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- Title
Automatic Estimation of Drill Wear Based on Images of Holes Drilled in Melamine Faced Chipboard with Machine Learning Algorithms.
- Authors
Jegorowa, Albina; Kurek, Jarosław; Antoniuk, Izabella; Krupa, Artur; Wieczorek, Grzegorz; Świderski, Bartosz; Bukowski, Michał; Kruk, Michał
- Abstract
In this article, an approach to drill wear evaluation is presented. Tool condition monitoring is an important problem in furniture manufacturing and similar industries. At the same time, approaches that rely on sets of sensors, often tend to be to robust or complex for the production environment. Instead of signals acquired from dedicated sensors, presented approach uses images of drilled holes as input data. Initial pictures are processed and enhanced in order to highlight the crucial properties. A set of selected features is then calculated on the resulting images, and later used during the training of 5 state-of-the-art classifiers. Presented research also evaluates number of images for consecutive drillings that needs to be taken into account in order to produce accurate results. From the selected set, the best performing classifier was Random Forest and it achieved close to 100% accuracy.
- Subjects
MACHINE learning; FURNITURE manufacturing; RANDOM forest algorithms; ARTIFICIAL intelligence; MELAMINE; MANUFACTURING industries
- Publication
Forests (19994907), 2023, Vol 14, Issue 2, p205
- ISSN
1999-4907
- Publication type
Article
- DOI
10.3390/f14020205