In order to use laser transmission welding (LTW) for additively manufactured parts such as prototypes, small series, or one-off products, an enhanced process knowledge is needed to overcome the difficulties in the part composition resulting from the additive manufacturing process itself. In comparison to an injection molding process for thermoplastic parts, the additive manufacturing process fused deposition modeling leads to an inhomogeneous structure with trapped air inside the volume. In this paper, a neural network-based expert system is presented that provides the user with process knowledge in order to improve the weld seam quality of laser welded additively manufactured parts. Both additive manufacturing and LTW process are assisted by the expert system. First, the designed expert system supports the user in setting up the additive manufacturing process to increase the transmissivity. During welding, the additive manufacturing and LTW process parameters are used to predict the weld seam strength. To create the database for the expert system, specimens of transparent and black polylactide are additively manufactured. In order to change the transmissivity at an emission wavelength of 940 nm of the diode laser used, the manufacturing parameters for the transparent parts are varied. The transmissivity of the parts is measured with a spectroscope. The transparent samples are welded to the black samples with laser powers between 8 and 14 W in the overlap configuration and shear tensile tests are performed. In this work, the predictions of the transmissivity and the shear tensile force are demonstrated with an accuracy of more than 88.1\% of the neural networks used for the expert system.