Optimizing Self-organizing Network for Thermal Simulation in Additive Manufacturing

Simulation modeling plays an important role in improving the understanding and control of manufacturing processes. Network representations encode complex structures into sparse adjacency matrices, which show great potential as a meshless alternative for thermal modeling in additive manufacturing (AM) processes. By representing heat transfer as a diffusion process over sparsely connected nodes, network models provide an opportunity for efficient simulation of thermal behaviors such as heat transfer and temperature gradients, thereby accelerating process-structure-property mapping and control of AM. However, very little has been done on constructing network simulation models that effectively capture the underlying part geometry. This paper presents a new self-organizing network approach for thermal simulation in AM processes. First, the part geometry is encoded into a sparse adjacency matrix, from which the original structural geometry can be recovered through a self-organizing process. Second, the governing diffusion equations are formulated and solved directly on the adjacency matrix to simulate heat transfer. Third, a new Markov Chain Monte Carlo (MCMC) formulation is proposed to stochastically calibrate the network model for thermal simulation. The proposed methodology is evaluated and validated through case studies of multi-layer AM processes. Experimental results show that the optimized self-organizing network not only reconstructs a geometry resembling the original part but also enables accurate and efficient simulation of thermal behaviors. This study establishes a generalized framework for optimal design and calibration of network-based thermal models, which demonstrates strong potential for improving the prediction and control of advanced manufacturing processes.