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

Abstract

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.

Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard

“…These approaches are further enhanced by data augmentation and classifier ensemble techniques [ 23 , 24 ]. The choice of classifiers also significantly impacts solution quality [ 25 , 26 , 27 , 28 ].…”

Advanced Feature Extraction Methods from Images of Drillings in Melamine Faced Chipboard for Automatic Diagnosis of Drill Wear

“…Additional improvements were made with data augmentation and classifier ensemble approaches [ 30 , 31 ]. The type of used classifiers also significantly influenced the overall solution quality [ 32 , 33 , 34 , 35 ].…”

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

“…Therefore, an important core technology of smart machinery is how to improve equipment activation through intelligent sensing and fault diagnosis prediction, and how to reduce the risk of downtime due to equipment failure. There are many types of mechanical equipment failures, such as abrasion and damage of the processing machine's spindle cutter [1][2][3][4][5][6][7], the abnormal damage of the bearing [8][9][10][11][12] or gearbox of the rotary machinery due to the harsh environment, rotational instability caused by mechanical failures of the power generator, etc. Therefore, accurate prediction of mechanical failures will reduce production losses, a key factor, and condition for the efficient production of smart machinery.…”