Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling

Jegorowa, Albina; Górski, J.; Kurek, Jarosław; Kruk, Michał

doi:10.1007/s00107-019-01428-5

Cited by 21 publications

(18 citation statements)

References 2 publications

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“…There are different approaches to tool condition monitoring, each related to various machine parts and their specifics, but usually in those cases three classes are considered for labelling the general state of tested element: red, green and yellow (Jegorowa et al 2019(Jegorowa et al , 2020. The first class describes tools that are in a poor state, and should be immediately replaced because of it.…”

Section: Methodsmentioning

confidence: 99%

Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard

et al. 2020

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In this paper, a set of improvements made in drill wear recognition algorithm obtained during previous work is presented. Images of the drilled holes made on melamine faced particleboard were used as its input values. During the presented experiments, three classes were recognized: green, yellow and red, which directly correspond to a tool that is in good shape, shape that needs to be confirmed by an operator, and which should be immediately replaced, since its further use in production process can result in losses due to low product quality. During the experiments, and as a direct result of a dialog with a manufacturer it was noted that while overall accuracy is important, it is far more crucial that the used algorithm can properly distinguish red and green classes and make no (or as little as possible) misclassifications between them. The proposed algorithm is based on an ensemble of possibly diverse models, which performed best under the above conditions. The model has relatively high overall accuracy, with close to none misclassifications between indicated classes. Final classification accuracy reached 80.49% for biggest used window, while making only 7 critical errors (misclassifications between red and green classes).

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Section: Methodsmentioning

confidence: 99%

Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard

et al. 2020

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“…One of the most innovative elements in the analyzed research trend is the adoption of the following four general methodological assumptions [33][34][35][36][37][38].…”

Section: Fundamental Assumptions Of the New Approach To Drill Conditi...mentioning

confidence: 99%

“…Generally, tool condition monitoring in the field of woodworking has also been popular for a long time [24][25][26]. Therefore, at the end of this introductory (and as concisely as possible) overview of the latest research trends, it is also worth noting the new and quite spectacular approach to drill condition monitoring in wood-based panels machining [27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…In the last 2 or 3 years, numerous research and scientific analyses have been carried out to address this problem. Some of them seem to be truly innovative and their synthetic review is the main content of this paper [33][34][35][36][37][38]. The purpose is to encourage more scientists to cooperate or compete in this research area (both attitudes are beneficial from a scientific development point of view) currently.…”

Section: Introductionmentioning

confidence: 99%

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The Review of New Scientific Developments in Drilling in Wood-Based Panels with Particular Emphasis on the Latest Research Trends in Drill Condition Monitoring

Górski

2022

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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 thematically) research has been recently carried out on drilling. The article focuses on the new and quite spectacular approach to drill condition monitoring in wood-based panels machining. One of the most innovative elements in the analyzed research trend is the adoption of the new general methodological assumptions that allow one to define the drill condition monitoring problem as a standard three-class classification. The general effectiveness of the tested monitoring systems (accuracy of classification ACC [%]), ranged between 67% and 82%. The critical classification error (CCE [%]) ranged between 0% and 1.6%. These results seem very promising, yet are still not good enough to develop a commercial monitoring system. A more useful form of obtaining diagnostic data and more effective classification strategies (algorithms) are likely to be required.

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“…Presently, multiclassifiers are mainly constructed by combining multiple classifiers. Commonly employed methods are one-versus-one (OVO) SVMs and one-versus-rest (OVR) SVMs [45]. OVO SVMs have a higher classification accuracy than OVR SVMs and have a low computational complexity.…”

Section: ) Base Learner Of Svm Modelmentioning

confidence: 99%

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

et al. 2020

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Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling

Cited by 21 publications

References 2 publications

Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard

Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard

The Review of New Scientific Developments in Drilling in Wood-Based Panels with Particular Emphasis on the Latest Research Trends in Drill Condition Monitoring

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

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