Work

Amalgamation of Physics-Based Cutting Force Model and Machine Learning Approach for End Milling Operation

Year: 2020

Type: article

Abstract: The application of data-driven or machine learning models is becoming imperative in recent times to analyze manufacturing process attributes. These models use input and output datasets to evolve the relationship similar to human perceptions. The development of a reliable data-driven model is challenging due to the necessity of conducting numerous experiments, the presence of outliers and noise in the datasets, process disturbances, etc. The data-driven models can be scaled easily by accommodating new variables and attributes to evolve progressively. Alternatively, physics-based models establish an explicit relationship between process variables and desirable attributes based on scientific knowledge and a set of assumptions, but its scalability is difficult. This paper presents the development of a hybrid cutting force model for end milling operation, combining both approaches to ensure that adequate process knowledge is captured. The outcomes of the proposed method are substantiated by performing a set of computational studies and end milling experiments.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2020.04.102

Authors: Ankit Agarwal, K.A. Desai

Institution: Indian Institute of Technology Jodhpur

Language: English

Topic: Advanced machining processes and optimization

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Department of Science and Technology, Ministry of Science and Technology, India, Ministry of Education, India, Science and Engineering Research Board, Division of Human Resource Development

Award: G2827951397