Experiment with the Multivolt Drop Technique to Predict the Physical Properties of Al6061 using Artificial Neural Network
Kanikicharla Jaya Sudheer Kumar1, B. Chandra Mohana Reddy2
1Kanikicharla Jaya Sudheer Kumar, Department of Mechanical Engineering, JNTUA College of Engineering, Anantapur (AP), India.
2Dr. B. Chandra Mohan Reddy, Department of Mechanical Engineering, JNTUA College of Engineering, Anantapur (AP), India.
Manuscript received on 21 June 2022 | Revised Manuscript received on 27 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 78-87 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71280711222 | DOI: 10.35940/ijrte.B7128.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: According to this study, because of its light weight, high specific strength, and stiffness at high temperatures, Al6061 is the most appropriate material in the transportation business. The major goal of this research is to evaluate the physical properties of Al6061, such as thermal conductivity and electrical resistivity, by experimental investigation utilizing the multivolt drop approach. As Artificial Intelligence techniques become more widespread, they are being used to forecast material properties in engineering research. So, the second goal of this research is to employ Artificial Neural Networks to build a prediction model with fewer errors by utilizing experimental data. It will reduce the situation of direct observations throughout a wide range of temperatures where the physical properties of Al6061 are significant. As a consequence, it was discovered that the enhanced optimum ANN has significant mechanical properties that impact prediction. The anticipated results in electrical resistivity and thermal conductivity had Root Mean Squared Errors of 0.99966 and 0.99401, respectively, with R-Square average values of 0.820105. Various tests and ANN methodologies were used to validate and compare the suggested results. The comparison of predicted values with multivolt drop experimental results demonstrated that the projected ANN model provided efficient Al6061 accuracy qualities.
Keywords: Al6061 Metal Matrix, Thermal Conductivity, Electrical Resistivity, Artificial Neural Network, Multivolt Drop Technique
Scope of the Article: Artificial Neural Network