Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process
Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editor: Dieter Uckelmann
Sensors 2021, 21(5), 1917; https://doi.org/10.3390/s21051917
Received: 25 February 2021
/
Revised: 4 March 2021
/
Accepted: 5 March 2021
/
Published: 9 March 2021
(This article belongs to the Special Issue Industry 4.0 and Smart Manufacturing)
This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.
View Full-Text
Keywords:
tool wear; turning; infrared thermography; artificial intelligence; deep learning; Industry 4.0
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Brili, N.; Ficko, M.; Klančnik, S. Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process. Sensors 2021, 21, 1917. https://doi.org/10.3390/s21051917
AMA Style
Brili N, Ficko M, Klančnik S. Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process. Sensors. 2021; 21(5):1917. https://doi.org/10.3390/s21051917
Chicago/Turabian StyleBrili, Nika, Mirko Ficko, and Simon Klančnik. 2021. "Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process" Sensors 21, no. 5: 1917. https://doi.org/10.3390/s21051917
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
