Closed-Loop High-Fidelity Simulation Integrating Finite Element Modeling With Feedback Controls in Additive Manufacturing

PurposeThis paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.Design/methodology/approachA systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.FindingsThe findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.Research limitations/implicationsThis paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.Originality/valueThis paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

On the effect of thin-wall thickness on melt pool dimensions in laser powder-bed fusion of Hastelloy X: Numerical modeling and experimental validation

Although the additive manufacturing (AM) market continues to grow, industries face barriers to AM adoption due to a shortage of skilled designers in the workforce that can apply AM effectively to meet this demand. This shortage is attributed to the high cost and infrastructural requirements of introducing high- barrier-to-entry AM processes such as powder bed fusion (PBF) into in-person learning environments. To meet the demands for a skilled AM workforce, it is important to explore other mediums of AM education, such as computer-aided instruction (CAI) and virtual reality (VR), which can increase access to hands-on learning experiences for inaccessible AM processes. However, limited work compares virtual and physical AM instruction or explores how the differences in immersion and presence between mediums can affect the knowledge gained and the mental effort exerted when learning about different AM processes. To address this gap in the literature, this research evaluates the use of CAI, VR, and in-person instruction in AM process education when learning about material extrusion (ME) and PBF. Our findings show that the differences in immersion and presence between CAI, VR, and in-person instruction do not have a statistically significant effect when learning about ME, but do have a significant effect when learning about PBF. Specifically, we found that VR generally yields equivalent effects in knowledge gain and cognitive load to in-person PBF education while offering advantages in both metrics over CAI learning. The findings from this work thus have significant implications for using VR as an alternative to in-person training to improve designer development in process-centric AM education of typically high-barrier-to-entry AM processes.

Integrated modelling and simulation of NiTi alloy by powder bed fusion: Single track study

Avoiding or eliminating thermal abnormalities in powder bed fusion (PBF) is critical since the abnormalities can lead to a higher failure rate of printing complex parts, a longer manufacturing lead time, and/or additional post-processing. Controlling the thermal evolution of the process can hinder or minimize some of the most frequently encountered thermal abnormalities. To achieve such an objective, the prediction and control of temperature distribution throughout an exposure layer is a crucial step. The generation of uniform temperature distribution throughout the printed layers and the avoidance of overheated zones are two primary sub-objectives for controlling the thermal evolution of the process. However, the complex and non-linear nature of the process has limited the ability to derive a universal analytical equation to correlate the process parameters with the thermal distribution of a printed layer. Laser specifications such as laser power and scanning speed are among the main process parameters that predominantly govern the temperature distribution throughout the layer. In this paper, we employ an artificial neural network (ANN) to correlate laser power with the temperature of the printed area around the melt pool in Inconel 718. In our first variant, we investigate the effectiveness of using the multilayer perceptron Radial Basis Neural Network (RBNN) to model the function for predicting the temperature distribution for various laser power. We use the Rosenthal equation to generate adequate inputs-outputs for training our function. We then compare the output with the simulation results for five different laser powers. The results show that the function was trained successfully with a low mean square root error of 9.7157 using 2000 samples, a wider gap exists between the trained function and the simulated data. In the second variant, we use a recurrent neural network (RNN), which enables temporal histories to be used for training. To fulfill such objective, we acquire real thermal data using a photon-counting IR camera for different printed layers. This step allows the training of a function to predict the temperature distribution precisely for different laser power and thermal history. As future work, we will employ the function to adjust the laser power to minimize the overheated zones and distribute the temperature uniformly throughout each exposure layer.KeywordsPowder bed fusion processArtificial neural networkFBFN neural networkTemperature predictionAdjusting laser power

Effect of thermal-fluidic transport on the temperature distribution and the melt pool in laser powder bed fusion of Ti6Al4V

Recently, Additive Manufacturing (AM) has been widely used in many applications. For a particular AM component, the choice of available AM processes is critical to the component’s quality, mechanical properties, and other important factors. In that context, this article presents an efficient decision support system for the selection of an appropriate AM process. A novel hybrid Multi-Criteria Decision Making (MCDM) technique has been proposed to select an appropriate AM process from available AM processes. The Best Worst Method (BWM) is used to determine optimal weights of criteria and the Proximity Indexed Value (PIV) method is employed to rank the available AM processes. For benchmarking the abilities of an AM process, a conceptual model of spur gear was fabricated by four available AM processes viz., Vat Photopolymerization (VatPP), Material Extrusion (ME), Powder Bed Fusion (PBF), and Material Jetting (MJ). Additionally, Dimensional accuracy (A), surface roughness (R), tensile strength (S), percentage elongation (%E), heat deflection temperature (HDT), process cost (PC) and build time (BT) has been considered as most significant criteria. Further, sensitivity analysis has been performed to validate the reliability of the results. The results suggested that the Material Jetting (MJ) process produces dimensionally accurate and quality parts among available alternatives AM processes. The ranking obtained using the PIV method is consistent and reliable.

Avoiding heat source calibration for finite element modeling of the laser powder bed fusion process

We present a powder-scale meshfree direct numerical simulation (DNS) capability for the powder bed fusion (PBF) based additive manufacturing (AM) processes using the novel Hot Optimal Transportation Meshfree (HOTM) method. The HOTM method is an incremental Lagrangian meshfree computational framework for materials behaviors under extreme thermomechanical loading conditions, which combines the Optimal Transportation Meshfree (OTM) method and the variational thermomechanical constitutive updates. The realistic multi-layer powder bed geometry is modeled explicitly in the HOTM simulations based on experimental data. A phase-aware constitutive model is developed to predict the phase change and multiphase mixing during the PBF AM processes automatically. The governing equations including the linear momentum and energy conservation equations are solved for the multiphase flow simultaneously to predict the deformation, temperature and local state of the powder particles. The powder-scale DNS is employed to study the influence of various laser powers on the melt pool thermodynamics.

The one-step AM process is considered the goal many researchers seek in the field of Additive Manufacturing (AM) of high-technology ceramics. Among the several AM techniques, only Powder Bed Fusion (PBF) can directly print high-technology ceramics using one step. However, the PBF technique faces numerous challenges to efficiently be employed in the PBF of ceramics. These challenges include the formation of cracks, generated thermal stress, effective laser–powder interaction, and low acquired relative density. This study developed a new preheating mechanism for ceramic materials using two laser systems to surpass beyond these challenges and successfully print ceramics with a single-step AM method. One laser is used to preheat the powder particles before the second laser is utilised to complete the melting/sintering process. Both lasers travel along the same scanning path. There is a slight delay (0.0001 s) between the preheating laser and the melting/sintering laser to guarantee that the melting/sintering laser scans a properly preheated powder. To further facilitate testing of the preheating system, a numerical model has been developed to simulate the preheating and melting process and to acquire proper process parameters. The developed numerical model was shown to determine the correct process parameters without needing costly and time-consuming experiments. Alumina samples (10 × 10 × 6 mm3) were successfully printed using alumina powder as feedstock. The surface of the samples was nearly defect-free. The samples’ relative densities exceeded 80%, the highest reported relative density for alumina produced by a single-step AM method. This discovery can significantly accelerate the transition to a one-step AM process of ceramics.

Powder bed fusion (PBF) is a popular additive manufacturing (AM) process with wide applications in key industrial sectors, including aerospace, automotive, healthcare, defence. However, a deficiency of PBF is its low quality of surface finish. A number of PBF process variables and other factors (e.g. powders, recoater) can influence the surface quality. It is of significant importance to measure and characterise PBF surfaces for the benefits of process optimisation, product performance evaluation and also product design. A state-of-the-art review is given to summarise the current research work on the characterisation of AM surfaces, particularly PBF surfaces. It is recognised that AM processes are different from conventional manufacturing processes and their produced surface topographies are different as well. In this paper, the surface characterisation framework is updated to reflect the unique characteristics of PBF processes. The surface spatial wavelength components and other process signature features are described and their production mechanisms are elaborated. A bespoke surface characterisation procedure is developed based on the updated framework. The robust Gaussian regression filter and the morphological filters are proposed to be used for the separation of the waviness component due to their robustness. The watershed segmentation is enhanced to extract globules from the residual surface. Two AM components produced by electron beam melting (EBM) and selective laser melting (SLM), are measured and characterised by the proposed methodology. Both of the two filters are qualified for the extraction of melted tracks. The watershed segmentation can enable the extraction of globules. The standard surface texture parameters of different surface wavelength components are compared. A set of bespoke parameters are intentionally developed to offer a quantitative evaluation of the globules.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

Electron Beam Additive Manufacturing of Ti <sub>6</sub>Al <sub>4</sub>V: Evolution of Powder Morphology and Part Microstructure with Powder Reuse

Fast prediction of thermal distortion in metal powder bed fusion additive manufacturing: Part 1, a thermal circuit network model

A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate model, behaves as a virtual twin of the real-world physical process. In terms of Laser Powder Bed Fusion (L-PBF) based additive manufacturing (AM), a DT can predict the current and future states of the melt pool and the resulting defects corresponding to the input laser parameters, evolve itself by assimilating in-situ sensor data, and optimize the laser parameters to mitigate defect formation. In this paper, we present a deep neural operator enabled DT framework for closed-loop feedback control of the L-PBF process. This is accomplished by building a physics-based computational model to accurately represent the melt pool states, an efficient Fourier neural operator (FNO) based surrogate model to approximate the melt pool solution field, followed by a physics-based procedure to extract information from the computed melt pool simulation that can further be correlated to the defect quantities of interest (e.g., surface roughness). An optimization algorithm is then exercised to control laser input and minimize defects. On the other hand, the constructed DT also evolves with the physical twin via offline finetuning and online material calibration. For instance, the probabilistic distribution of laser absorptivity can be updated to match the real-time captured thermal image data. Finally, a probabilistic framework is adopted for uncertainty quantification. The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing in L-PBF-based metal AM.