Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas.
This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.