Deep Learning Detection Algorithm for Surface Defects of Automobile Door Seals

The surface defects of automobile door seals are mainly detected manually at present, which is costly and has low efficiency. Therefore, a deep learning automatic detection algorithm of automobile door seals is studied in this paper. At first, the defects are classified and the data set is made according to the geometric characteristics of the defects. While enhancing the data set, the K-means clustering algorithm is used to cluster the target annotation frame in the data set, and the anchor that matches with the surface defect size of the seal is obtained. Finally, in view of the characteristics of large variation range of defect size, the target detection algorithm YOLOV3 is selected as the basic framework. Meanwhile, considering the high proportion of small and medium-sized targets in defects, the output scale is introduced at 4 times of the sampling position of YOLOV3 backbone network. In order to further enhance the correlation between the extracted features and the channel, spatial position and coordinate position, the feature fusion and attention mechanism module is constructed. The test results show that the mean of the average precision is improved by 4.81% compared with YOLOV3

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

A physics-driven model for the closed-loop quality control of remote laser welding

Remote Laser Welding (RLW) has grown in importance over conventional joining methods such as Gas Metal Arc Welding (GMAW), Resistance Spot Welding (RSW), Self-Pierce Riveting (SPR) since it offers advantages, such as weight reduction, high processing speed, ability to weld a wide range of metals, and better weld quality. Despite such advantages, it also poses several challenges that have prevented its widespread implementation in the industry. The presented thesis deals with the RLW of galvanized steel (i.e. zinc-coated steel) since it is widely used in the automotive industry due to better resistance to corrosion and better adhesion of the paint to the surface. However, RLW of such steel is challenging because the zinc vapour disturbs the molten pool resulting in weld defects. Therefore, RLW of galvanized steel is performed in overlap configuration with a joining gap to ventilate the zinc vapour from the welding area. An important challenge faced during the laser welding of galvanized steels is to achieve a consistent joining gap between two metals. If the gap is too wide, two metals do not join together. If the gap is too narrow, welding takes places with defects such as explosions, spatters and porosities. The maximum joining gap is controlled by the welding fixture; whereas, the minimum joining gap is controlled by the laser dimpling process (i.e. an upstream process). In the literature, the following research gaps have been identified regarding the laser dimpling process. These gaps are as follows: (i) lack key performance indicators to determine the dimple quality, (ii) lack a comprehensive characterization of dimpling process considering multi-inputs (i.e. key control characteristics) and multi-outputs (i.e. key performance indicators), and (iii) an effective implementation in a real manufacturing system taking into consideration process variation. Overcoming the aforementioned limitations in the literature, the presented thesis introduces proposes methodologies to develop: (i) surrogate models for dimpling process characterization considering multi-inputs and multi-outputs system by conducting physical experimentation, (ii) process capability spaces based on the developed surrogate models that allows the estimation of a desired process fallout rate in the case of violation of process requirements, and (iii) the optimization of the process parameters based on the developed process capability spaces. The weld quality is measured by key performance indicators defined in industrial standards (EN ISO 13919-1, 1997; EN ISO 13919-2, 2001). The weld must be produced such that each key performance indicator meets its defined allowable limits and any deviation from these limits is considered as a weld defect. The weld profile is important because the weld should have a desired profile for achieving the maximum strength. In this thesis, the weld profile is determined by penetration, top width, interface width (i.e. fusion zone dimensions). It must be pointed out that the presented fusion zone dimensions are difficult to measure directly during the welding process unless production is stopped which is nearly unfeasible as it is economically unjustified; whereas, it can be monitored by process signals (e.g. autistic, optical, thermal). Today, in-process monitoring is often provided by photodiodes or cameras. Owing to the lack of understanding of the process, it is limited to empirical correlations between the appearance of a weld defect and signal changes. The lack of methods linking (i) in-process monitoring data (e.g. visual sensing, acoustic and optical emissions); with, (ii) multi fusion zone dimensions (e.g. penetration, interface width, etc.), and (iii) welding process parameters (e.g. laser power, welding speed, focal point position) underscores the limitations of current data-driven in-process monitoring methods. Furthermore, the current in-process monitoring methods is an indirect measurement of fusion zone dimensions. Therefore, an accurate model to perform non-destructive measurement of fusion zone dimension is essential for on-line monitoring of laser welding as a part of quality assurance. Based on this requirement, the occurring physics in the laser welding process are decoupled by sequential modelling. It consists of three steps as follows: (i) calculating the laser intensity acting on the material, (ii) calculating the keyhole profile in using an analytic method, and (iii) solving the heat equation using the FEM to calculate the temperature distribution. After obtaining the temperature distribution, the fusion zone profile is defined by selecting an isotherm. Then, the aforementioned fusion zone dimensions (i.e. Penetration, Top Width, Interface Width) are measured from the calculated the fusion zone profile according to the industrial standard