A 3D dynamic numerical approach for temperature and thermal stress distributions in multilayer laser solid freeform fabrication process

Abstract

This paper presents a 3D transient numerical approach for modeling the multilayer laser solid freeform fabrication (LSFF) process. Using this modeling approach, the geometry of the deposited material as well as temperature and thermal stress fields across the process domain can be predicted in a dynamic fashion. In the proposed method, coupled thermal and stress domains are numerically obtained assuming a decoupled interaction between the laser beam and powder stream. To predict the time-dependent geometry of the deposited material, once the melt pool boundary is obtained, the process domain is discretized in a cross-sectional fashion based on the powder feed rate, elapsed time, and intersection of the melt pool and powder stream area on the workpiece. Layers of additive material are then added onto the non-planar domain. Main process parameters affected by a multilayer deposition due to the formation of non-planar surfaces, such as powder catchment, are incorporated into the modeling approach to enhance the accuracy of the results. To demonstrate the proposed algorithm, fabrication of a four-layer thin wall of AISI 304 L stainless steel on a workpiece with the same material is modeled. The geometry of the wall, temperature, and stress fields across the modeling domain are dynamically predicted throughout the process. The model is used to investigate the effect of preheating and clamping the workpiece to the positioning table. Results show that preheating improves the process by reducing the thermal stresses as well as the settling time for the formation of a steady-state melt pool in the first layer. In addition, clamping the workpiece can also decrease thermal stresses at its critical locations (i.e. deposition region). In terms of geometrical aspects, the results show that the temperature and the thickness of the deposited layers increase at the end-points of layers 2–4. The reliability and the accuracy of the model are experimentally verified.