The review of new scientific developments in drilling in wood-based panels with particular emphasis on the latest research trends in drill condition monitoring

J Górski - Forests, 2022 - mdpi.com
Drilling is still one of the basic cutting processes that are of particular interest to wood
science and technology professionals. As a result, considerable (and very diverse …

Improved drill state recognition during milling process using artificial intelligence

J Kurek, A Krupa, I Antoniuk, A Akhmet, U Abdiomar… - Sensors, 2023 - mdpi.com
In this article, an automated method for tool condition monitoring is presented. When
producing items in large quantities, pointing out the exact time when the element needs to …

Decision confidence assessment in multi-class classification

M Bukowski, J Kurek, I Antoniuk, A Jegorowa - Sensors, 2021 - mdpi.com
This paper presents a novel approach to the assessment of decision confidence when multi-
class recognition is concerned. When many classification problems are considered, while …

Automatic estimation of drill wear based on images of holes drilled in melamine faced chipboard with machine learning algorithms

A Jegorowa, J Kurek, I Antoniuk, A Krupa, G Wieczorek… - Forests, 2023 - mdpi.com
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 …

Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard

M Bukowski, J Kurek, B Świderski, A Jegorowa - Sensors, 2024 - mdpi.com
The advancement of machine learning in industrial applications has necessitated the
development of tailored solutions to address specific challenges, particularly in multi-class …

Advanced feature extraction methods from images of drillings in melamine faced chipboard for automatic diagnosis of drill wear

I Antoniuk, J Kurek, A Krupa, G Wieczorek, M Bukowski… - Sensors, 2023 - mdpi.com
In this paper, a novel approach to evaluation of feature extraction methodologies is
presented. In the case of machine learning algorithms, extracting and using the most …