Abstract
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).
Highlights
The furniture manufacturing process is a complicated one, with many different stages requiring high precision and well thought actions
Even with such limited collection, accuracy of 85% was reached with the presented Convolution neural networks (CNN) algorithm, using AlexNet model (Krizhevsky et al 2012; Russakovsky et al 2015; Shelhamer 2017), and the accuracy was increased to 93.4% by using Support Vector Machine (SVN) as final CNN layer
CNN model trained from scratch, VGG19 and 5xVGG16 were used, since those performed best in terms of overall accuracy, assuming that every chosen model should operate in a different manner, and be efficient enough for the manufacturer requirements
Summary
The furniture manufacturing process is a complicated one, with many different stages requiring high precision and well thought actions. 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 increased to 93.4% by using Support Vector Machine (SVN) as final CNN layer. Values in each cell are presented in the following order: Green, Yellow, Red training set was divided, so that 90% samples were used for actual training, while remaining 10% samples were used for validation
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