scholarly journals Experimental Investigation of Drilling Small Hole on Duplex Stainless Steel (SS2205) Using EDM

2020 ◽  
Vol 22 (1) ◽  
pp. 285-294
Author(s):  
S. Rajaram ◽  
G. RajKumar ◽  
R. Balasundaram ◽  
D. Srinivasan
Keyword(s):  
Genetic Algorithm ◽  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Metal Removal ◽  
Removal Rate ◽  
Electrical Discharge Machine ◽  
Operating Parameter ◽  
Metal Removal Rate ◽  
Spark Gap ◽  
Electric Pressure

AbstractThis work investigates drilling of small holes of Ø 3 mm on duplex Stainless Steel. Its machinability index is very low (0.66) as compared to other steels, hence Electrical Discharge Machine is used. The input parameters are Current, Spark Gap & Di electric Pressure. Each input parameter is considered for 3 levels. Therefore total number of experiments is 3×3×3 – 27. To reduce the number of runs, Taguchi L9 orthogonal array is used, which is having advantage of maximum and minimum trial runs in its design. The output response is metal removal rate. To find the best operating parameter, the regression model of ANOVA is given to input of MAT Lab-Genetic Algorithm. The experimental results indicated that models are significant. The test result indicated that the contributions of current is 42.42%, Di electric pressure is 35.36% and Spark gap is 1.93% on metal removal rate. From Genetic Algorithm it is observed among three levels of factors, lower value of current and Di electric pressure produced maximum metal removal rate. The SS 2205 has wide variety of applications such as high pressure components, control valves etc., which are having large number of components to it. Hence performing micro holes on such high hardness alloy is useful.

scholarly journals Experimental Investigation of Drilling Small Hole on Duplex Stainless Steel (SS 2205) Using EDM

2019 ◽  
Vol 23 (1) ◽  
pp. 98-102 ◽  
Author(s):  
S. Rajaram ◽  
G. Rajkumar ◽  
R. Balasundaram ◽  
D. Srinivasan
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Metal Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Input Parameter ◽  
Operating Parameter ◽  
High Hardness ◽  
Spark Gap ◽  
Machinability Index

Abstract This work investigates drilling of small holes of 3 mm in diameter on duplex stainless steel. Its machinability index is very low (0.66) when compared to other steels; hence, electrical discharge machining is used. The input parameters are current, spark gap, and dielectric pressure. Each input parameter is considered for three levels. Therefore, the total number of experiments is 3 × 3 × 3 = 27. To reduce the number of runs, Taguchi L9 orthogonal array is used, which have the advantage of maximum and minimum trial runs in its design. The output response is metal removal rate (MRR). To find the best operating parameter, the regression model generated using ANOVA is given as input for MATLAB genetic algorithm (GA). The experimental results indicated that models are significant. The test result indicated that the contribution of the current on the MRR is 42.42%, dielectric pressure is 35.36%, and spark gap is 1.93%. From GA, it is observed that among these three factors, lower value of current and dielectric pressure produced the maximum MRR. The SS 2205 has wide variety of applications, such as high pressure components and control valves, which have large number of components attached to it. Hence, performing micro holes on such high hardness alloy is useful.

surface roughness,metal removal Effect of Wire Diameter, Feeding Rate, Pulse (on/off) Time on Surface Roughness and Metal Removal Rate for Cr-Mo Steel (SCM425H) During Wire Electrical Discharge Machine (WEDM) Cutting Operation

2020 ◽  
Vol 38 (6A) ◽  
pp. 854-860
Author(s):  
Saad K. Shather ◽  
Sami A. Hammood ◽  
Noor Al-Huda A. Hussain ◽  
Noor H. Hasson
Keyword(s):  
Surface Roughness ◽  
Electrical Discharge ◽  
Feeding Rate ◽  
Metal Removal ◽  
Removal Rate ◽  
Electrical Discharge Machine ◽  
Wire Diameter ◽  
Metal Removal Rate ◽  
Discharge Machine ◽  
Off Time

Increase the demand to produce complex shapes with high quality and dimensional accuracy such as production aerospace, cars, die sinking has been leading to increase the demand to use the non- traditional cutting operations such as wire electro-discharge machine (WEDM) rather than using the traditional operations. An idea to understand the effect of wire diameter, wire feed, pulsing (on/off) time on surface roughness, and metal removal rate of Cr-Mo steel during wire electrical discharge machining was investigated. Two Steel alloy samples with dimensions of (60 x50 x 20)mm were cut into four rectangular spaces with (5x10x20)mm at one side of each sample using wire cut (EDM) machine with a wire diameter of 0.25 mm and feeding rate 2 m/min for sample 1 and a 0.3 mm diameter and 3 m/min feeding rate for sample 2. Pulse (on, off) time was (110, 50), (112, 52), (115, 55), (116, 57) corresponds to space 1, space 2, space 3, and space 4 in both steel block. Surface roughness and metal removal rate measurements were estimated. The results showed that wire diameter, feeding rate, and pulse (on, off) time is proportional with metal removal rate, while reversed with surface roughness. The wire diameter of 0.3 mm and a feeding rate of 3m/min enhanced better surface quality and productivity. Pulse (on, off) time is the most effective parameter. Best duration time was recorded at the values (116, 57).

Parameter Optimization of Milling Ti6Al4V Using GA Approach

2010 ◽  
Vol 426-427 ◽  
pp. 1-4 ◽  
Author(s):  
Feng Xu ◽  
Jian Jun Zhu ◽  
Xin Wu ◽  
Xiao Jun Zang ◽  
Dun Wen Zuo
Keyword(s):  
Genetic Algorithm ◽  
Titanium Alloy ◽  
Parameter Optimization ◽  
Cutting Tool ◽  
Metal Removal ◽  
Removal Rate ◽  
Metal Removal Rate ◽  
Machined Surface ◽  
Milling Parameters ◽  
Machined Surface Roughness

The research was carried out on the parameter optimization of milling titanium alloy in this paper. The cutting models including cutting force, tool life and machined surface roughness are obtained by orthogonal array experiments. The maximum metal removal rate, MRR is selected as objective function. The constraints related to machine tool, workpiece, cutting tool and other machining situations are presented in details. Genetic algorithm is used to search for the optimum milling parameters for the maximum metal removal rate of titanium alloy. The optimization results show the optimization system can improve the productivity of milling Ti6Al4V obviously.

Use of artificial neural network and multi-objective genetic algorithm approach to predict and ascertain stable cutting zone in conventional turning process

2018 ◽  
Vol 49 (5) ◽  
pp. 191-214 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Metal Removal ◽  
Removal Rate ◽  
Metal Removal Rate ◽  
Multi Objective Genetic Algorithm ◽  
Cutting Zone ◽  
Artificial Neural ◽  
Chatter Index

This article is focused on the investigation of stable cutting zone in turning operation. Experiments have been conducted to acquire raw chatter signals. Generally, raw chatter signals are contaminated with ambient noise. Wavelet transform has been used for pre-processing and denoising these signals. In order to predict the severity of tool chatter, a new parameter denoted as chatter index has been evaluated considering the aforesaid denoised signals. In the present work, mathematical models have been developed for chatter index and metal removal rate using feedforward backpropagation–based artificial neural network considering three activation functions: TANSIG, LOGSIG and PURELIN. Furthermore, multi-objective genetic algorithm technique has been applied to evaluate stable cutting zones with maximized metal removal rate. TANSIG activation function found to be best option to achieve the aforesaid objectives. Good correlation between the artificial neural network predicted results and experimental ones validate the developed technique.

Ensembled local mean decomposition and genetic algorithm approach to investigate tool chatter features at higher metal removal rate

2020 ◽  
pp. 107754632097115
Author(s):  
Pankaj Gupta ◽  
Bhagat Singh
Keyword(s):  
Genetic Algorithm ◽  
Prediction Models ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Metal Removal Rate ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Chatter Index ◽  
Selection Of

Improper selection of cutting parameters leads to regenerative chatter and loss in productivity. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate. Chatter signals generated during the turning of Al6061-T6 have been acquired using a microphone. Stability lobes diagram has been plotted to access the stability regime. Further, to study the effect of feed rate on stability, the recorded signals have been processed using local mean decomposition signal processing technique, followed by the selection of dominating product functions using Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Prediction models of chatter index and metal removal rate have been developed. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher metal removal rate. Finally, obtained stable range has been validated by performing more experiments.

Quantification of tool chatter and metal removal rate using wavelet denoising and statistical approach

2018 ◽  
Vol 49 (2) ◽  
pp. 62-81 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Statistical Significance ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Chatter Index ◽  
Tool Chatter

Tool chatter is an unavoidable phenomenon encountered in machining processes. Acquired raw chatter signals are contaminated with various types of ambient noises. Signal processing is an efficient technique to explore chatter as it eliminates unwanted background noise present in the raw signal. In this study, experimentally recorded raw chatter signals have been denoised using wavelet transform in order to eliminate the unwanted noise inclusions. Moreover, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate has been ascertained experimentally. Furthermore, in order to quantify the chatter severity, a new parameter called chatter index has been evaluated considering aforesaid denoised signals. A set of 15 experimental runs have been performed using Box–Behnken design of experiment. These experimental observations have been used to develop mathematical models for chatter index and metal removal rate considering response surface methodology. In order to check the statistical significance of control parameters, analysis of variance has been performed. Furthermore, more experiments are conducted and these results are compared with the theoretical ones in order to validate the developed response surface methodology model.

Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Rajesh Kumar Bhushan
Keyword(s):  
Tool Wear ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Al Alloy ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Nose Radius ◽  
Multiresponse Optimization

Optimization in turning means determination of the optimal set of the machining parameters to satisfy the objectives within the operational constraints. These objectives may be the minimum tool wear, the maximum metal removal rate (MRR), or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are the cutting speed, feed rate, depth of cut, and nose radius. The optimum set of these four input parameters is determined for a particular job-tool combination of 7075Al alloy-15 wt. % SiC (20–40 μm) composite and tungsten carbide tool during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate. The regression models, developed for the minimum tool wear and the maximum MRR were used for finding the multiresponse optimization solutions. To obtain a trade-off between the tool wear and MRR the, a method for simultaneous optimization of the multiple responses based on an overall desirability function was used. The research deals with the optimization of multiple surface roughness parameters along with MRR in search of an optimal parametric combination (favorable process environment) capable of producing desired surface quality of the turned product in a relatively lesser time (enhancement in productivity). The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum the MRR, which correspond to a satisfactory overall desirability.

Optimization of MRR and Surface Roughness of AlMg3 (AA5754) Alloy in CNC Lathe Machine by Using Taguchi Method

2018 ◽  
Vol 877 ◽  
pp. 110-117 ◽  
Author(s):  
Poornesh Kumar Chaturvedi ◽  
Harendra Kumar Narang ◽  
Atul Kumar Sahu
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Metal Removal ◽  
Removal Rate ◽  
Surface Condition ◽  
Point Of View ◽  
Depth Of Cut ◽  
Metal Removal Rate ◽  
Cnc Lathe ◽  
Lathe Machine

Quality of the product is the major concern in manufacturing industries from customers as well as producers point of view. There are number of factors in the product such as surface condition, height, weight, length, width etc., which may be consider for the measurement of the quality. Surface roughness and Metal Removal Rate (MRR) are the two main outcomes on which numerous researchers have applied different approaches for several years to get optimum results. In this study, Taguchi Method is applied for getting optimum parameters settings for Surface roughness and Metal Removal Rate (MRR) in case of turning AlMg3 (AA5754) in CNC Lathe machine, which is an aluminum alloy having diameter 20 mm and length 100 mm. The three parameters i.e. spindle speed, feed rate and depth of cut with 3 levels are taken as the process variables and the working ranges of these parameters for conducting experiments are selected based on Taguchi’s L9 Orthogonal Array (OA) design. To analyze the significant process parameters; main effect plots for data means and for S/N ratio are generated using Minitab statistical software.

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