A brief review: acoustic emission method for tool wear monitoring during turning
By Xiaoli Li. 2002
536 citations recorded by the source corpus.
Subfields: Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/ad5eeb319b9d9cb8f4155fc1134941a35b8eb755
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Mechanical Engineering
- R. Paurobally
- Arjan Siddhpura
- Krzysztof Jemielniak
- Tomasz Urbanski
- Joanna Kossakowska
- Sebastian Bombiński
- J. V. Abellán-Nebot
- Vivek T. Rathod
In Mechanics of Materials
- J. V. Abellán-Nebot
- Arjan Siddhpura
- Krzysztof Jemielniak
- Vivek T. Rathod
- R. Paurobally
- Fernando Romero Subirón
Citations in the corpus
- A Review of Electric Impedance Matching Techniques for Piezoelectric Sensors, Actuators and Transducers (2019, background): “The devices for applications like biomedical imaging [6–8], acoustic emission [9,10], condition monitoring [11,12], nondestructive evaluation (NDE) [13–17], and structural health monitoring (SHM) [18–22] are to be designed considering the performance as a whole system due to the criticality of the t”
- A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, methodology): “In a brief review, Li [101] discussed AE-based tool wear condition monitoring for turning including AE generation in turning, various methods used for AE signal measurement and processing, and methodologies for estimating tool wear.”
- Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, unknown): “…using temperature sensors (Chryssolouris & Domroese, 1989), cutting force (Korkmaz & Günay, 2018b; Korkmaz et al., 2020), vibration, acoustic emission (Li, 2002), surface roughness (Yurtkuran et al., 2016), torque/current (Axinte & Gindy, 2004), power consumption (Rivero et al., 2008), etc.”
- A review of machining monitoring systems based on artificial intelligence process models (2010, background): “Continuous signals are associated with shearing in the primary zone and wear on the tool face and flank, while burst or transient signals result from either tool fracture or chip breakage [ 31 ].”
- Tool condition monitoring based on numerous signal features (2011, background): “requires the application of the most meaningful SFs which best describe the tool wear [1–4].”