Purpose This study aims to characterize the suitability of a direct extrusion process in the fused layer manufacturing (FLM)-method under processing of granulated plastics. Design/methodology/approach In this paper, a granulate-based direct extrusion system in the FLM method is presented. This system is characterized with respect to the strand deposition mechanism and resulting component properties (geometrically and mechanically). Findings The extruder output could be identified as a linear relation between the applied extruder speed and the resulting mass flow. A developed model for the material and temperature-dependent strand deposition process was validated under experimental investigations. Further, it was possible to define process windows to realize desired strand widths and strand heights. In addition, analyses were conducted to determine the tensile strength transversely to the orientation of the layer plane. Research limitations/implications The extrusion system was characterized under the processing of materials ABS Magnum 8434 and PLA Ingeo 4043D. Due to the restricted choice of materials, further investigations are planned under an extension of the test materials. Furthermore, the degree of the geometric complexity of the test components should be increased to finally characterize the process. Originality/value By means of the characterization of the direct extrusion system, it is possible for users to classify the process and to use the process in specific application areas. In comparison to filament-based extrusion systems, significant advantages can be achieved by means of direct extrusion. These include, for example, the use of less expensive work materials (by factor >10), the use of existing test certificates and the advantage of higher mechanical properties. This makes it possible to meet modern product requirements and to produce competitive components.

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This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible to ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for low latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model's performance further. This suggests that there is room for future enhancements in the study.

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