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Fusion welding processes are widely used for joining metal structures, such as pipes, ships, and cars. In general, these joining processes offer a very good compromise between reliability, safety, cost and maintenance which are important issues in the current economical context. The negative heat effects of welding, i.e. distortions and residual stresses of the welded parts, are well known and many researches in this field have already been done in the last decades in order to minimize them. On the experimental side, many sophisticated procedures have become state of the art to deal with this problem. On the computational side, the improvement of the simulation algorithms and the computing power enables the simulations of many physical phenomena occurring during the welding process. The implementation of welding simulation techniques is nevertheless not an easy task and often associated with expert knowledge which hinders their global application in an industrial environment. This paper is focused on the industrial requirements of a welding simulation software with special respect to the needs of the automotive industry. The necessary information to run a welding simulation and the expectations of a weld specialist without deep knowledge in numerical methods are investigated. These expectations are tested on an automotive welded assembly with a commercially available welding simulation software designed especially for the needs of the automotive industry. A welding experiment is done and the measured temperature distributions and distortions serve as reference to validate the simulation results. The result quality of the simulations of temperature fields and distortions is in best agreement with experimental data. The workflow is well adapted for the considered industrial requirements and the time-tosolution as well as the computational costs are acceptable, whereas the efficient calibration of the heat input model is still a point which will be further investigated in current and future research works.
The method described here allows to determine the size of the thermal contact between two metal sheets joined by spot welding. This size is a measure for the size of the weld nugget, i.e. the zone melted during the welding process, and thus the quality of the welded joint. The method applies active thermography in transmission or reflection setup. Especially the reflection setup offers an attractive possibility for non-destructive testing when components can be accessed from one side only. The spot weld region is optically heated by laser or flash light radiation. The weld nugget provides the mechanical joint, but also constitutes a thermal bridge between the two welded sheets. The latter will be exploited in this method. The better thermal contact at the weld Nugget contrasts with the surrounding material, where the heat transfer between the two sheets is comparatively low. A major advantage of the described method is the applicability on sheets without any surface treatment. This is achieved by a proper normalization of the data, allowing for a correction of the varying surface emissivity.
Spot welding is one of the most important technologies for joining sheet metal. While there are lot of approaches to non-destructive testing, quality assurance still mainly relies on welding parameter monitoring and destructive testing, leading to significant failure rates. In this paper an approach to spot weld testing using flash thermography is presented. The main focus of attention is on the identification of two typical error classes: stick welds and welds at the splash limit. Besides investigating the principal feasibility of thermography for zinc plated samples the results of a series test of spot welds joining 1 mm thick TRIP steel are shown. Based upon these results a statistical criterion is developed which allows a reliable classification of the named error classes.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.