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Study on Process Parameters of Centrifugal Cast Al17 wt% Si and Predicting the Mechanical and Tribological Properties using Machine Learning Algorithms


Affiliations
1 Research Scholar, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
2 Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
3 Assistant Professor, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054, India
4 Associate Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
5 Student, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054, India

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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.

Keywords

Al17wt%Si, Mechanical properties, Tribological properties, Prediction models, Machine learning algorithm.
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  • Study on Process Parameters of Centrifugal Cast Al17 wt% Si and Predicting the Mechanical and Tribological Properties using Machine Learning Algorithms

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Authors

Harish N
Research Scholar, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
Kiran Aithal
Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
Hamritha S
Assistant Professor, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054, India
K Ramesh Babu N
Associate Professor, Nitte Meenakshi Institute of Technology, Department of Mechanical Engineering, Bangalore 560064, India
Ayushi Chattergi
Student, Department of Industrial Engineering and Management M S Ramaiah Institute of Technology, Bangalore 560054, India

Abstract


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.

Keywords


Al17wt%Si, Mechanical properties, Tribological properties, Prediction models, Machine learning algorithm.

References





DOI: https://doi.org/10.18311/jmmf%2F2023%2F34490