Study on Process Parameters of Centrifugal Cast Al17 wt% Si and Predicting the Mechanical and Tribological Properties using Machine Learning Algorithms
Authors
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Research Scholar, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064 ,IN
N. Harish -
Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064 ,INKiran Aithal
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Assistant Professor, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054 ,INS. Hamritha
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Associate Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064 ,INK. N. Ramesh Babu
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Student, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054 ,INAyushi Chattergi
DOI:
https://doi.org/10.18311/jmmf/2023/34490Keywords:
Al17wt%Si, Mechanical properties, Tribological properties, Prediction models, Machine learning algorithmAbstract
Aluminum alloys are the most widely utilized metal in today’s world for manufacturing all industrial applications that
demand lightweight characteristics as well as mechanical and tribological capabilities. As a requirement for obtaining
quality products, it is critical that the manufacturing process is also optimized. As a replace to the conventional methods
of manufacturing, this article presents the development of machine learning (ML) models for Al-17wt% Si taking into
account process parameters such as different teeming temperatures and rotation speeds of molds. In addition, the properties
of hardness and wear are taken into account in the construction of the data base and the models are formed for the same.
In this work, machine learning techniques such as Linear Regression (LR) and Artificial Neural Networks (ANN) algorithms
are used to predict tensile and wear properties. ANN and LR models show similar results, but ANN can handle many more
complexities, making the model reliable. This method of predicting the properties will lead to the definition of the optimized
process parameters, minimizing the efforts on conventional manufacturing and testing processes.
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