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IJNRD
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
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Impact Factor : 8.76

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Paper Title: PREDICTION MODEL OF SURFACE ROUGHNESS USING TAGUCHI METHOD
Authors Name: Sumant Kumar , Rohit Kumar , Vikas Kumar , Aryaman Goel
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IJNRD_181577
Published Paper Id: IJNRD2205170
Published In: Volume 7 Issue 5, May-2022
DOI:
Abstract: 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
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Cite Article: "PREDICTION MODEL OF SURFACE ROUGHNESS USING TAGUCHI METHOD ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 5, page no.1271-1278, May-2022, Available :http://www.ijnrd.org/papers/IJNRD2205170.pdf
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ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID:IJNRD2205170
Registration ID: 181577
Published In: Volume 7 Issue 5, May-2022
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Page No: 1271-1278
Country: VARANASI, Uttar Pradesh, India
Research Area: Mechanical Engineering 
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2205170
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2205170
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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