Authors:
Mühenad Bilal
1
;
Sunil Kancharana
1
;
Christian Mayer
1
;
Daniel Pfaller
1
;
Leonid Koval
1
;
Markus Bregulla
1
;
Rafal Cupek
2
and
Adam Ziębiński
2
Affiliations:
1
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt 85057, Germany
;
2
Silesian University of Technology, Institute of Informatics, Gliwice, Poland
Keyword(s):
Predictive Maintenance, Machine Learning, Damage Detection, Illumination Source, Mask R-CNN.
Abstract:
The implementation of intelligent software in the manufacturing industry is a technology of growing importance and has highlighted the need for improvement in automatization, production, inspection, and quality assurance. An automated inspection system based on deep learning methods can help to enhance inspection and provide a consistent overview of the production line. Camera-based imaging systems are among the most widely used tools, replacing manual industrial quality control tasks. Moreover, an automatized damage detection system on milling tools can be employed in quality control during the coating process and to simplify measuring tool life. Deep Convolutional Neural Networks (DCNNs) are state-of-the-art methods used to extract visual features and classify objects. Hence, there is great interest in applying DCNN in damage detection and classification. However, training a DCNN model on Titanium-Nitride coated (TiN) milling tools is extremely challenging. Due to the coating, the
optical properties such as reflection and light scattering on the milling tool surface make image capturing for computer vision tasks quite challenging. In addition to the reflection and scattering, the helical-shaped surface of the cutting tools creates shadows, preventing the neural network from efficient training and damage detection. Here, in the context of applying an automatized deep learning-based method to detect damages on coated milling tools for quality control, the light has been shed on a novel illumination technique that allows capturing high-quality images which makes efficient damage detection for condition monitoring and quality control reliable. The method is outlined along with results obtained in training a ResNet 50 and ResNet 101 model reaching an overall accuracy of 83% from a dataset containing bounding box annotated damages. For instance and semantic segmentation, the state-of-the-art framework Mask R-CNN is employed.
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