Abstract
Manufacturers would like to increase production volumes while preserving the high quality of their products. The long testing times can cause a bottleneck of production processes especially taking into account the observed tendency for testing all produced devices. The main aim of this work consists in the analysis of time changes of features extracted from thermal images using the multivariate approach. The paper shows that if the principal component analysis (PCA), belonging to multivariate methods, is applied for quality control based on infrared images, then the minimum testing times can be estimated. In order to draw the final conclusions regarding testing times and, what is also very important, which principal components should be selected for classification, a detailed temporal analysis for an exemplary production line has been carried out. The future impact of the results includes higher productivity and cost-effectiveness due to the determination of an optimal decision time in production line quality control systems using the proposed approach.
Highlights
The constant development of measuring techniques and equipment results in a huge amount of data available for quality control tasks
I.e., temporal analysis of principal component analysis (PCA) results aimed at acquisition time selection, has not been presented in the literature
The novelty of the paper consists in the detailed analysis of principal components temporal behavior aimed at the selection of the components which ensure the shortest time for performing production line quality control
Summary
The constant development of measuring techniques and equipment results in a huge amount of data available for quality control tasks. The importance of the third dimension through detailed analysis of the time evolution of features extracted using a multivariate algorithm has been shown Such an approach, i.e., temporal analysis of PCA results aimed at acquisition time selection, has not been presented in the literature. The novelty of the paper consists in the detailed analysis of principal components temporal behavior aimed at the selection of the components which ensure the shortest time for performing production line quality control. The main conclusion is that it is possible to minimize the time required for quality control and obtain satisfactory quality control results but the principal components selection must be made individually for each problem These factors, i.e., time and quality, are crucial in production line applications of infrared imaging. The PCA, as a well-established method, has been briefly described and its application to analysis of infrared images has been given
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
