https://mikelayuso.com/tags/welding/WeldingHugoBlox Kit (https://hugoblox.com)en-usFri, 30 Jun 2017 00:00:00 +0000https://mikelayuso.com/media/icon_hu_b0970716f810afd6.pnghttps://mikelayuso.com/tags/welding/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>https://mikelayuso.com/papers/electrode-degradation/Thu, 30 Jun 2016 00:00:00 +0000https://mikelayuso.com/papers/electrode-degradation/<h2 id="abstract">Abstract</h2> <p>This work presents the preliminary analysis done to determine the electrode degradation during a resistance spot welding manufacturing process of aluminium-based unions. This process represents a critical step in a high production rate manufacturing line, where currently identical welding parameters are used for the tens of thousands of dayly produced parts. During the welding process, the aluminium can melt locally, producing droplets that adhere to the electrode speeding its degradation that affects the quality of the joint. Degradation is hard to predict since it depends on part surface quality, electrode preparation and system set-up. In this work, an analysis of the degradation of the electrode associated with the observed and measured quality of the parts is carried out. With the final aim to advance toward a zero defect manufacturing scenario and increase the quality of the produced parts, a process data analysis based on fuzzy logic algorithms will be performed. An assessment of (i) produced part quality, (ii) the electrode degradation state, (iii) detection of different performance modes of the parameters and (iv) detection of events or process changes, will be done using image and production line data. This will allow to determine (i) when the electrode must be changed and (ii) the optimal combination of welding parameters to extend electrodes&rsquo; life assuring welding quality.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Detailed characterization of electrode degradation patterns</li> <li>Analysis of process parameters affecting electrode lifespan</li> <li>Recommendations for electrode maintenance and replacement</li> </ul>