyen
01-06-2020
39
2
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(BQ) In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimization results have been used for identifying the machining conditions.
10p xuanphuongdhts
27-03-2017
44
2
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(BQ) The aim of this research is to show the influence of the hardness of the alloy steel on the material removal rate and on the workpiece surface roughness.
5p xuanphuongdhts
27-03-2017
25
2
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