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Register a new userComprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing
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Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to its high level of automation, high deposition rates, and good quality of printed parts. In-process monitoring and feedback controls that would reduce the uncertainty in the quality of the material are in the early stages of development. Machine learning promises the ability to accelerate the adoption of new processes and property design in additive manufacturing by making process-structure-property connections between process setting inputs and material quality outcomes. The molten pool dimensional information and temperature are the indicators for achieving the high quality of the build, which can be directly controlled by processing parameters. For the purpose of in situ quality control, the process parameters should be controlled in real-time based on sensed information from the process, in particular the molten pool. Thus, the molten pool-process relations are of preliminary importance. This paper analyzes experimentally collected in situ sensing data from the molten pool under a set of controlled process parameters in a WLAM system. The variations in the steady-state and transient state of the molten pool are presented with respect to the change of independent process parameters. A multi-modality convolutional neural network (CNN) architecture is proposed for predicting the control parameter directly from the measurable molten pool sensor data for achieving desired geometric and microstructural properties. Dropout and regularization are applied to the CNN architecture to avoid the problem of overfitting. The results highlighted that the multi-modal CNN, which receives temperature profile as an external feature to the features extracted from the image data, has improved prediction performance compared to the image-based uni-modality CNN approach.
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Wire-feed laser additive manufacturing is an emerging fabrication technique capable of highly automated large-scale volume production that can reduce both material waste and overall cost while improving product lead times. Quality assurance is necessary for implementation into critical structural applications. However, the large number of process variables along with the cost associated with traditional trial and error methods makes this difficult. This study investigates a comprehensive quality framework based on learning from experimental data that will enable improved quality control along with consistent microstructural features of the part. Specifically, a comprehensive experimental data across multiple process variables and output characteristics in terms of overall bead quality, geometric shape (i.g. bead height, width, fusion zone depth, etc.), and microstructural features are collected. The predicted process-geometry-microstructure relations are then captured by virtue of data-driven machine learning models. The properties of printed beads are visualized based on an extensive range of processing space within a 3-dimensional contour map. The insights and impacts of process variables on bead morphology, geometric and microstructural features are comprehensively investigated for quality improvement during manufacturing processes.
The advancement of machine learning promises the ability to accelerate the adoption of new processes and property designs for metal additive manufacturing. The molten pool geometry and molten pool temperature are the significant indicators for the final parts geometric shape and microstructural properties for the Wire-feed laser direct energy deposition process. Thus, the molten pool condition-property relations are of preliminary importance for in situ quality assurance. To enable in situ quality monitoring of bead geometry and characterization properties, we need to continuously monitor the sensors data for molten pool dimensions and temperature for the Wire-feed laser additive manufacturing (WLAM) system. We first develop a machine learning convolutional neural network (CNN) model for establishing the correlations from the measurable molten pool image and temperature data directly to the geometric shape and microstructural properties. The multi-modality network receives both the camera image and temperature measurement as inputs, yielding the corresponding characterization properties of the final build part (e.g., fusion zone depth, alpha lath thickness). The performance of the CNN model is compared with the regression model as a baseline. The developed models enable molten pool condition-quality relations mapping for building quantitative and collaborative in situ quality estimation and assurance framework.
The future of space exploration missions will rely on technologies increasing their endurance and self-sufficiency, including for manufacturing objects on-demand. We propose a process for handling and additively manufacturing powders that functions independently of the gravitational environment and with no restriction on feedstock powder flowability. Based on a specific sequence of boundary loads applied to the granular packing, powder is transported to the printing zone, homogenized and put under compression to increase the density of the final part. The powder deposition process is validated by simulations that show the homogeneity and density of deposition to be insensitive to gravity and cohesion forces within the DEM model. We further provide an experimental proof of concept of the process by successfully 3D printing parts on-ground and in weightlessness, on parabolic flight. Powders exhibiting high and low flowability are used as model feedstock material to demonstrate the versatility of the process, opening the way for additive manufacturing of recycled material.
A thick electrode with high areal capacity has been developed as a strategy for high-energy-density lithium-ion batteries, but thick electrodes have difficulties in manufacturing and limitations in ion transport. Here, we reported a new manufacturing approach for ultra-thick electrode with aligned structure, called structure electrode additive manufacturing or SEAM, which aligns active materials to the through-thicknesses direction of electrodes using shear flow and a designed printing path. The ultra-thick electrodes with high loading of active materials, low tortuous structure, and good structure stability resulting from a simple and scalable SEAM lead to rapid ion transport and fast electrolyte infusion, delivering a higher areal capacity than slurry-casted thick electrodes. SEAM shows strengths in design flexibility and scalability, which allows the production of practical high energy/power density structure electrodes.
Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas due to its ability to rapidly produce, prototype, and customize designs. AM techniques afford significant opportunities in regard to nuclear materials, including an accelerated fabrication process and reduced cost. High-fidelity modeling and simulation (M&S) of AM processes is being developed in Idaho National Laboratory (INL)s Multiphysics Object-Oriented Simulation Environment (MOOSE) to support AM process optimization and provide a fundamental understanding of the various physical interactions involved. In this paper, we employ Bayesian inverse uncertainty quantification (UQ) to quantify the input uncertainties in a MOOSE-based melt pool model for AM. Inverse UQ is the process of inversely quantifying the input uncertainties while keeping model predictions consistent with the measurement data. The inverse UQ process takes into account uncertainties from the model, code, and data while simultaneously characterizing the uncertain distributions in the input parameters--rather than merely providing best-fit point estimates. We employ measurement data on melt pool geometry (lengths and depths) to quantify the uncertainties in several melt pool model parameters. Simulation results using the posterior uncertainties have shown improved agreement with experimental data, as compared to those using the prior nominal values. The resulting parameter uncertainties can be used to replace expert opinions in future uncertainty, sensitivity, and validation studies.
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