Prediction of Surface roughness & Material Removal Rate for machining of P20 Steel in CNC milling using Artificial Neural Networks

2018 ◽  
Vol 5 (9) ◽  
pp. 18376-18382 ◽  
Author(s):  
M. Vishnu Vardhan ◽  
G. Sankaraiah ◽  
M. Yohan
2011 ◽  
Vol 335-336 ◽  
pp. 535-540 ◽  
Author(s):  
Veluswamy Muthuraman ◽  
Raju Ramakrishnan

The prediction of optimal machining conditions for required surface roughness and material removal rate (MRR) plays a very significant role in process planning of wire electrical discharge machining (WEDM). Artificial neural networks (ANN) are widely applied to predict the performance characteristics of complex machining process like WEDM very accurately. This present work deals with the features of cutting operation by WEDM of tungsten carbide- cobalt composite(WC – Co) and an artificial neural networks(ANN) model in terms of machining parameters, developed to predict surface roughness(Ra) and material removal rate (MRR).The experiment was planned as per Taguchi’s L 27 orthogonal array. The predictive capacity of the models was validated. The test results indicate that the proposed models could adequately describe the performance indicators with the limits of the factors that are being investigated. Finally the accuracy of the developed ANN model was compared to the experimental values. It was observed that the proposed ANN model is good.


In many material processing and manufacturing industries quality and productivity are two important requirements but these are more antithetical criteria in any machining operations. So, it is vital to optimize the productivity and quality simultaneously. The main objective of this paper is to optimize the process parameters of drilling operations such as cutting speed, feed, and point angle on aluminum alloy 7075. Al 7075 is one of the multifunctional materials in various applications. Taguchi is mostly used for data analysis and optimization of process parameters for getting maximum material removal rate and least surface roughness factor. Machining operations were conducted on CNC milling machine. The number of drilling experiments was performed on aluminum 7075 using HSS drill bit on CNC milling machine. The investigation of variance (ANOVA) was engaged to find the most notable control factors affecting the material removal rate & surface roughness. The conclusions of present work were drawn from several experimental trails; it was found that at the 9th experimental trail, point angle was most significant x factor for surface roughness and feed is the most affecting factor for material removal rate.


Author(s):  
Amritpal Singh ◽  
Rakesh Kumar

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.


2020 ◽  
Vol 38 (9A) ◽  
pp. 1406-1413
Author(s):  
Yousif Q. Laibia ◽  
Saad K. Shather

Electrical discharge machining (EDM) is one of the most common non-traditional processes for the manufacture of high precision parts and complex shapes. The EDM process depends on the heat energy between the work material and the tool electrode. This study focused on the material removal rate (MRR), the surface roughness, and tool wear in a 304 stainless steel EDM. The composite electrode consisted of copper (Cu) and silicon carbide (SiC). The current effects imposed on the working material, as well as the pulses that change over time during the experiment. When the current used is (8, 5, 3, 2, 1.5) A, the pulse time used is (12, 25) μs and the size of the space used is (1) mm. Optimum surface roughness under a current of 1.5 A and the pulse time of 25 μs with a maximum MRR of 8 A and the pulse duration of 25 μs.


2020 ◽  
Vol 38 (9A) ◽  
pp. 1352-1358
Author(s):  
Saad K. Shather ◽  
Abbas A. Ibrahim ◽  
Zainab H. Mohsein ◽  
Omar H. Hassoon

Discharge Machining is a non-traditional machining technique and usually applied for hard metals and complex shapes that difficult to machining in the traditional cutting process. This process depends on different parameters that can affect the material removal rate and surface roughness. The electrode material is one of the important parameters in Electro –Discharge Machining (EDM). In this paper, the experimental work carried out by using a composite material electrode and the workpiece material from a high-speed steel plate. The cutting conditions: current (10 Amps, 12 Amps, 14 Amps), pulse on time (100 µs, 150 µs, 200 µs), pulse off time 25 µs, casting technique has been carried out to prepare the composite electrodes copper-sliver. The experimental results showed that Copper-Sliver (weight ratio70:30) gives better results than commonly electrode copper, Material Removal Rate (MRR) Copper-Sliver composite electrode reach to 0.225 gm/min higher than the pure Copper electrode. The lower value of the tool wear rate achieved with the composite electrode is 0.0001 gm/min. The surface roughness of the workpiece improved with a composite electrode compared with the pure electrode.


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