More Like this

1. Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization

   Aboutaleb, A.M.; Bian, L.; Shamsaei, N.; Thompson, S.M.; Rao, P.K. (August 2016, Annual International Solid Freeform Fabrication Symposium)

   Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods. 

   more » « less
2. Grid Search Hyperparameter Tuning in Additive Manufacturing Processes

   Ogunsanya, M.; Isichei, J.; Desai, S. (July 2023, Manufacturing letters)

   In Machine learning (ML) and deep learning (DL), hyperparameter tuning is the process of selecting the combination of optimal hyperparameters that give the best performance. Thus, the behavior of some machine learning (ML) and deep learning (DL) algorithms largely depend on their hyperparameters. While there has been a rapid growth in the application of machine learning (ML) and deep learning (DL) algorithms to Additive manufacturing (AM) techniques, little to no attention has been paid to carefully selecting and optimizing the hyperparameters of these algorithms in order to investigate their influence and achieve the best possible model performance. In this work, we demonstrate the effect of a grid search hyperparameter tuning technique on a Multilayer perceptron (MLP) model using datasets obtained from a Fused Filament Fabrication (FFF) AM process. The FFF dataset was extracted from the MakerBot MethodX 3D printer using internet of things (IoT) sensors. Three (3) hyperparameters were considered – the number of neurons in the hidden layer, learning rate, and the number of epochs. In addition, two different train-to-test ratios were considered to investigate their effects on the AM process data. The dataset consisted of five (5) dominant input parameters which include layer thickness, build orientation, extrusion temperature, building temperature, and print speed and three (3) output parameters: dimension accuracy, porosity, and tensile strength. RMSE, and the computational time, CT, were both selected as the hyperparameter performance metrics. The experimental results reveal the optimal configuration of hyperparameters that contributed to the best performance of the MLP model. 

   more » « less
3. Thermo-mechanical physics-informed deep learning for prediction of thermal stress evolution in laser metal deposition

   Sharma, R; Guo, Y (June 2025, Engineering Applications of Artificial Intelligence)

   Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, this study introduces a physics-informed neural network (PINN) framework that incorporates governing physical laws into deep neural networks (NNs) to predict temperature and thermal stress evolution during the laser metal deposition (LMD) process. The study also discusses enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy. 

   more » « less
4. Pressure‐assisted binder jetting for additive manufacturing of mock energetic composites

   https://doi.org/10.1002/prep.202300175  

   Kirby, Levi; Udaykumar, H. S.; Song, Xuan (November 2023, Propellants, Explosives, Pyrotechnics)

   Abstract Additive manufacturing (AM) has emerged as a promising approach to achieve energetic materials (EMs) with intricate geometries and controlled microstructures, which are crucial for safety and performance optimization. However, current AM methods still face limitations such as limited densities and inadequate solids loading. To overcome these limitations, we have developed a pressure‐assisted binder jet (PBJ) process that has the potential to allow for the fabrication of intricate EMs while preserving their desired properties. This study aims to investigate the effects of printing parameters on the microstructures and properties of EMs, including density, solids loading, mechanical properties, and heterogeneity. Our results demonstrate that the PBJ process achieves exceptional properties in EMs, including densities up to 83.4 % and solids loading up to 95.4 %, surpassing those achieved by existing AM processes. Furthermore, the mechanical properties of the fabricated EMs are comparable to those achieved using conventional fabrication techniques, including a compressive strength of 3.32 MPa, a Young's modulus of 16.68 MPa, a Poisson's ratio of 0.45, a shear modulus of 5.73 MPa, and a bulk modulus of 21.01 GPa. Various test cases were printed to showcase the ability of the PBJ process to create EMs with complex structures and exceptional properties. Micro‐computed tomography was employed to analyze the influence of printing parameters on the internal composition and microstructures of the printed specimens. 

   more » « less
5. Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes

   https://doi.org/10.1038/s41524-024-01198-6  

   Amin, Abdullah Al; Li, Yangfan; Lu, Ye; Xie, Xiaoyu; Gan, Zhengtao; Mojumder, Satyajit; Wagner, Gregory J; Liu, Wing Kam (December 2024, npj Computational Materials)

   Abstract Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developedAdditiveManufacturingComputationalFluidDynamics code (AM-CFD) combined with a cylindrical heat source is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model is proposed to relate the Volumetric Energy Density (VED_σ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VED_σ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations. 

   more » « less