Machine learning for quality control system
- PDF / 939,726 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 76 Downloads / 229 Views
ORIGINAL RESEARCH
Machine learning for quality control system Gonçalo San‑Payo1 · João Carlos Ferreira2 · Pedro Santos1 · Ana Lúcia Martins3 Received: 30 November 2018 / Accepted: 9 December 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract 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 Keywords Quality control · Incremental learning · Image classification · Defect detection system
1 Introduction Computer vision problems can be applied to quality control tasks, more precisely in defect detection and classification. There are many quality control systems of manufacturing processes that can be improved with the right use of machine learning algorithms, such as mobile phone cover glass production in Li et al. (2014), fabric production in Chan and Pang (2000), etc. Many machine learning algorithms can * João Carlos Ferreira jcafa@iscte‑iul.pt Gonçalo San‑Payo goncalo.san‑[email protected] Pedro Santos [email protected] Ana Lúcia Martins almartins@iscte‑iul.pt 1
INOV Inesc Inovação-Instituto de novas tecnologias, Lisbon, Portugal
2
Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR- IUL, Lisbon, Portugal
3
Instituto Universitário de Lisboa (ISCTE-IUL), Bussiness Research Unit (BRU-IUL), Lisbon, Portugal
be used for image classification problems, but most of them have a fixed number of classes, and the algorithms cannot learn new classes incrementally. This can be a problem for applications and processes where new data and classes are created because it would require training the algorithm again from scratch with the old and new data together. The present work addresses this issue as it plays a major part in the proposed system. Quality control is a key factor in all major manufacturing businesses, as costumers and investors are increasingly demanding for higher quality. It is vital for a company to en
Data Loading...
