Selective laser melting (SLM) is one of the widely used techniques in metallic additive manufacturing, in which high-density laser powder is utilized to selectively melting layers of powders to create geometrically complex parts. Temperature distribution and molten pool geometry directly determine the balling effect, and concentrated balling phenomenon significantly deteriorates surface integrity and mechanical properties of the part. Finite element models have been developed to predict temperature distribution and molten pool geometry, but they were computationally expensive. In this paper, the three-dimensional temperature distributions are predicted by analytical models using point moving heat source and semi-ellipsoidal moving source respectively. The molten pool dimensions under various process conditions are obtained from the three-dimensional temperature predictions and experimentally validated. Ti-6Al-4V alloy is chosen for the investigation. Good agreements between the predictions and the measurements are observed. The presented models are also suitable for other metallic materials in the SLM process.

Rapid and accurate prediction of residual stress in metal additive manufacturing processes is of great importance to guarantee the quality of the fabricated part to be used in a mission-critical application in the aerospace and automotive industries. Experimentation and numerical modeling are valuable tools for measuring and predicting the residual stress; however, to-date conducting experimentation and numerical modeling is expensive and time-consuming. Thus, herein, a physics-based thermomechanical analytical model is proposed to predict the residual stress of the additively manufactured part rapidly and accurately. A moving point heat source approach is used to predict the temperature field by considering the effects of scan strategies, heat loss, and energy needed for solid-state phase transformation. Due to the high temperature gradient in this process, part experiences a high amount of thermal stress following solidification which may exceed the yield strength of the material. The thermal stress is obtained using Green’s function of stresses due to the point body load. The Johnson-Cook flow stress model is used to predict the yield surface of the part under repeated heating and cooling. As a result of the cyclic heating and cooling and the fact that the material is yielded, the residual stress build-up is predicted based on incremental plasticity and kinematic hardening behavior of the metal according to the property of volume invariance in plastic deformation in coupling with the equilibrium and compatibility conditions. The computational methodology is realized with the laser powder fusion of maraging steel 350 as a material of example. The validation of the predictive models has been presented in terms of the comparison of predicted and measured scan-direction and build-direction residual stress distributions along depth of build under various process parameter combinations. Moreover, for the first time, the Jonson-Cook parameters of maraging steel 350 are predicted using analytical modeling of machining forces and non-linear optimization techniques.