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.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

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.

Statistical Analysis of Process Parameters in Drilling of Al/Al2O3p Metal Matrix Composites

2014 ◽  
Vol 14 (3) ◽  
pp. 171-175 ◽  
Author(s):  
Yashvir Singh ◽  
Amneesh Singla ◽  
Ajay Kumar
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Process Parameters ◽  
Metal Matrix ◽  
Design Method ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Drilling Parameters

AbstractThis paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/Al2O3p metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, point angle of drill. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors are depth of cut and drill point angle with the contributions of 87% and 12% respectively, whereas the cutting speed is insignificant contributing by 1% only. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness.

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.

scholarly journals Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Small Diameter ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Depth Of Cut ◽  
Turning Process ◽  
Micro Turning

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

Surface Roughness and Chip Formation of AlSi/AIN Metal Matrix Composite by End Milling Machining using the Taguchi Method

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
M. S. Said ◽  
J. A. Ghani ◽  
R. Othman ◽  
M. A. Selamat ◽  
N. N. Wan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Matrix Composite ◽  
Surface Finish ◽  
Chip Formation ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Taguchi Optimization ◽  
Cutting Speeds

The purpose of this research is to demonstrate surface roughness and chip formation by the machining of Aluminium silicon alloy (AlSic) matrix composite, reinforced with aluminium nitride (AlN), with three types of carbide inserts present. Experiments were conducted at various cutting speeds, feed rates, and depths of cut, according to the Taguchi method, using a standard orthogonal array L9 (34). The effects of cutting speeds, feed rates, depths of cut, and types of tool on surface roughness during the milling operation were evaluated using Taguchi optimization methodology, using the signal-to-noise (S/N) ratio. The surface finish produced is very important in determining whether the quality of the machined part is within specification and permissible tolerance limits. It is understood that chip formation is a fundamental element that influences tool performance. The analysis of chip formation was done using a Sometech SV-35 video microscope. The analysis of results, using the S/N ratio, concluded that a combination of low feed rate, low depth of cut, medium cutting speed, and an uncoated tool, gave a remarkable surface finish. The chips formed from the experiment varied from semi–continuous to discontinuous. 

Effect of machining parameters on the surface finish of a metal matrix composite under dry cutting conditions

2015 ◽  
Vol 231 (6) ◽  
pp. 913-923 ◽  
Author(s):  
Brian Boswell ◽  
Mohammad Nazrul Islam ◽  
Ian J Davies ◽  
Alokesh Pramanik
Keyword(s):  
Surface Roughness ◽  
Metal Matrix Composite ◽  
Tool Life ◽  
Surface Finish ◽  
Feed Rate ◽  
Metal Matrix ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting

The machining of aerospace materials, such as metal matrix composites, introduces an additional challenge compared with traditional machining operations because of the presence of a reinforcement phase (e.g. ceramic particles or whiskers). This reinforcement phase decreases the thermal conductivity of the workpiece, thus, increasing the tool interface temperature and, consequently, reducing the tool life. Determining the optimum machining parameters is vital to maximising tool life and producing parts with the desired quality. By measuring the surface finish, the authors investigated the influence that the three major cutting parameters (cutting speed (50–150 m/min), feed rate (0.10–0.30 mm/rev) and depth of cut (1.0–2.0 mm)) have on tool life. End milling of a boron carbide particle-reinforced aluminium alloy was conducted under dry cutting conditions. The main result showed that contrary to the expectations for traditional machined alloys, the surface finish of the metal matrix composite examined in this work generally improved with increasing feed rate. The resulting surface roughness (arithmetic average) varied between 1.15 and 5.64 μm, with the minimum surface roughness achieved with the machining conditions of a cutting speed of 100 m/min, feed rate of 0.30 mm/rev and depth of cut of 1.0 mm. Another important result was the presence of surface microcracks in all specimens examined by electron microscopy irrespective of the machining condition or surface roughness.

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.

scholarly journals Optimization of CNC Turning Parameters in Machining EN19 using Face Centered Central Composite Design Based RSM

2020 ◽  
Vol 9 (2) ◽  
pp. 889-896
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Removal Rate ◽  
Cutting Speed ◽  
Vital Role ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Cnc Turning ◽  
Software Analysis ◽  
Central Composite

Manufacturing a defect free (quality) product is playing a vital role in today’s globally competitive, customer oriented era. Meeting the demand of the market by producing sufficient quantity is another challenge. Achieving greater production rates without compromising on quality, increases the complexity of the task. Adopting modern manufacturing methods like CNC turning are essential to meet the above requirements. EN19 is an important member in the family of alloy steels, which has a wide variety of applications in automobile and machine tool industries. Optimization of machining parameters is crucial in obtaining the required outputs such as quality and productivity. In this work, optimization of CNC turning parameters for machining EN19 alloy steel is performed. The number of experiments was designed using face centred central composite based response surface methodology with varied independent process parameters namely cutting speed, feed and depth of cut. After designing the experiments, the performance measures such as surface roughness of the test samples and Material Removal Rate (MRR) is calculated using the existing formulae. The influence of parameters on MRR and surface roughness are determined by analysis of variance (ANOVA) and for significance interactions of the process parameters are also considered. Using MINITAB 17 software analysis is performed. Further, regression analysis has been done and second order mathematical model is obtained. Using desirability approach, optimization is carried out.

EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS FOR ALUMINIUM MATRIX COMPOSITE BY USING TAGUCHI METHOD WITH DECISION TREE ALGORITHM

2021 ◽  
pp. 2150021
Author(s):  
P. RAVEENDRAN ◽  
S. V. ALAGARSAMY ◽  
M. RAVICHANDRAN ◽  
M. MEIGNANAMOORTHY
Keyword(s):  
Surface Roughness ◽  
Decision Tree ◽  
Taguchi Method ◽  
Cutting Speed ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Cnc Turning ◽  
Cnc Turning Process ◽  
Taguchi’S Design Of Experiments

The intend of this research work is to explore the effect of various parameters in a CNC turning process like cutting speed ([Formula: see text]), feed ([Formula: see text]), and depth of cut ([Formula: see text]) on surface roughness (Ra) of turning AA7075 filled with 10[Formula: see text]wt.% of TiO2 composite fabricated through stir casting method. Taguchi method and decision tree (DT) algorithm were utilized to foresee the surface roughness (Ra) of the proposed composite. The microstructure of composite was ensured with the presence of TiO2 particles dispersed in a homogeneous manner within the matrix material. The machining of composite was carried out by using the CNC turning center and tungsten carbide insert as tool material. This experimental work was designed on L27 (33) orthogonal array using Taguchi’s design of experiments. From its signal-to-noise (S/N) ratio study, the minimum surface roughness (Ra) was obtained at the optimum level of parameters with the cutting speed at 1500[Formula: see text]rpm, feed at 0.15[Formula: see text]mm/rev and depth of cut at 0.3[Formula: see text]mm. Analysis of variance (ANOVA) and decision tree (DT) algorithm were used to identify the significant effect of parameters. The experimental result shows that depth of cut was the major significant parameter on surface roughness (Ra) when compared to cutting speed and feed.

Optimization of tungsten carbide processing parameters for laser heating and ultrasonic vibration composite assisted cutting

2018 ◽  
Vol 233 (12) ◽  
pp. 4140-4153
Author(s):  
Feng Jiao ◽  
Ming-jun Zhang ◽  
Ying Niu
Keyword(s):  
Surface Roughness ◽  
Tungsten Carbide ◽  
Ultrasonic Vibration ◽  
Laser Heating ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Properties

Laser heating assisted cutting is a lucrative technique for machining difficult-to-machine materials such as tungsten carbide (YG20), which uses a high power laser to focally heat a workpiece before the material removal with traditional or innovative cutting tool. In the latter case, the application of ultrasonic vibration to the cutting edge was found to replace the continuous cutting mode to the interrupted one, it reduces the adhesion and entanglement of chips, improves the tool wear and surface roughness of the workpiece. The combination of laser heating assisted cutting and two-dimensional ultrasonic vibration cutting methods has been successfully applied by the authors of this paper for cutting of tungsten carbide (YG20). In this follow-up study, the proposed composite method is experimentally and theoretically verified. Through the mathematical model and simulation analysis, its advantages, including small cutting force, softening the effect and improved machining properties of the processed material (YG20) are corroborated. The dependencies between the laser power, cutting speed, depth of cut, and feed rate on the surface roughness are established via the response surface methodology. The genetic algorithm is applied to the optimization of machining parameters by setting the material removal rate as the object variable and surface roughness as a constraint variable. The results obtained strongly suggest that the optimized parameters improve the processing efficiency and furnish the required processing quality.

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