Sonali S. Patil, Sujit S. Pardeshi, Abhishek D. Patange^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 177-199, 2023, DOI:10.32604/cmes.2023.025516

Abstract In-process damage to a cutting tool degrades the surface finish of the job shaped by machining and causes a significant financial loss. This stimulates the need for Tool Condition Monitoring (TCM) to assist detection of failure before it extends to the worse phase. Machine Learning (ML) based TCM has been extensively explored in the last decade. However, most of the research is now directed toward Deep Learning (DL). The “Deep” formulation, hierarchical compositionality, distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform efficiently in a high-noise environment of cross-domain… More >

Nilesh Dhobale¹, Sharad Mulik², R. Jegadeeshwaran^3,*, Abhishek Patange⁴

Sound & Vibration, Vol.55, No.2, pp. 87-116, 2021, DOI:10.32604/sv.2021.014224

Abstract Due to continuous cutting tool usage, tool supervision is essential for improving the metal cutting industry. In the metal removal process tool, supervision is carried out either by an operator or online tool supervision. Tool supervision helps to understand tool condition, dimensional accuracy, and surface superiority. For downtime in the metal cutting industry, the main reasons are tool breakage and excessive wear, so it is necessary to supervise tool which gives better tool life and enhance productivity. This paper presents different conventional and artificial intelligence techniques for tool supervision in the processing procedures that have been depicted in writing. More >