scholarly journals Optimization of Process Parameter in Wire Electrical Discharge Machining of H11 Tool Steel Using Response Surface Methodology

2021 ◽  
Vol 9 (12) ◽  
pp. 2150-2157
Author(s):  
Pratik R. Vishwasrao
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Tool Steel ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Surface ◽  
Dimensional Accuracy ◽  
Machining Parameters ◽  
Optimum Parameters ◽  
Conventional Machining

Abstract: Conventional machining is now being superseded by non-conventional machining to cope up with increased demand of machining of complex shapes with high surface finish machining and dimensional accuracy. Wire electric discharge machining (WEDM) is electro-thermal principle based nontraditional machining, widely used for machining of electrically conductive materials. This paper summarized, the parametric influence of Pulse-on duration (Ton), Pulse-off duration (Toff) and Pulse peak current (Ip) on material removal rate (MRR), surface roughness (SR) of H11 tool steel. Response surface methodology (RSM) is used for modelling and optimization. ANOVA has been carried out to identify importance of the machining parameters on the performance characteristics considered. Further the verification experiment has been carried out to confirm the performance of optimum parameters. The results from this study will be useful for selecting appropriate set of process parameters to WEDM machining of H11 tool steel.

OPTIMIZATION OF ELECTRICAL DISCHARGE MACHINING PARAMETERS OF SISIC THROUGH RESPONSE SURFACE METHODOLOGY

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Abdul Azeez Abdu Aliyu ◽  
Jafri Mohd Rohani ◽  
Ahmad Majdi Abdul Rani ◽  
Hamidon Musa
Keyword(s):  
Silicon Carbide ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Industrial Applications ◽  
Machining Parameters ◽  
Difficult Time ◽  
Pulse On Time

In recent years, researchers have demonstrated increases interest in studies involving silicon carbide (SiC) materials due to several industrial applications. Extreme hardness and high brittleness properties of SiC make the machining of such material very difficult, time consuming and costly. Electrical discharge machining (EDM) has been regarded as the most viable method for the machining of SiC. The mechanism of EDM process is complex. Researchers have acknowledged a challenge in generating a model that accurately describes the correlation between the input parameters and the responses. This paper reports the study on parametric optimization of siliconized silicon carbide (SiSiC) for the following quality responses; material removal rate (MRR), tool wear ratio (TWR) and surface roughness (Ra). The experiments were planned using Face centered central composite design. The models which related MRR, TWR and Ra with the most significant factors such as discharge current (Ip), pulse-on time (Ton), and servo voltage (Sv) were developed. In order to develop, improve and optimize the models response surface methodology (RSM) was used. Non-linear models were proposed for MRR and Ra while linear model was proposed for TWR. The margin of error between predicted and experimental values of MRR, TWR and Ra are found within 6.7, 5.6 and 2.5% respectively. Thus, the excellent reproducibility of this experimental study is confirmed, and the models developed for MRR, TWR and Ra are justified to be valid by the confirmation tests.

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.

scholarly journals Prediction of Machining Characteristics of Hybrid Composites Using Response Surface Methodology Approach

2018 ◽  
Vol 7 (3.1) ◽  
pp. 162 ◽  
Author(s):  
Ramanan. G ◽  
Rajesh Prabha.N ◽  
Diju Samuel.G ◽  
Jai Aultrin. K. S ◽  
M Ramachandran
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Hybrid Composites ◽  
Removal Rate ◽  
Cutting Speed ◽  
Chemical Properties ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Optimal Parameters ◽  
Rsm Technique

This manuscript presents the influencing parameters of CNC turning conditions to get high removal rate and minimal response of surface roughness in turning of AA7075-TiC-MoS2 composite by response surface method. These composites are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by RSM technique. Turning process is studied by response surface methodology design of experiment. The optimal parameters were predicted by RSM technique. The most influencing process parameter predicted from RSM techniques in cutting speed and depth of cut.   

scholarly journals Relevance Vector Machine Based Analyses of MRR and SR of Electrodischarge Machining Designed by Response Surface Methodology

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kanhu Charan Nayak ◽  
Rajesh Kumar Tripathy ◽  
Sudha Rani Panda
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Duty Cycle ◽  
Discharge Current ◽  
Removal Rate ◽  
Relevance Vector Machine ◽  
Machining Process ◽  
Machining Parameters ◽  
Electrodischarge Machining ◽  
Pulse On Time

Relevance vector machine is found to be one of the best predictive models in the area of pattern recognition and machine learning. The important performance parameters such as the material removal rate (MRR) and surface roughness (SR) are influenced by various machining parameters, namely, discharge current (Ip), pulse on time (Ton), and duty cycle (tau) in the electrodischarge machining process (EDM). In this communication, the MRR and SR of EN19 tool steel have been predicted using RVM model and the analysis of variance (ANOVA) results were performed by implementing response surface methodology (RSM). The number of input parameters used for the RVM model is discharge current (Ip), pulse on time (Ton), and duty cycle (tau). At the output, the corresponding model predicts both MRR and SR. The performance of the model is determined by regression test error which can be obtained by comparing both predicted MRR and SR from model and experimental data is designed using central composite design (CCD) based RSM. Our result shows that the regression error is minimized by using cubic kernel function based RVM model and the discharge current is found to be one of the most significant machining parameters for MRR and SR from ANOVA.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

scholarly journals Optimization of Machining Parameters of Aluminum Alloy BS-1474 2014 A using Response Surface Methodology

2020 ◽  
Author(s):  
waqas javaid ◽  
Tauqeer Iqbal ◽  
Ammar ul Hassan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Response Surface ◽  
Removal Rate ◽  
Machining Process ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process

Abstract High surface quality, optimum Material Removal Rate (MRR) and Tool-Chip Interface temperature (T c ) have significant importance in machining process. Similarly, dimensional accuracy in machining process also relies heavily on machining parameters. In machining operations, there are a number of parameters which have direct or indirect effect on the Surface Roughness (Ra) and MRR of the product. The surface roughness and MRR in turning process are affected by spindle speed (SS), feed rate (FR) and depth of cut (DOC). The optimization of turning parameters will be very helpful in improving quality of manufacturing and machining cost. In order to have an improved product, extensive research has been carried out to optimize machining process. The current research is focused at Response Surface Methodology (RSM) of turning process of Aluminum alloy (BS-1474 2014 A) by using variable sets of machining parameters i.e., SS, FR and DOC with carbide tipped tool. Multiple experiments were performed on CNC Lathe machine by using different combinations of process parameters. Response surface methodology was applied to reach theoretical values of the responses parameters (i.e, Ra, MRR, T c ) and an agreement was observed between actual machining results and methodology values. Design Expert-7 was used as a statistical tool to come to a conclusion on whether achieved results are optimum for turning process or otherwise. For a close correlation, comparison between hypothetical and investigational data is also the part of this research. Significant agreement between theoretically optimized machining parameters and experimental data has been observed.

scholarly journals Optimization of the Material Removal Rate and Electrode Wear Ratio in Electrical Discharge Machining of Reaction-bonded Silicon Carbide by Response Surface Methodology

2020 ◽  
Vol 9 (4) ◽  
pp. 2116-2120
Keyword(s):  
Silicon Carbide ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
High Hardness ◽  
Electrode Wear ◽  
Good Surface ◽  
Pulse On Time

Reaction-bonded silicon carbide (RB-SiC) is widely used as moulding dies material in many industries thanks to its excellent properties. Nevertheless, because of its high hardness and brittleness, it is extremely hard to be machined with high accuracy and good surface finish. Therefore, electrical discharge machining (EDM) has been chosen as an alternative method to machine the RB-SiC. In the present study, an experimental investigation has been conducted to optimize and validate the EDM parameters on the MRR and EWR of low conductivity RB-SiC in EDM. The new Cu – 1.0 wt. % CNF composite electrode that fabricated via powder metallurgy (PM) process was used as the electrode. The experiments were systematically conducted by face-cubic centre (FCC) approach of response surface methodology (RSM). The mathematical models for MRR and EWR were developed in this study. In addition, analysis of variance (ANOVA) was also figured out to check the significance of the models. Three experiments were conducted as the confirmation test to determine the error percentage of MRR and EWR. Based on the results, only 3.06% and 3.93% errors were determined for both MRR and EWR, respectively. The optimum conditions for multi responses (MRR and EWR) were found to be at a current of 6A, voltage of 22V, and pulse on-time of 12µs. The findings of this study provide an important reference to the manufacturing industries, especially mould and die industry.

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