Laser welding of additively manufactured thermoplastic components assisted by a neural network-based expert system

Laser transmission welding (LTW) is a known technique to join conventionally produced high volume thermoplastic parts, e.g. injected molded parts for the automotive sector. For using LTW for additively manufactured parts (usually prototypes, small series, or one-off products), this technique has to be evolved to overcome the difficulties in the part composition resulted in the additive manufacturing process itself. In comparison to the injection molding process, the additive manufacturing process leads to an inhomogeneous structure with trapped air inside the volume. Therefore, a change in the transmissivity results due to the additive manufacturing process. In this paper, a method is presented to enhance the weld seam quality of laser welded additively manufactured parts assisted by a neural network-based expert system. The designed expert system supports the user setting up the additive manufacturing process. With the results of a preliminary work, a neural network is trained to predict the transmissivity values of the transparent samples. To validate the expert system, specimen of transparent polylactide are additively manufactured with various manufacturing parameters in order to change the transmissivity. The transmissivity of the parts are measured with a spectroscope. The parameters of the additive manufacturing process are used to predict the transmissivity with the neural network and are compared to the measurements. The transparent samples are welded to black polylactide samples with different laser power in overlap configuration and shear tensile tests are performed. With these experiments, the prediction of additive manufacturing parameters with the expert system in order to use the parts for a LTW process is demonstrated.

Automated summary

Laser transmission welding (LTW) is a technique used to join thermoplastic parts, and a neural network-based expert system can enhance the weld seam quality of additively manufactured parts. Experimental results demonstrate the predictive ability of the expert system for additive manufacturing parameters.