In times of an aging population, the implicit knowledge of specialists must be made accessible to technical systems and future employees. Currently, important decisions still require specialists, while planning software cannot be adapted according to previous results and with-it company specific attributes. This paper presents an artificial intelligence (AI) driven collaborative assistance system in production planning. The system combines ontology-based production planning with automated process planning and multi-criteria workforce allocation. It captures experiential knowledge and transforms it into ontology-based information by using AI. With the ontological database a partly automation of planning tasks is realized. For the use case of machining, the method increases the achievement of planning targets in a range of 74–87%.

This paper introduces a prediction model for the three-dimensional (3D) geometry of multi-layer single-track (wall-deposition) workpieces in Wire Arc Additive Manufacturing (WAAM), enabling accurate predictions with minimal experimental effort. The model extends a prior single-bead prediction model by incorporating four key enhancements: (1) using multiple cross-sections to capture the full wall geometry, (2) integration of additional input parameters to account for thermal history and deposition sequence, (3) development of an improved geometric-characterization function for better representation of wall geometry, and (4) employing a hybrid dataset composed of synthetic and experimental datasets acquired without specialized equipment, such as in-process geometry measurement systems, thereby simplifying the data collection process. A two-step transfer learning strategy was employed to pretrain the model on a synthetic dataset and subsequently train it using an experimental dataset. This approach enables accurate predictions, even when only a limited amount of experimental data is available. Compared with baseline models without transfer learning, the developed model achieved a substantial reduction in prediction errors, averaging improvements between 5–30 %. Specifically, it attained an error of approximately 10 % for height predictions and 15 % for width predictions. These contributions enhance the adaptability and scalability of the WAAM processes, thereby enabling more efficient and precise manufacturing.