PREDICTION MODEL OF SURFACE ROUGHNESS USING TAGUCHI METHOD

Registration ID: IJNRD_181577

Published ID: IJNRD2205170

DOI: Click Here to Get

Sumant Kumar , Rohit Kumar , Vikas Kumar , Aryaman Goel

In this modern world of highly growing technology and new advancements in every field is necessary as per Increased demand and accuracy. In this paper, artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. New materials come with highly advanced properties, to determine the quality of the product there are various factors like Surface Roughness, Material Removal rate. Surface roughness not only enables one to have good surface properties but also reduces the overall manufacturing cost associated, In terms of metrology it gives us a proper tolerance and accuracy which determines the allowances in the different types of fits. These properties are also vital in terms of improved Tensile strength, Fatigue Strength, corrosion resistance, and temperature-dependent failures (creep). Composites are supermaterials which are having a matrix and reinforcement to improve the qualities of base metal, Al6061 matrix along with SiC (5%) is taken as a composite material. Composite has a very good surface texture, good machinability, and good strength. Stir Casting is one of the best composite fabrication processes which has a mechanical stirrer (Ultrasonic stirrer) that gives mixing up nanoparticles and it comes with a uniform composition. A cylindrical Workpiece of dimensions Diameter (30mm), Length (100mm) is turned on the lathe and it is divided into 9 segments to measure roughness by changing the machining parameters, Speed, depth of cut, feed rate by analyzing various research papers three levels of speed (27.23m/min, 60.21m/min,94.24m/min), feed(.04mm,.12mm,.20mm), depth of cut(.1mm,.2mm,.3mm) are taken. This study focuses on optimizing surface roughness by using the Taguchi method subsequently, 27 readings are taken into consideration to make a regression model, and ANOVA is done which helps us to predict the Roughness without doing any experimental work. The optimal parameters obtained are Speed=94.24m/min ,Depth of cut= 0.1mm , feed = 0.0795 mm

Published Paper   E-Certificate

Sumant Kumar, Rohit Kumar, Vikas Kumar, & Aryaman Goel (May-2022). PREDICTION MODEL OF SURFACE ROUGHNESS USING TAGUCHI METHOD . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(5), 1271-1278. https://ijnrd.org/papers/IJNRD2205170.pdf

Volume 7 Issue 5, May-2022

Pages : 1271-1278

Paper Reg. ID: IJNRD_181577

Published Paper Id: IJNRD2205170

Downloads: 000121982

Research Area: Mechanical Engineering 

Country: VARANASI, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2205170.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2205170

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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