Transfer learning in recognition of drill wear using convolutional neural network

Kurek, Jarosław; Wieczorek, Grzegorz; Kruk, Bartosz Swiderski Michal; Jegorowa, Albina; Osowski, S.

doi:10.1109/cpee.2017.8093087

Cited by 26 publications

(20 citation statements)

References 9 publications

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“…The data set used during the training process was similar to that in previous works (Kurek et al 2017b(Kurek et al , 2019a, but in this case it was larger, containing 5 sets of images, one for each of the drills used, with a total of 8526 samples: 3780 representing green class, 2800 for yellow class and 1946 for red class. Since green class was significantly better represented (almost half of the samples), there was a possibility that such lack of balance in the data set could result in poor accuracy after training, therefore it was decided to optimize the data set.…”

Section: Data Preparationmentioning

confidence: 99%

“…The first of the previous works considered in the current approach (Kurek et al 2017b) focused on applying CNN to the problem of drill wear prediction with a very limited set of training data (242 images representing three classes: 102 green samples, 60 yellow samples and 80 red samples). Even with such limited collection, accuracy of 85% was reached with the presented CNN algorithm, using AlexNet model (Krizhevsky et al 2012;Russakovsky et al 2015;Shelhamer 2017), and the accuracy was additionally increased to 93.4% by using Support Vector Machine (SVN) as final CNN layer.…”

Section: Introductionmentioning

confidence: 99%

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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: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

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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show abstract

Section: Introductionmentioning

confidence: 99%

“…In [ 5 , 6 , 7 ], it was shown that using only images of drilled holes and convolutional neural networks (CNN) can give satisfying results, and it is a much simpler solution than that based on multiple sensors. In [ 5 ], only an original set of 242 images was used. Trying to build simple CNN on dataset of this size achieved poor accuracy (35%) and required the use of highly pretrained neural networks on the model data in order to achieve a high value of said metric (around 93%).…”

Section: Introductionmentioning

confidence: 99%

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Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard

Wieczorek

Chlebuś

Gajda

et al. 2021

Sensors

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The multiclass prediction approach to the problem of recognizing the state of the drill by classifying images of drilled holes into three classes is presented. Expert judgement was made on the basis of the quality of the hole, by dividing the collected photographs into the classes: “very fine,” “acceptable,” and “unacceptable.” The aim of the research was to create a model capable of identifying different levels of quality of the holes, where the reduced quality would serve as a warning that the drill is about to wear down. This could reduce the damage caused by a blunt tool. To perform this task, real-world data were gathered, normalized, and scaled down, and additional instances were created with the use of data-augmentation techniques, a self-developed transformation, and with general adversarial networks. This approach also allowed us to achieve a slight rebalance of the dataset, by creating higher numbers of images belonging to the less-represented classes. The datasets generated were then fed into a series of convolutional neural networks, with different numbers of convolution layers used, modelled to carry out the multiclass prediction. The performance of the so-designed model was compared to predictions generated by Microsoft’s Custom Vision service, trained on the same data, which was treated as the benchmark. Several trained models obtained by adjusting the structure and hyperparameters of the model were able to provide better recognition of less-represented classes than the benchmark.

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Tool wear state prediction based on feature-based transfer learning

Lü

Chen

et al. 2021

Int J Adv Manuf Technol

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Transfer learning in recognition of drill wear using convolutional neural network

Cited by 26 publications

References 9 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

Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard

Tool wear state prediction based on feature-based transfer learning

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