Machine learning for quality control system - San-Payo, G. (2020)
In this work, we propose and develop a classiﬁcation model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classiﬁcation model. As a manufacturing business progresses, new objects are created, thus, the classiﬁcation model must be able to learn the new classes without losing previous knowledge. However, most classiﬁcation algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classiﬁes features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classiﬁcation problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items