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Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning
摘要： Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.
关键词： fermentation monitoring,optical sensor,superellipsoid model fitting,process automation,quality inspection,deep learning
更新于2025-09-12 10:27:22
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Quality Inspection System for Robotic Laser Welding of Double-Curved Geometries
摘要： The quality of robotic laser welded parts is related to the joint location, the trajectory of the laser focal point and the process parameters. By performing in-process monitoring, it is possible to acquire sufficient process knowledge for post-inspection to evaluate the geometrical weld quality. The existing solutions for such systems operate along linear welds. This paper contributes with a quality inspection system for robot laser welding, that can handle double-curved geometries. The data acquisition system includes a CMOS camera, which is mounted such that it looks through the laser optics, external LED illumination and matching optical filters. During the process, the area around the moving laser focal point is captured, resulting in a sequence of images. The trajectory of the focal point is determined by estimating the 2D displacement field between each image using template matching and subsequently filtering the data through a Kalman filter to improve the accuracy and robustness of the system. The joint location is determined by applying a Canny edge detector and a standard Hough transform within a specified region of interest. As this paper deals with double-curved geometries, the region of interest is moved in relation to the laser trajectory, such that it always contains the visible part of the joint, that is closest to the focal point. The developed post-inspection system evaluates the quality of the weld by comparing the estimated trajectory relative to the determined location of the joint. The performance of the proposed quality inspection system was validated empirically on 18 samples. The tests showed promising results, as the system was able to accurately detect changes in the welding trajectory relative to the location of the joint with an accuracy of ± 0.2 mm.
关键词： Vision system,Image processing,Quality inspection,Laser welding,Welding trajectory
更新于2025-09-12 10:27:22