Abstract
Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance D2 it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to 88.8%, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.
Highlights
IntroductionThe purpose of a production process is to manufacture a product to the desired specifications and quality
The main motivation for this research is focused on the development of automated online Gas Metal Arc Welding (GMAW) welding process monitoring, as there is a critical need for automatic and efficient analysis in multivariate processes
36 multivariate patterns were observed in the 36 types of Mahalanobis distances D2 generated by the multivariate variables of the weld beads
Summary
The purpose of a production process is to manufacture a product to the desired specifications and quality. Quality is essential to satisfy customer needs and to improve the product’s competitiveness. To specify the quality of a product, measurements of its quality characteristics are needed to obtain variability data between different units of the finished product. The variability of the measurements is the result of the variation of each element that makes up the process and it is necessary to investigate the causes that produce this variation. The importance of the measurements for product analysis lies on obtaining information of the product itself, its relationship with the operation of the elements of the process, and the correction of the process parameters as well as the acceptance or rejection of the batch of production. It is necessary to examine the measurements with techniques that inspect their variability
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