MATLAB AND SIMULINK TECHNIQUES IN PERFORMANCE MODELLING AND CHARACTERISING OF CRITICAL VARIABLES THA

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 11, Issue 2, Apr 2021, 295-304 © TJPRC Pvt. Ltd.

ESTIMATION OF CUTTING FORCES BY REGRESSION ANALYSIS IN MACHINING OF AL-SIC MMCS RAGHAVENDRA1*, SATISH N2 & AJAY KUMAR3 1* 2

Bangalore Institute of Technology / Department of Mechanical Engineering, Bengaluru, India

Bangalore Institute of Technology / Department of Mechanical Engineering, Bengaluru, India 3

KSIT / Department of Mechanical Engineering, Bengaluru, India

ABSTRACT The increasing usage of MMCs in aviation industries is due to the excellent properties of the MMC materials which helps in sustaining the properties for longer period of time. In this regard, an attempt is made to evaluate the properties of the composite material reinforced with silicon carbide particles. The motto of writing this technical paper is to analyze the impact of machining parameters with respect to the hardness of the materials which gets altered due to cryogenic treatment. The study is focused on analyzing the cutting forces that are generated during machining of normal and cryotreated materials for varying speed, feed and depth of cut parameter. Mathematical modeling represents the

under circumstances which have not been physically observed.

A Mathematical model is developed by using the concepts of mathematics which helps in analyzing the effects of various parameters and to make predictions about the behaviour of the system. The models generated are usually based on variables and the relationships. Variables are the parameters of interest that can be quantified and Relationships are described as differential operators, functions or algebraic operators etc..

Original Article

behaviour of a system using mathematical concepts. A mathematical model helps in prediction of behaviour of a system

KEYWORDS: MMCs, SiC, Machinability & Cryogenic treatment

Received: Feb 02 2021; Accepted: Feb 22, 2021; Published: Mar 16, 2021; Paper Id.: IJMPERDAPR202122

1. INTRODUCTION Machinability refers to the ease by which a given work piece will be altered in its physical appearance.

Machinability is not merely the property of the work piece material. It is the overall behavior of the work piece material when it is machined under certain condition. Machinability plays vital role in the selection of a material for its commercial exploitation. For the present study machinability study is being carried out by subjecting the cast composite material to turning operation with varying speed and feed conditions for carbide tool. Tool wear was mainly affected by cutting speed and feed rate under dry turning conditions. Wear rate increases with increase in cutting speed and feed rate.

Multiple regression is one of the methods that is used for analyzing the data and building of models. The problem associated with use of this technique is to specify the functional formulation. The linear assumption may not be valid for all the cases and there are non-linear functions that exist. The assumptions make a vital role when a dependent variable is to be predicted from the measured variables. In such conditions it’s necessary to use the data that determines nature of function, relationship of the variables and nature of relationship.

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2. LITERATURE REVIEW Turning operation is being carried out for Al reinforced Silicon carbide composite and found that the tool wear increases with increase in reinforcement percentage and surface roughness has been found to be generally affected by feed rate and cutting speed [1].

Studies are carried out on Al reinforced SiC (5%) composites to evaluate the tool wear phenomenon which is being influenced by varying machining characteristics. The influence of these parameters has been studied and results indicate that speed and feed parameters influence more on the tool wear. Better surface finish was obtained for low feed and high speed condition [2]. Similar results have been obtained for the material reinforced with SiC with PCD tool at speeds of 300 to 700 m/min [3]. Results of the investigations indicate that PCD tools perform better than carbide tools irruptive of cutting conditions. Tool flank wear has been found to increase with increasing cutting speed and feed [4].

Experimental investigation carried out on the machinability of MMCs by PCD. Inserts are considered as the best choice for the machining of material having higher hardness. Other machining parameter like power consumption and various types of wears are being monitored with the changes in the cutting conditions and the results indicates that the carbide and PCD tools last for longer duration of time with respect to cutting conditions [5&6].

Review of the literature, indicates that the Machinability characteristics of MMCs are influenced by many parameters and there is a large scope for furthering the investigations to study the influence of various parameters at different levels.

3. EXPERIMENTAL DETAILS The experimental investigation planned, to fulfil the objectives set for the present work are discussed in the following section.

3.1 Work Material& Processing: The Aluminum (Al6061) based MMCs with 8% volume fraction of particulate SiC has been selected for the present investigation processed by stir casting process. The specimen size selected is 80mm in diameter and 1000mm length.

3.2 Tooling Details: Carbide tool has been selected for carrying out the machinability study of the Al-Sic(p) MMC. 3.3 Lathe tool Dynamometer: The Axial/feed force (FX), Tangential force (FY) and Radial/ Thrust force (Fz) developed during the turning operation is shown in the Figure 3.1. Figure 3.2 indicates the lathe tool dynamometer used for the present study.

Figure 3.1: Cutting Forces during Turning Operation

Impact Factor (JCC): 9.6246

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Figure 3.2: Lathe Tool Dynamometer

3.4 Cryogenic Treatment As Cast MMC are subjected to cryogenic treatment at a temperature of -1800C in the Liquid Nitrogen medium. The cryo treatment leads to improvement in the hardness of the material which makes it tougher to machine[7].

3.5 Regression Analysis Regression analysis is being carried out to study the influence of the machining parameters in the study.

Simple regression equation for one dependent and independent variable is

Multiple regression equation model for variables is

Where,

– is the dependent variable

– Coefficient obtained from regression analysis – Coefficient values of respective X variables obtained from regression analysis – Independent Variables selected for the study

4. RESULTS & DISCUSSIONS Mathematical models developed based on the concepts of regression analysis is been carried out based on the input parameters like Speed, Feed, Depth of Cut, Percentage of SiC Reinforcement, Temperature, Cutting forces (Fx, Fz) to predict the output parameter Fy which is being measured during turning operation. The main cutting force Fy is the important parameter that has to be predicted from the given set of input parameters. The regression model R square value is estimated to be 0.945 (94.5%) for the normal condition and 0.832 (83.2%) for the cryogenic treated conditions. The value of R Square indicates the percentage of closeness to the measured values. It can be observed from the plots, that the measured and the estimated values are in close tolerance with each other. Irrespective of the cutting conditions, it can be observed that the values are very close to each other and the prediction analysis is found to be effective. The same predictive analysis can be further utilized to determine the cutting forces without carrying out the experimentation. www.tjprc.org

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The estimated results of the regression analysis are staying in line with the measured values in the close tolerance, Hence it can be concluded that from the regression analysis the estimation can be carried out effectively for the given set of cutting conditions.

(a)

Speed 310 RPM, DOC 0.5mm

(b) Speed 310 RPM, DOC 1.0mm

(c) Speed 310 RPM, DOC 1.5mm

(d) Speed 500 RPM, DOC 0.5mm

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(e) Speed 500 RPM, DOC 1.0mm

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(f) Speed 500 RPM, DOC 1.5mm

(g) Speed 775 RPM, DOC 0.5mm

(h) Speed 775 RPM, DOC 1.0mm

(i) Speed 775 RPM, DOC 1.5mm Figure 4.1: Cutting Force (Fy) v/s Feed for Normal Condition

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(j) Speed 310 RPM, DOC 0.5 mm

(k) Speed 310 RPM, DOC 1.0 mm

(l) Speed 310 RPM, DOC 1.5 mm

(m) Speed 500 RPM, DOC 0.5 mm

Impact Factor (JCC): 9.6246

(n) Speed 500 RPM, DOC 1.0 mm

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Estimation of Cutting Forces by Regression Analysis in Machining of Al-Sic Mmcs

(0)

301

Speed 500 RPM, DOC 1.5 mm

(p) Speed 775 RPM, DOC 0.5 mm

(q) Speed 775 RPM, DOC 1.0 mm

(r) Speed 775 RPM, DOC 1.5 mm Figure 4.2: Cutting Force (Fy) v/s Feed for Cryo-treated Condition

Figure 4.1 (a-i) indicates the Cutting Force (Fy) v/s Feed conditions for the normal composites material subjected to machinability study for Varying Speed, Feed and Depth of Cut conditions. It can be observed from the figures that the

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measured and the regression models are very close to each other irrespective of all cutting conditions.

Similar results are also obtained for the Cryo treated composite materials subjected to machinability study, Figure 4.2 (j-r) indicates the Cutting Forces measured and predicted from the regression model.

The respective regression co-efficient obtained during the regression analysis are also indicated in the respective Table 4.1. The obtained regression coefficients of the regression analysis can be substituted to the regression equation to estimate the output. The respective coefficients with their respective parameters can be substituted for the identical estimation of the output i.e the cutting force for the respective input parameter.

Table 4.1: Regression Coefficient Obtained for Normal &Cryo Treated Condition Parameters

Speed Feed Depth of Cut Percentage of SiC Temperature Cutting Force Fx Cutting Force Fz

Normal Condition R Square Intercept 310 0.5 0.5 6 27 8 11

0.945427 -24.16 0.022464 3.614807 1.650467 0 0.895061 -0.1739 0.629734

Cryo Treated Condition R Square Intercept 310 0.5 0.5 6 27 8 11

0.832386 -11.0116 0.008002 1.959483 -1.23986 0 0.470737 0.425056 0.414393

5. CONCLUSIONS MMCs material that was successfully fabricated by stir casting process and the cryotreatement is subjected to machining process and the results obtained during the machining process is discussed in the following conclusions. 

Cutting forces are found to increase with cutting speed and feed rate, for normal and cryotreated composites. The main cutting force Fy is distinctly different compared to Fx and Fz in all the conditions.



The estimated results of the regression analysis are staying in line with the measured values in the close tolerance, Hence it can be concluded that from the regression analysis the estimation can be carried out effectively for the given set of cutting conditions.



The regression model R square value is estimated to be 0.945 (94.5%) for the normal condition and 0.832 (83.2%) for the cryogenic treated conditions. The value of R Square indicates the percentage of closeness to the measured values.

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Impact Factor (JCC): 9.6246

NAAS Rating: 3.11

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