Impact Of Surface And Pore Characteristics On Fatigue Life Of Laser Powder Bed Fusion Ti–6Al–4V Alloy Described By Neural Network Models

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti–6Al–4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half were machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty

Part-Scale Model for Fast Prediction of Thermal Distortion in DMLS Additive Manufacturing Part 2: A Quasi-Static Thermomechanical Model

The direct metal laser sintering (DMLS) additive manufacturing process can quickly produce complex parts with excellent mechanical properties. However, thermal stress accumulated in the layer-by-layer build cycles of DMLS may induce part distortion and even cause the failure of the whole build process. This paper is the second part of two companion papers that present a part-scale model for fast prediction of temperature history and part distortion in DMLS. In this paper, a quasi-static thermomechanical (QTM) model is built to estimate the thermal distortion of entire parts in DMLS. Firstly, the thermal contraction in each build cycle is modeled as a quasi-static loading process; the final thermal stress accumulated in the parts is the superposition of thermal stress generated in each build cycle. Secondly, the stress relaxation process after the parts are cut off from the substrate is modeled, and final distortion of the parts is predicted with thermal stress calculated from the thermal contraction processes. In comparison to existing transient thermomechanical models, the QTM can predict thermal distortion in DMLS with much faster computational speed, and a comparison against experiment shows less than 10% error.Mechanical Engineerin

Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 1, A Thermal Circuit Network Model

The additive manufacturing (AM) process metal powder bed fusion (PBF) can quickly produce complex parts with mechanical properties comparable to wrought materials. However, thermal stress accumulated during PBF induces part distortion, potentially yielding parts out of specification and frequently process failure. This manuscript is the first of two companion manuscripts that introduce a computationally efficient distortion and stress prediction algorithm that is designed to drastically reduce compute time when integrated in to a process design optimization routine. In this first manuscript, we introduce a thermal circuit network (TCN) model to estimate the part temperature history during PBF, a major computational bottleneck in PBF simulation. In the TCN model, we are modeling conductive heat transfer through both the part and support structure by dividing the part into thermal circuit elements (TCEs), which consists of thermal nodes represented by thermal capacitances that are connected by resistors, and then building the TCN in a layer-by-layer manner to replicate the PBF process. In comparison to conventional finite element method (FEM) thermal modeling, the TCN model predicts the temperature history of metal PBF AM parts with more than two orders of magnitude faster computational speed, while sacrificing less than 15% accuracy. The companion manuscript illustrates how the temperature history is integrated into a thermomechanical model to predict thermal stress and distortion

Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 2, A Quasi-static Thermo-mechanical Model

The additive manufacturing (AM) process metal powder bed fusion (PBF) can quickly produce complex parts with mechanical properties comparable to that of wrought materials. However, thermal stress accumulated during Metal PBF may induce part distortion and even cause failure of the entire process. This manuscript is the second part of two companion manuscripts that collectively present a part-scale simulation method for fast prediction of thermal distortion in Metal PBF. The first part provides a fast prediction of the temperature history in the part via a thermal circuit network (TCN) model. This second part uses the temperature history from the TCN to inform a model of thermal distortion using a quasi-static thermo-mechanical model (QTM). The QTM model distinguished two periods of Metal PBF, the thermal loading period and the stress relaxation period. In the thermal loading period, the layer-by-layer build cycles of Metal PBF are simulated, and the thermal stress accumulated in the build process is predicted. In the stress relaxation period, the removal of parts from the substrate is simulated, and the off-substrate part distortion and residual stress are predicted. Validation of part distortion predicted by the QTM model against both experiment and data in literature showed a relative error less than 20%. This QTM, together with the TCN, offers a framework for rapid, part-scale simulations of Metal PBF that can be used to optimize the build process and parameters

Optimization of Build Orientation for Minimum Thermal Distortion in DMLS Metallic Additive Manufacturing

The additive manufacturing (AM) process direct metal laser sintering (DMLS) can quickly produce complex parts. However, thermal stress in DMLS may induce thermal distortion and cause build failure. This manuscript presents an optimization algorithm for the build orientation in DMLS to minimize thermal distortion. The algorithm is built on the foundation of two coupled thermal and thermo-mechanical models developed in our previous work. The DIRECT search method and a universal objective function for thermal distortion were used. Constraints were included to rule out build orientations resulting in overheating or excessive oxidation. The optimization algorithm was tested on a rectangular bar and a complex, contoured part. Both parts were printed using an EOS M290 machine, and measured by a coordinate measuring machine. In comparison to build orientations chosen by two novice operators, the optimized build orientations gave significant reduction in the thermal distortion and number of print trials before print success.Mechanical Engineerin