https://mikelayuso.com/tags/deep-learning/Deep LearningHugoBlox Kit (https://hugoblox.com)en-usFri, 30 Jun 2017 00:00:00 +0000https://mikelayuso.com/media/icon_hu_b0970716f810afd6.pnghttps://mikelayuso.com/tags/deep-learning/https://mikelayuso.com/papers/spot-welding-monitoring/Fri, 30 Jun 2017 00:00:00 +0000https://mikelayuso.com/papers/spot-welding-monitoring/<h2 id="abstract">Abstract</h2> <p>This work is a continuation of our previous work on the development of a monitoring system of a Spot Welding production line. Here we use the process information and photographs of more than 150,000 parts to improve the predictions of the previously developed fuzzy algorithm to predict the degradation state of the electrode. And, we present an alternative method based on deep-learning that aims at substituting the image analysis software developed by us to extract values associated with the quality level of the welded parts from photographs. The deep-learning algorithm learned here is applied to compress original photographs to a 15×15 pixels size image using an encoding / decoding model. Obtained compressed images are then used to predict quality parameters from a fuzzy rule-based classification algorithm. The results are promising and show that compressed images keep the relevant information from the original image that serve to directly determine the degree of the degradation of the electrode without requiring the use of previously developed image analysis software.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Hybrid fuzzy-deep learning architecture for weld quality classification</li> <li>Real-time monitoring capabilities for production environments</li> <li>Comprehensive validation across different welding scenarios</li> </ul>