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<abstract> <p>Laser Powder Bed Fusion (LPBF) process is one of the advanced Additive Manufacturing (AM) processes, which is employed for the fabrication of complex metallic components. One of the major drawbacks of the LPBF is the development of residual stresses due to the high temperature gradients developed during the process thermal cycles. Reliable models for the prediction of residual strain and stress at part scale are required to support the LPBF process optimization. Due to the computational cost of the LPBF simulation, the current modelling methodology utilizes assumptions to make feasible the prediction of residual stresses at parts or component level. To this scope, a thermomechanical modelling approach for the simulation of LPBF process is presented with focus to residual stress and strain prediction. The modelling efficiency of the proposed approach was tested on a series on cases for which experimental data were available. The good comparison between the predicted and experimental data validated the modelling method. The efficiency of the thermomechanical modelling method is demonstrated by the reduced computational time required.</p> </abstract>

Metal Additive Manufacturing (AM) allows the fabrication of intricate shaped parts that cannot be produced with conventional manufacturing techniques. Despite the advantages of this novel manufacturing technology, the main drawback is the inferior fatigue performance of AM metal materials and parts due to the presence of process-induced defects that act as initial cracks. Reliable fatigue modeling methods that can assist the design and characterization of AM components must be developed. In this work, a computational damage-tolerance framework for the fatigue analysis of the AM metals and parts is presented. First, thermal modeling of the AM process for the part fabrication is performed to predict the susceptible areas for defect formation in the parts. From the processing of results, the characteristics of the critical defect are determined and used as input in a fracture mechanics-based model for the prediction of fatigue life of AM metals and parts. For validation purposes, the framework is utilized for the fatigue modeling and analysis of AM Ti-6Al-4V and 316L SS metals of relative experimental test cases found in the literature. The predicted results exhibit good correlation with the available experimental data, demonstrating the predictive capability of the modeling procedure.

One of the main challenges encountered in the Laser-based Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) process is the fabrication of defect-free parts. The presence of defects severely degrades the mechanical performance of AM parts and especially their fatigue strength. The most popular and reliable method to assess the ability of the employed process parameters for the fabrication of full-density parts is the process windows map, also known as printability map. However, the experimental procedure for the design of the printability maps and the identification of the optimum-density process parameters is usually time-consuming and expensive. In the present work, a modelling framework is presented for the determination of a printability map and the optimization of the L-PBF process based on the prediction and characterization of melt-pool geometric features and the prediction of porosity of small samples of 316L SS and Ti-6Al-4V metal alloys. The results are compared with available experimental data and present a good correlation, verifying the modelling methodology. The suitability of the employed defect criteria for each material and the effect of the hatch-spacing process parameter on the optimum-density parameters are also presented.

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