Modeling and analysis of process parameters on metal removal rate (MRR) in machining of aluminium titanium diboride (Al-TiB2) composite

2015 ◽  
Vol 11 (3) ◽  
pp. 372-385 ◽  
Author(s):  
M. P. Jenarthanan ◽  
A Ram Prakash ◽  
R Jeyapaul
Keyword(s):  
Mathematical Model ◽  
Response Surface ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Surface Model ◽  
Design Matrix ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Content Type

Purpose – The purpose of this paper is to develop a mathematical model for optimizing the metal removal rate (MRR) through Response Surface Methodology (RSM). The developed model helps us to analyze the influence of individual input machining parameters (cutting speed, feed rate, weight percentage) on the responses in machining of Al-TiB2 composite. Design/methodology/approach – RSM is used to optimize the MRR by developing a mathematical model. Three factors, three-level box Behnken design matrix in RSM is employed to carry out the experimental investigation. The “Design Expert 8.0” software is used for regression and graphical analysis of the data are collected. The optimum values of the selected variables are obtained by solving the regression equation and by analyzing the response surface contour plots. Analysis of variance (ANOVA) is applied to check the validity of the model and for finding the significant parameters. Findings – The response surface model developed, helps to calculate the MRR at different input cutting parameters with the chosen range with more than 95 per cent confidence intervals. Originality/value – The effect of machining parameters on MRR during machining of Al-TiB2 composites using RSM has not been previously analyzed.

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.

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.   

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.

Mathematical modeling of delamination factor on drilling of ARALL composites through RSM

2017 ◽  
Vol 13 (4) ◽  
pp. 578-589 ◽  
Author(s):  
M.P. Jenarthanan ◽  
Karthikeyan M. ◽  
Naresh Neeli
Keyword(s):  
Response Surface ◽  
Measuring System ◽  
Surface Model ◽  
Machining Parameters ◽  
Content Type ◽  
Solid Carbide ◽  
Fiber Metal ◽  
Tool Diameter ◽  
Central Composite ◽  
Individual Input

Purpose The purpose of this paper is to develop a mathematical model for delamination during drilling by using a response surface methodology (RSM) and also to determine how the input parameters (tool diameter, spindle speed and feed rate) influence the output response (delamination) in machining of fiber metal laminates. Design/methodology/approach Three factors and a three-level central composite design in RSM are used to carry out the experimental investigation. A video measuring system is used to measure the width of maximum damage of the machined FML composite. The “Design Expert 7.0” is used to analyze the data collected graphically. Analysis of variance is carried out to validate the model and for determining the most significant parameter. Findings The response surface model is used to predict the input factors influencing the delamination on the machined surfaces of the ARALL composite at different cutting conditions with the chosen range of 95 percent confidence intervals. Analysis of the influences of entire individual input machining parameters on the delamination has been carried out using RSM. Originality/value The effect of delamination on drilling of ARALL composites with solid carbide tools of various diameters has not been analyzed yet using RSM.

Experimental investigation of machinability characteristics in Al-TiB2 metal matrix composite (MMC) based on the Taguchi method with fuzzy logics

2016 ◽  
Vol 12 (1) ◽  
pp. 177-193 ◽  
Author(s):  
M.P. Jenarthanan ◽  
A. Ram Prakash ◽  
R. Jeyapaul
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Taguchi Method ◽  
Metal Matrix ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Fuzzy Logics ◽  
Content Type ◽  
Five Factors

Purpose – The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals. Design/methodology/approach – Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB2, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems. Findings – Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained. Originality/value – Machinability characteristics in Al-TiB2 MMC based on the Taguchi method with fuzzy logic has not been analysed previously.

scholarly journals EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS, POWER CONSUMPTION, AND MATERIAL REMOVAL RATE OF ALUMINIUM 6065-SI-MWCNT METAL MATRIX COMPOSITE IN TURNING OPERATIONS

2021 ◽  
Vol 22 (2) ◽  
pp. 283-293
Author(s):  
Savina Jaddinagadhe Puttaswamy ◽  
Raghavendra Bommanahalli Venkatagiriyappa
Keyword(s):  
Surface Roughness ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Stir Casting ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Lathe Machine ◽  
Tungsten Carbide Tool

Nanocomposites were prepared with Al-6065-Si and multi walled carbon nanotubes of 1 wt.% as reinforcement through the stir-casting method. Fabricated nanocomposites were machined on a lathe machine using a tungsten carbide tool. The study investigated the multi-objective optimization of the turning operation. Cutting velocity, feed, and depth of cut were considered for providing minimum Surface Roughness of the workpiece. Also, the power consumed by the lathe machine with maximum metal removal rate was examined by surface response methodology. The design of experiments was developed based on rotational central composite design. Analysis of variance was executed to investigate the adequacy and the suitable fit of the developed mathematical models. Multiple regression models were used to represent the relationship between the input and the desired output variables. The analysis indicates that the feed is the most influential factor that effects the surface roughness of the workpiece. Cutting speed and the depth of cut are two other important factors that proportionally influence the power consumed by the lathe tool as compared to the feed rate. ABSTRAK: Komposit nano disediakan bersama Al-6065-Si dan karbon nanotiub berbilang dinding sebanyak 1 wt.% sebagai bahan penguat melalui kaedah kacauan-tuangan. Komposit nano yang terhasil melalui mesin pelarik ini menggunakan alat tungsten karbida. Kajian ini merupakan pengoptimuman pelbagai objektif operasi pusingan. Kelajuan potongan, suapan dan kedalaman potongan diambil kira sebagai pemberian minimum pada kekasaran permukaan bahan kerja. Tenaga yang digunakan bagi mesin pelarik dengan kadar maksimum penyingkiran logam diteliti melalui kaedah tindak balas permukaan. Rekaan eksperimen yang dibangunkan ini adalah berdasarkan rekaan komposit pusingan tengah. Analisis varian telah dijalankan bagi mengkaji kecukupan dan penyesuaian lengkap bagi model matematik yang dibangunkan. Model regresi berganda digunakan bagi menunjukkan hubungan antara input dan pembolehubah output yang dikehendaki. Analisis menunjukkan pemberian suapan merupakan faktor mempengaruhi keberkesanan kekasaran permukaan bahan kerja. Kelajuan pemotongan dan kedalaman potongan adalah dua faktor penting lain yang mempengaruhi kadar langsung ke atas tenaga yang digunakan oleh mesin pelarik dibandingkan kadar pemberian suapan.

Investigating and Modeling the Machinability of Al 6061 Using Response Surface Methodology

2015 ◽  
Vol 761 ◽  
pp. 267-272
Author(s):  
Basim A. Khidhir ◽  
Ayad F. Shahab ◽  
Sadiq E. Abdullah ◽  
Barzan A. Saeed
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Effect Of Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process

Decreasing the effect of temperature, surface roughness and vibration amplitude during turning process will improve machinability. Mathematical model has been developed to predict responses of the surface roughness, temperature and vibration in relation to machining parameters such as the cutting speed, feed rate, and depth of cut. The Box-Behnken First order and second-order response surface methodology was employed to create a mathematical model, and the adequacy of the model was verified using analysis of variance. The experiments were conducted on aluminium 6061 by cemented carbide. The direct and interaction effect of the machining parameters with responses were analyzed. It was found that the feed rate, cutting speed, and depth of cut played a major role on the responses, such as the surface roughness and temperature when machining mild steel AISI 1018. This analysis helped to select the process parameters to improve machinability, which reduces cost and time of the turning process.

Development of Mathematical Model for Metal Removal Rate on EDM using Copper & Brass Electrodes

2018 ◽  
Vol 5 (2) ◽  
pp. 4345-4352 ◽  
Author(s):  
B. Kishan ◽  
B.Sudheer Premkumar ◽  
S. Gajanana ◽  
K. Buchaiah ◽  
M.A. Gaffar
Keyword(s):  
Mathematical Model ◽  
Metal Removal ◽  
Removal Rate ◽  
Metal Removal Rate

Ascertaining of chatter stability using wavelet denoising and artificial neural network

2018 ◽  
Vol 233 (1) ◽  
pp. 39-62 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Metal Removal ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Artificial Neural ◽  
Propagation Network ◽  
Tool Chatter

In modern machining industries, tool chatter detection and suppression along with maximized metal removal rate is a challenging task. Inexpedient vibration between cutting tool and work piece promotes unstable cutting. This results in enhanced detritions of tool and poor surface finish along with unpredictable metal removal rate. In the present work, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate have been ascertained experimentally. Experimentally recorded raw chatter signals have been denoised using wavelet transform. An artificial neural network model based on feed forward back propagation network has been proposed for predicting stable cutting zone and metal removal rate in turning process. It has been deduced that Tangent Sigmoid activation function in an artificial neural network is the best option to achieve the aforesaid objectives. Well correlation between the artificial neural network predicted results and experimental ones validate the developed technique of ascertaining the tool chatter severity.

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