A Study on Material Removal Rate and Surface Roughness of Abrasive Water Jet Machining Process on Hybrid Metal Matrix Composites using Response Surface Methodology

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
M. Shunmugasundaram ◽  
A. Praveenkumar ◽  
L. Ponraj Sankar ◽  
S. Sivasankar
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Removal Rate ◽  
Stir Casting ◽  
Water Jet ◽  
Machining Process ◽  
Machining Parameters ◽  
Abrasive Water Jet

Hybrid metal matrix composite (HMMC) plays a crucial role in the development of better and advanced materials and in their automotive and aeronautical applications. In this investigation also HMMC is prepared using aluminium alloy, as matrix of material and boron nitrate and silicon carbide are chosen for reinforcing material and stir casting method is employed to prepare this new HMMC. The abrasive water jet method is employed for machining the developed HMMCs. The Taguchi approach with ANOVA approach is utilized for optimizing the independent machining parameters to exploit removal rate of material and surface roughness is to be minimized. The Taguchi based response surface methodology(RSM) is utilized for optimizing the machining parameters. Based on the response tables, the rate of feed and stand of distance are more influential parameters on the rate of removal of material and roughness of surface. The optimized machining parameters for improving the rate of removal the material are 100mm/min of FR, 1.5mm SOD and 600gm/min. The 125mm/min FR, 1.5mm SOD and 400gm/min AFR are the optimized machining parameters for minimizing the surface roughness.

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 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.

Abrasive Water Jet Machining of Aluminum-Silicon Carbide Particulate Metal Matrix Composites

2015 ◽  
Vol 830-831 ◽  
pp. 83-86 ◽  
Author(s):  
B. Arul Kumar ◽  
G. Kumaresan
Keyword(s):  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Stir Casting ◽  
Ceramic Particle ◽  
Water Jet ◽  
Machining Process ◽  
Matrix Composites ◽  
Abrasive Water Jet ◽  
Abrasive Water Jet Machining ◽  
Silicon Carbide Particulate

Particle Reinforced Metal Matrix Composites (PRMMC's) have proved to be extremely difficult to machine using conventional manufacturing processes due to heavy tool wear caused by the presence of the hard reinforcement. This paper presents details and results of an investigation into the machinability of SiC particle reinforced aluminium matrix composites using Abrasive Water Jet Machining (AWJM). Al-SiC MMC specimens, prepared with stir casting method. The surface roughness of the composite material for these different compositions are examined and compared. The influence of the ceramic particle reinforcement on the machining process was analyzed.

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