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Moreover, the results confirm that the proposed model is a valuable tool for the prediction of residual stress, eliminating the costly experiments and time-consuming finite element simulations. However, the excessive preheating could have an opposite impact on residual stress accumulation. Results showed that preheating of the substrate could reduce the residual stress in an additively manufactured part due to the reduction in temperature gradient and resultant shrinkage stresses. To validate the predicted residual stress, experimental measurements are conducted using X-ray diffraction on IN718 samples manufactured via laser powder bed fusion under different process conditions. IN718 is used as a material example to pursue this investigation.

If the thermal stress exceeds the yield strength of the material, then the residual stress under cyclic heating and cooling will be calculated based on the incremental plasticity and kinematic hardening behavior of metal. The effect of preheating is super-positioned with initial temperature in the modeling of temperature profile using a moving heat source approach the resultant temperature gradient is then employed to predict the thermal stress from a point body load approach. In this study, an analytical model is proposed to predict the residual stress through the calculation of preheating affected temperature profile and thermal stress. This study aimed at the investigation of the effect of substrate temperature on residual stress in laser powder bed fusion using a physics-based analytical model. The model is found to be deficient but provides useful insights into future work in this direction. As a preliminary step towards tackling the second challenge, an analytical model is explored for determining the optimal sequence for scanning patterns in LPBF. The first challenge is addressed via a commercially available open-architecture LPBF machine. The proposed framework depends on having: (1) LPBF machines capable of adjusting scan sequence in real-time and (2) accurate and computationally efficient models and optimization approaches that can be efficiently executed online. The framework involves the use of physics-based models for online optimization of scan sequence, while data acquired from in-situ thermal sensors provide correction or calibration of the models. To address these weaknesses, we present a framework for intelligent online scan sequence optimization to achieve uniform temperature distribution in LPBF.

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However, scan sequence continues to be determined offline based on trial-and-error or heuristics, which are neither optimal nor generalizable. Several works have highlighted the important role (laser) scanning strategies, including laser power, scan speed, scan pattern and scan sequence, play in achieving uniform temperature distribution in LPBF. However, parts produced by LPBF are prone to residual stresses, deformations, and other defects linked to nonuniform temperature distribution during the process. Laser powder bed fusion (LPBF) is an increasingly popular approach for additive manufacturing (AM) of metals. Experimental measurement of residual stress was conducted using X-ray diffraction on the fabricated IN718 built via laser powder bed fusion to validate the proposed model.

As a result of the cyclic heating and cooling and the fact that the material is yielded, the residual stress build-up is precited using 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 JohnsonвЂ“Cook flow stress model is used to predict the yield surface of the part under repeated heating and cooling. The thermal stress is obtained using GreenвЂ™s function of stresses due to the point body load. Due to the high-temperature gradient in this process, the part experiences a high amount of thermal stress which may exceed the yield strength of the material. A moving point heat source approach is used to predict the temperature field by considering the effects of scan strategies, heat loss at partвЂ™s boundaries, and energy needed for solid-state phase transformation. Thus, a fully coupled thermomechanical analytical model is proposed to predict residual stress of the additively manufactured parts rapidly and accurately. Experimentations and numerical modeling of residual stress however are valuable but expensive and time-consuming. 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, automotive, and medical industries.

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