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).
In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a poor quality product and therefore generate some financial loss for the company. In various approaches to this problem, usually, three classes are considered: green for a drill that is sharp, red for the opposite, and yellow for a tool that is suspected of being worn out, requiring additional evaluation by a human expert. In the above problem, it is especially important that the green and the red classes not be mistaken, since such errors have the highest probability of generating financial loss for the manufacturer. Most of the solutions analysing this problem are too complex, requiring specialized equipment, high financial investment, or both, without guaranteeing that the obtained results will be satisfactory. In the approach presented in this paper, images of drilled holes are used as the training data for the Siamese network. The presented solution is much simpler in terms of the data collection methodology, does not require a large financial investment for the initial equipment, and can accurately qualify drill wear based on the chosen input. It also takes into consideration additional manufacturer requirements, like no green-red misclassifications, that are usually omitted in existing solutions.
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 eliminating human interaction with the system might be one goal, it is not the only possible option—lessening the workload of human experts can also bring huge improvement to the production process. The presented approach focuses on providing a tool that will significantly decrease the amount of work that the human expert needs to conduct while evaluating different samples. Instead of hard classification, which assigns a single label to each class, the described solution focuses on evaluating each case in terms of decision confidence—checking how sure the classifier is in the case of the currently processed example, and deciding if the final classification should be performed, or if the sample should instead be manually evaluated by a human expert. The method can be easily adjusted to any number of classes. It can also focus either on the classification accuracy or coverage of the used dataset, depending on user preferences. Different confidence functions are evaluated in that aspect. The results obtained during experiments meet the initial criteria, providing an acceptable quality for the final solution.
This paper presents a time-efficient approach to the drill wear classification problem that achieves a similar accuracy rate compared to more complex and time-consuming solutions. A total of three classes representing drill state are recognized: red for poor state, yellow for elements requiring additional evaluation, and green for good state. Images of holes drilled in melamine faced chipboard were used as input data, focusing on evaluating differences in image color values to determine the overall drill state. It is especially important that there are as few mistakes as possible between the red and green class, as these generate the highest loss for the manufacturer. In green samples presented in gray-scale, most pixels were either black (representing the hole) or white (representing the chipboard), with very few values in between. The current method was based on the assumption that the number of pixels with intermediate values, instead of extreme ones, would be significantly higher for the red class. The presented initial approach was easy to implement, generated results quickly, and achieved a similar accuracy compared to more complex solutions based on convolutional neural networks.
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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