Informatics Publishing Limited

Harish N ¹, Lokesha K ¹, Bharath M R ¹, P B Nagaraj ¹, Shailesh ¹

Affiliations
1 Department of Mechanical Engineering, M S Ramaiah Institute of Technology, Bangalore 560054, IN

Abstract

In engineering manufacturing, contribution of machine tools is a vital deciding factor in fulfilling the dynamic requirements of an industry. In this context, an effort has been made to determine the most significant factor on surface roughness on mild steel material in plane turning operation. In the current study, an experimental work is carried out to determine the effect on surface roughness while machining under various machining factors, cutting tool insert nose radius and process parameters. The experimental trials were designed with Taguchi L9 orthogonal array and carried out for each insert nose radius with different levels of process parameters, spindle speed, depth of cut and feed rate. The surface roughness of the machined part was measured for each trial. The effect and significance of process parameters and insert tool nose radius on surface roughness was analysed using Analysis of variance (ANOVA). The investigation reveals that the spindle speed is the most significant factor on surface finish during plane turning operation.

Keywords

Surface roughness, Taguchi, ANOVA, process parameters, insert nose radius

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Harish N ¹, Kiran Aithal ², Hamritha S ³, K Ramesh Babu N ⁴, Ayushi Chattergi ⁵

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

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.

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