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, J.

doi:10.3390/f13020242

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Monitoring operational performance [ 11 ], optimizing drilling parameters [ 12 ] and AI algorithms perform well for various complex problems. The applications of such algorithms are also growing increasingly common in the wood industry in general, especially when it comes to problems encountered during the drilling process [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Kurek

Krupa

Antoniuk

et al. 2023

Sensors

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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 be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring.

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Section: Introductionmentioning

confidence: 99%

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Kurek

Krupa

Antoniuk

et al. 2023

Sensors

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“…affects the efficiency of drilling operation that is often quantified through tool durability, cost, and quality of holes. An extensive review regarding the drilling of wood and woodbased materials is presented in the study performed by Gorski [2]. However, regarding the drilling of plywood, there is limited information in the literature.…”

Section: Introductionmentioning

confidence: 99%

Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood

Bedelean,

Ispas,

Răcășan

2023

Applied Sciences

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Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process.

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“…At the same time, the general complexity of this process, when combined with different noise-influencing registered signals and specific properties of the production environment, tends to increase requirements for the existing, sensor-based solutions in terms of both accuracy and automatization. Taking into account recent, high accuracy solutions (such as Ranjan et al 2020 describing the tool monitoring in micro-drilling process focuses on hole quality prediction, Yu et al 2021 specifically on chisel edge wear of drills, or solutions described in an overview of current methods provided by Górski 2022), it is even more important to create more automated, diagnostic methods for cutting tools used in the wood industry, especially elements such as drills.…”

Section: Introductionmentioning

confidence: 99%

Tool condition monitoring for the chipboard drilling process using automatic, signal-based tool state evaluation

Świderski

Antoniuk

Kurek

et al. 2022

BioRes

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An automatic approach to tool condition monitoring is presented, with the best solution achieving overall accuracy of 94.33% and 9 misclassification errors. In the wood industry, cutting tools need to be evaluated periodically. This is especially the case when drills are concerned; since when dulled, the resulting poor-quality product may generate loss for the manufacturing company, due to the need to discard it during quality control. Each tool can be classified either as useful or useless, and the second type should be exchanged as fast as possible. Manual evaluation of tools is time consuming, which results in production downtime. This problem requires a faster, automated, and precise solution for the work environment. In response to this issue, an ensemble algorithm was developed. Different signals were collected for the input data, including feed force, cutting torque, noise, vibrations, and acoustic emission. Based on those signals, a set of 152 initial features was generated, while after feature selection 19 of them were used by the classifiers. Different algorithms were tested and evaluated in terms of overall accuracy and number of errors. The best classifiers were used to prepare ensemble solution, which was able to classify the tools accurately, with very few errors between recognized classes.

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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

Cited by 7 publications

References 37 publications

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Improved Drill State Recognition during Milling Process Using Artificial Intelligence

Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood

Tool condition monitoring for the chipboard drilling process using automatic, signal-based tool state evaluation

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