https://mikelayuso.com/tags/quality-control/Quality ControlHugoBlox Kit (https://hugoblox.com)en-usSun, 31 Dec 2017 00:00:00 +0000https://mikelayuso.com/media/icon_hu_b0970716f810afd6.pnghttps://mikelayuso.com/tags/quality-control/https://mikelayuso.com/papers/surface-crack-detection/Sun, 31 Dec 2017 00:00:00 +0000https://mikelayuso.com/papers/surface-crack-detection/<h2 id="abstract">Abstract</h2> <p>The aim of this work is to detect open surface cracks on metallic welds using laser thermography and posterior processing algorithms. In the last years the trend on welding processes has been to extend their use to applications with higher stress conditions. As a consequence, obtained welded regions are more prone to cracks. The types of cracks originated from welding processes are, in general, superficial and their detection is not straightforward since it detection is affected by their width. Traditional crack detection Non Destructive Techniques, such as Dye Penetrant or Magnetic Particles, are time consuming and require an experienced technician to correctly interpret the results. Furthermore, they foul the surface which needs to be cleaned after the test. Laser Thermography [5] [10] has several advantages over these traditional techniques: it is fast, non-contact and clean. In this paper, dynamic tests have been performed by scanning the weld maintaining the laser and camera motionless, and moving the piece linearly across them, so that the weld is scanned. The use of a scanning laser line to heat the surface allows the detection of the characteristic disruption that cracks generate in the natural diffusion of heat on the surface. Briefly, cracks block the heat front. This makes the temperature just before the crack to increase, while keeping the temperature of the region just after it at similar values for larger times than for other nearby regions. This thermal effect is known as “Thermal barrier” [11]. Here, a new method to detect the cracks based on this effect has been developed starting from two different approaches.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li>Novel laser thermography setup optimized for surface crack detection</li> <li>Thermal Barrier effect modeling for improved signal processing</li> <li>Experimental validation on various material types</li> </ul>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>