A review of flank wear prediction methods for tool condition monitoring in a turning process

By Arjan Siddhpura, R. Paurobally. The International Journal of Advanced Manufacturing Technology, 2012

320 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2e3cf6fe5692fc516ad1ecefbe6132f60f5ecd16

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Munish Kumar Gupta
2. Vishal S. Sharma

In Electrical and Electronic Engineering

1. Munish Kumar Gupta
2. Mustafa Kuntoğlu
3. D. Pimenov
4. Andrés Bustillo
5. Vishal S. Sharma
6. Szymon Wojciechowski

In Mechanical Engineering

1. D. Pimenov
2. Munish Kumar Gupta
3. Mustafa Kuntoğlu
4. Andrés Bustillo
5. Vishal S. Sharma
6. Szymon Wojciechowski

Citations in the corpus

• Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, background): “In this domain, there is a noticeable tendency to move to production modules with a high level of automation (Siddhpura & Paurobally, 2013), which requires an increase in the number of intelligent self-regulating and self-adjusting machines (Kim et al., 2018).”