Prediction of tool wear length in micro-EDM process using an artificial neural network

Kanake, Vikas; Ahuja, Bhawna

doi:10.1016/j.matpr.2022.02.345

Cited by 5 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Kanake et al [31], the most crucial metric variable for milling in the micro-EDM procedure, tool wear (TR), should be worked on. In the electric discharge machining operation, tool wear is a significant problem.…”

Section: Ann-ga On Prediction Of Tool Wearmentioning

confidence: 99%

An Overview of the Study of ANN-GA, ANN-PSO, ANFIS-GA, ANFIS-PSO and ANFIS-FCM Predictions Analysis on Tool Wear During Machining Process

Okokpujie¹,

Sinebe²

2023

JESA

View full text Add to dashboard Cite

The implementation of soft computing procedures in tool wear prediction and optimization is a significant process in machining operations for sustainable manufacturing of components with quality finishing. Tool wear is one of the response parameters that leads to a high rate of production cost due to constant tool substitution during machining, mostly when machining hard metals that are difficult to machine. With these challenges, several techniques have been put in place to optimize and predict tool wear rates, including turning, milling, grinding, shaping, and drilling. This study focuses on the evaluation of existing literature that employs soft computing procedures such as ANN-GA, ANFIS, ANFIS-PSO, and ANFIS-FCM in the prediction of cutting tool wear rate during machining processes. From the different study reviews, the results show that the application of these soft computing procedures significantly improves tool life during the manufacturing process by employing the optimal machining parameters in an eco-friendly nano-lubrication environment. This study also points out the challenges currently faced with these soft computing techniques and gives a sustainable way forward as a recommendation to improve the manufacturing process.

show abstract

Section: Ann-ga On Prediction Of Tool Wearmentioning

confidence: 99%