ArticleSensors (Basel, Switzerland)2021
Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis.Sensors (Basel, Switzerland) · 2024Article
- Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images.Sensors (Basel, Switzerland) · 2022Article
- Prediction of Metabolic Characteristics of Cardiovascular and Cerebrovascular Diseases Based on Convolutional Neural Network.Computational and mathematical methods in medicine · 2022Article
- Tool Condition Monitoring of the Cutting Capability of a Turning Tool Based on Thermography.Sensors (Basel, Switzerland) · 2021Article
- Automated Inorganic Pigment Classification in Plastic Material Using Terahertz Spectroscopy.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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