Modeling Of Additive Manufacturing Processes Research Articles

Vat photopolymerization additive manufacturing process modeling: a thermal-chemical coupling approach informed by in-situ and ex-situ characterization data

Vat photopolymerization (VPP) is one of the most widely used additive manufacturing methods. The VPP process temperature and material curing reaction interplay with each other to critically determine the final product quality. Insights about the time-varying process temperature and degree of conversion (DoC) is desired for VPP process control but difficult to attain due to lacking effective operando characterization technologies. This work reports a new method to create a thermal-chemical model of the VPP process by solving an inverse heat conduction problem (IHCP) based on in-situ observable temperature measurement to estimate the chemistry reaction-induced heat source that is a function of DoC. Ex-situ photo differential scanning calorimetry (Photo-DSC) characterization is used to initialize the chemistry reaction model parameters so that DoC can be calculated. Specifically, vat substrate temperature is measured using an in-situ infrared thermal camera and used as input to solve an IHCP for estimating exothermic heat generation rate for the internal heat generation component at the curing part. Overall, the newly developed VPP modeling framework combines an IHCP that is optimized by in-situ thermal monitoring with a chemical reaction heat generation and conduction model that is educated by Photo-DSC characterization. The model predictions of temperature and DoC are experimentally validated by comparing against in-situ temperature measurement and ex-situ spectroscopy measurement of prints at different exposure times.

Development of a rapid solidification model for additive manufacturing process and application to Al-Si alloy

Accurate prediction of the solidification microstructure produced by additive manufacturing (AM) process is indispensable for the precise manufacturing process control. In the present study, a simple solidification model that can be applied to the powder bed fusion – laser beam (PBF-LB) process has been developed by integrating conventional and rapid solidification theories in literature. Based on the solidification parameters calculated by Rosenthal equation, the present solidification model can predict the primary dendrite arm spacing (PDAS), primary cell fraction, and solute profile using solute trapping, dendrite tip undercooling, and eutectic undercooling. Single track experiments for Al-12Si binary alloy were also performed to validate the accuracy of the model.

Modeling of additive manufacturing processes with time‐dependent material properties using physics‐informed neural networks

AbstractRecently, physics‐informed neural networks (PINNs) have been effectively utilized in a wide range of problems within the domains of applied mathematics and engineering. In PINNs, the governing physical equations are directly incorporated into the loss function of the network and a conventional labeled dataset is not required for its training. In order to successfully simulate the additive manufacturing processes with concrete, a novel process‐based FE‐simulation has been developed where the Drucker–Prager plasticity model is used as the material model. In this work, we will examine the deployment of a PINN to substitute the Newton–Raphson iterations that occur in the return‐mapping algorithm of the Drucker–Prager plasticity model.

Editorial: Advances in computational modeling of additive manufacturing processes

Editorial: Advances in computational modeling of additive manufacturing processes

Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks

Additive manufacturing (AM) is commonly used to produce builds with complex geometries. Despite recent advancements in data-driven modeling of AM processes, the generalizability of such models across a wide range of geometries has remained a challenge. Here, a graph-based representation using neural networks is proposed to capture spatiotemporal dependencies of thermal responses in AM processes. Our results tested on the Directed Energy Deposition process, indicate that our deep learning architecture accurately predicts long thermal histories for unseen geometries in the training process, offering a viable alternative to expensive computational mechanics or experimental solutions.

Microstructure affected residual stress prediction based on mechanical threshold stress in direct metal deposition of Ti-6Al-4 V

Although the material microstructure attributes play an important role in the mechanical properties of the additively manufactured (AM) part, their impact is largely ignored in the mechanics modeling of AM processes. A physics-based mechanical threshold stress (MTS) model is proposed to consider the material internal attributes, such as grain size and dislocation to dislocation barrier during simulation of the material flow stress and deformation. This model is based on the dislocation dynamics and thermal activation theory with a combination of three main components of athermal stress, thermal stress, and threshold stress. The MTS flow stress model is embedded into a fully coupled thermo-mechanical incremental plasticity model to predict the residual stress during AM processes from the prediction of temperature field and associated thermal stresses. The experimental measurements of residual stress are conducted on additively manufactured Ti-6Al-4 V samples for the model validation. The proposed model provides insight into the impact of microstructure on residual stress prediction in the additive manufacturing process modeling.

Shape Deviation Generator--A Convolution Framework for Learning and Predicting 3-D Printing Shape Accuracy

The 3-D shape accuracy is a critical performance measure for products built via additive manufacturing (AM). With advances in computing and increased accessibility of AM product data, machine learning for AM (ML4AM) has become a viable strategy for enhancing 3-D printing performance. A proper description of the 3-D shape formation through the layer-by-layer fabrication process is critical to ML4AM, such as feature selection and AM process modeling. The physics-based modeling and simulation approaches present voxel-level description of an object formation from points to lines, lines to surfaces, and surfaces to 3-D shapes. However, this computationally intensive modeling framework does not provide a clear structure for machine learning of AM data. Significant progress has been made to model and predict the shape accuracy of planar objects under data analytical frameworks. In order to predict, learn, and compensate for 3-D shape deviations using shape measurement data, we propose a shape deviation generator (SDG) under a novel convolution formulation to facilitate the learning and prediction of 3-D printing accuracy. The shape deviation representation, individual layer input function, and layer-to-layer transfer function for the convolution modeling framework are proposed and derived. A deconvolution problem for identifying the transfer function is formulated to capture the interlayer interaction and error accumulation effects in the layer-by-layer fabrication processes. Physics-informed sequential model estimation is developed to fully establish the SDG models. The Gaussian process regression is adopted to capture spatial correlations. The printed 2-D and 3-D shapes via a stereolithography (SLA) process are used to demonstrate the proposed modeling framework and derive new process insights for AM processes. Note to Practitioners —With advances in computing and increased availability of product data, machine learning for additive manufacturing (ML4AM) has become a viable strategy for enhancing 3-D printing accuracy. This work establishes the shape deviation generator (SDG) as a novel data analytical framework through a convolution formulation to model the 3-D shape formation in the AM process. This new engineering-informed machine-learning framework will facilitate the learning of AM data to establish models for geometric shape accuracy prediction and control.

Numerical verification of an Octree mesh coarsening strategy for simulating additive manufacturing processes

Thermo-mechanical finite element modeling of additive manufacturing processes, such as Directed Energy Deposition and Laser Powder Bed Fusion, has been widely applied for the prediction and mitigation of part distortion. However, as the size of modeled geometries gets larger, the number of nodes and elements required in the finite element mesh increases significantly. Because runtime will increase as more nodes are added, it is not practical to conduct full simulations of large builds using standard static meshes. Advanced meshing strategy is required to reduce the run time and to retain the accuracy of the prediction. In this work, a mesh coarsening strategy is evaluated for predicting temperature, distortion, and residual stress in additive manufacturing, aiming to achieve feasible run times with reasonable accuracy on large builds. Directed Energy Deposition of thin wall geometries built from Inconel® 625 and Ti6Al4V is used as a reference and models with 2 levels of Octree mesh coarsening are investigated. The thermal history, in situ distortion, residual stress, and run times are compared with previously experimentally validated static mesh results. Two levels of mesh coarsening is found to be the most effective case for both materials reducing the computational time by 75% while reporting less than 2.5% error for the peak distortion and negligible error for the thermal history difference as compared to the static mesh. Keeping two fine layers of elements underneath the heat source is found to be the most efficient in terms of prediction accuracy and run time.

On process-step parallel computability and linear superposition of mechanical responses in additive manufacturing process simulation

We study linearity assumptions in the transient macroscale mechanical aspect of additive manufacturing (AM) process simulation. Linearity assumptions are often resorted to in combination with calibrated inelastic deformation components to arrive at computationally tractable yet reasonably accurate AM process models. We point out that linearity assumptions permit the independent computation of the response increment in each step of the AM process, and the total mechanical response is the superposition of all the process-step increments. In effect, process-step increments are computed with respect to the stress-free reference configuration in each step. The implication is that the mechanical response increment in each linearised AM process step may be computed in parallel. Trivial process-step-wise parallel computability breaks down, however, if nonlinearity (i.e. geometric or material) is modelled. In our investigation the influence of geometric nonlinearity on part distortion is small (but this is of course part-geometry specific), and more realistic stresses are obtained by imposing a nonlinear elastoplastic material law after the parallel computation and superposition of the linear AM response increments. It is demonstrated that simulation wall-clock time is reduced by exploiting process-step parallel computability in the linear regime. Moreover, numerical experiments suggest that process-step parallelization scales better (in wall-clock time) than conventional parallelization in the sequential computation of each response increment.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Integrated component-support topology optimization for additive manufacturing with post-machining

PurposeThe purpose of this paper is to communicate a method to perform simultaneous topology optimization of component and support structures considering typical metal additive manufacturing (AM) restrictions and post-print machining requirements.Design/methodology/approachAn integrated topology optimization is proposed using two density fields: one describing the design and another defining the support layout. Using a simplified AM process model, critical overhang angle restrictions are imposed on the design. Through additional load cases and constraints, sufficient stiffness against subtractive machining loads is enforced. In addition, a way to handle non-design regions in an AM setting is introduced.FindingsThe proposed approach is found to be effective in producing printable optimized geometries with adequate stiffness against machining loads. It is shown that post-machining requirements can affect optimal support structure layout.Research limitations/implicationsThis study uses a simplified AM process model based on geometrical characteristics. A challenge remains to integrate more detailed physical AM process models to have direct control of stress, distortion and overheating.Practical implicationsThe presented method can accelerate and enhance the design of high performance parts for AM. The consideration of post-print aspects is expected to reduce the need for design adjustments after optimization.Originality/valueThe developed method is the first to combine AM printability and machining loads in a single topology optimization process. The formulation is general and can be applied to a wide range of performance and manufacturability requirements.

Current Issues of Developing Methodology and Software Solutions Used in Different Phases of Modelling Additive Production Processes

The mathematical methods and examples considered in the article allow efficient modeling of additive manufacturing processes by formulating a number of new criteria for geometry evaluation for compliance with the technological limitations of the additive manufacturing techniques. The aim of the research is the development of the new techniques, methods, algorithms and structured data aimed to validate the entire chain of additive manufacturing process.

A thermal-mechanical finite element workflow for directed energy deposition additive manufacturing process modeling

This work proposes a finite element (FE) analysis workflow to simulate directed energy deposition (DED) additive manufacturing at a macroscopic length scale (i.e. part length scale) and to predict thermal conditions during manufacturing, as well as distortions, strength and residual stresses at the completion of manufacturing. The proposed analysis method incorporates a multi-step FE workflow to elucidate the thermal and mechanical responses in laser engineered net shaping (LENS) manufacturing. For each time step, a thermal element activation scheme captures the material deposition process. Then, activated elements and their associated geometry are analyzed first thermally for heat flow due to radiation, convection, and conduction, and then mechanically for the resulting stresses, displacements, and material property evolution. Simulations agree with experimentally measured in situ thermal measurements for simple cylindrical build geometries, as well as general trends of local hardness distribution and plastic strain accumulation (represented by relative distribution of geometrically necessary dislocations).

On thermal modeling of Additive Manufacturing processes

A two-dimensional Finite Difference (FD) model of the thermal history of parts manufactured in powder bed fusion Additive Manufacturing (AM) processes is presented. The temperature of the part is calculated in each time-step taking into account the moving laser heat source, the melting phase change and functions of both temperature and porosity are used for the material thermal properties. Also, an algorithm for node birth and distance adaptation over time is utilized, minimizing computational time and memory. A validation of the results of the model is included.

Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities

One of the major barriers that hinder the realization of significant potential of metal-based additive manufacturing (AM) techniques is the variation in the quality of the manufactured parts. Uncertainty quantification (UQ) and uncertainty management (UM) can resolve this challenge based on the modeling and simulation of the AM process. This paper reviews the research state of the art and discusses needs and opportunities in the UQ/UM of the AM processes, with a focus on laser powder bed fusion AM. The major methods and models of laser powder bed fusion AM process are summarized first. The current research work in UQ of AM processes is then reviewed. Based on the review of AM process models and current UQ approaches for the AM process, this paper presents insights into how the current state of the art UQ and UM techniques can be applied to AM to improve the product quality. Future research needs in UQ and UM of AM are also discussed. Laser sintering of metal nanoparticles, which is part of the micro-AM process, is used as an example to illustrate the application of UQ and UM in the AM.

Effect of stress relaxation on distortion in additive manufacturing process modeling

A method for modeling the effect of stress relaxation at high temperatures during laser direct energy deposition processes is experimentally validated for Ti-6Al-4V samples subject to different inter-layer dwell times. The predicted mechanical responses are compared to those of Inconel® 625 samples, which experience no allotropic phase transformation, deposited under identical process conditions. The thermal response of workpieces in additive manufacturing is known to be strongly dependent on dwell time. In this work the dwell times used vary from 0 to 40s. Based on past research on ferretic steels and the additive manufacturing of titanium alloys it is assumed that the effect of transformation strain in Ti-6Al-4V acts to oppose all other strain components, effectively eliminating all residual stress at temperatures above 690°C. The model predicts that Inconel® 625 exhibits increasing distortion with decreasing dwell times but that Ti-6Al-4V displays the opposite behavior, with distortion dramatically decreasing with lowering dwell time. These predictions are accurate when compared with experimental in situ and post-process measurements.

Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process to provide illustrative examples of the proposed classification and metamodel approach. We describe how a coordinated approach can be used to extend modeling capabilities by promoting model composability. As part of future work, a framework is envisioned to realize a more coherent strategy for model development and deployment.