5,307 research outputs found
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
Additive Manufacturing (AM) is a manufacturing paradigm that builds
three-dimensional objects from a computer-aided design model by successively
adding material layer by layer. AM has become very popular in the past decade
due to its utility for fast prototyping such as 3D printing as well as
manufacturing functional parts with complex geometries using processes such as
laser metal deposition that would be difficult to create using traditional
machining. As the process for creating an intricate part for an expensive metal
such as Titanium is prohibitive with respect to cost, computational models are
used to simulate the behavior of AM processes before the experimental run.
However, as the simulations are computationally costly and time-consuming for
predicting multiscale multi-physics phenomena in AM, physics-informed
data-driven machine-learning systems for predicting the behavior of AM
processes are immensely beneficial. Such models accelerate not only multiscale
simulation tools but also empower real-time control systems using in-situ data.
In this paper, we design and develop essential components of a scientific
framework for developing a data-driven model-based real-time control system.
Finite element methods are employed for solving time-dependent heat equations
and developing the database. The proposed framework uses extremely randomized
trees - an ensemble of bagged decision trees as the regression algorithm
iteratively using temperatures of prior voxels and laser information as inputs
to predict temperatures of subsequent voxels. The models achieve mean absolute
percentage errors below 1% for predicting temperature profiles for AM
processes. The code is made available for the research community at
https://github.com/paularindam/ml-iter-additive.Comment: 10 pages, 8 figure
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Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks
The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model
Online Two-stage Thermal History Prediction Method for Metal Additive Manufacturing of Thin Walls
This paper aims to propose an online two-stage thermal history prediction
method, which could be integrated into a metal AM process for performance
control. Based on the similarity of temperature curves (curve segments of a
temperature profile of one point) between any two successive layers, the first
stage of the proposed method designs a layer-to-layer prediction model to
estimate the temperature curves of the yet-to-print layer from measured
temperatures of certain points on the previously printed layer. With
measured/predicted temperature profiles of several points on the same layer,
the second stage proposes a reduced order model (ROM) (intra-layer prediction
model) to decompose and construct the temperature profiles of all points on the
same layer, which could be used to build the temperature field of the entire
layer. The training of ROM is performed with an extreme learning machine (ELM)
for computational efficiency. Fifteen wire arc AM experiments and nine
simulations are designed for thin walls with a fixed length and unidirectional
printing of each layer. The test results indicate that the proposed prediction
method could construct the thermal history of a yet-to-print layer within 0.1
seconds on a low-cost desktop computer. Meanwhile, the method has acceptable
generalization capability in most cases from lower layers to higher layers in
the same simulation, as well as from one simulation to a new simulation on
different AM process parameters. More importantly, after fine-tuning the
proposed method with limited experimental data, the relative errors of all
predicted temperature profiles on a new experiment are smaller than 0.09, which
demonstrates the applicability and generalization of the proposed two-stage
thermal history prediction method in online applications for metal AM.Comment: 30 pages, 21 figures, 2 table
Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks
Accurately predicting the temperature field in metal additive manufacturing
(AM) processes is critical to preventing overheating, adjusting process
parameters, and ensuring process stability. While physics-based computational
models offer precision, they are often time-consuming and unsuitable for
real-time predictions and online control in iterative design scenarios.
Conversely, machine learning models rely heavily on high-quality datasets,
which can be costly and challenging to obtain within the metal AM domain. Our
work addresses this by introducing a physics-informed neural network framework
specifically designed for temperature field prediction in metal AM. This
framework incorporates a physics-informed input, physics-informed loss
function, and a Convolutional Long Short-Term Memory (ConvLSTM) architecture.
Utilizing real-time temperature data from the process, our model predicts 2D
temperature fields for future timestamps across diverse geometries, deposition
patterns, and process parameters. We validate the proposed framework in two
scenarios: full-field temperature prediction for a thin wall and 2D temperature
field prediction for cylinder and cubic parts, demonstrating errors below 3%
and 1%, respectively. Our proposed framework exhibits the flexibility to be
applied across diverse scenarios with varying process parameters, geometries,
and deposition patterns.Comment: 42 pages, 13 Figure
Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators
High-fidelity, data-driven models that can quickly simulate thermal behavior
during additive manufacturing (AM) are crucial for improving the performance of
AM technologies in multiple areas, such as part design, process planning,
monitoring, and control. However, the complexities of part geometries make it
challenging for current models to maintain high accuracy across a wide range of
geometries. Additionally, many models report a low mean square error (MSE)
across the entire domain (part). However, in each time step, most areas of the
domain do not experience significant changes in temperature, except for the
heat-affected zones near recent depositions. Therefore, the MSE-based fidelity
measurement of the models may be overestimated.
This paper presents a data-driven model that uses Fourier Neural Operator to
capture the local temperature evolution during the additive manufacturing
process. In addition, the authors propose to evaluate the model using the
metric, which provides a relative measure of the model's performance compared
to using mean temperature as a prediction. The model was tested on numerical
simulations based on the Discontinuous Galerkin Finite Element Method for the
Direct Energy Deposition process, and the results demonstrate that the model
achieves high fidelity as measured by and maintains generalizability to
geometries that were not included in the training process
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Laser-directed-energy deposition (DED) offers advantages in additive
manufacturing (AM) for creating intricate geometries and material grading. Yet,
challenges like material inconsistency and part variability remain, mainly due
to its layer-wise fabrication. A key issue is heat accumulation during DED,
which affects the material microstructure and properties. While closed-loop
control methods for heat management are common in DED research, few integrate
real-time monitoring, physics-based modeling, and control in a unified
framework. Our work presents a digital twin (DT) framework for real-time
predictive control of DED process parameters to meet specific design
objectives. We develop a surrogate model using Long Short-Term Memory
(LSTM)-based machine learning with Bayesian Inference to predict temperatures
in DED parts. This model predicts future temperature states in real time. We
also introduce Bayesian Optimization (BO) for Time Series Process Optimization
(BOTSPO), based on traditional BO but featuring a unique time series process
profile generator with reduced dimensions. BOTSPO dynamically optimizes
processes, identifying optimal laser power profiles to attain desired
mechanical properties. The established process trajectory guides online
optimizations, aiming to enhance performance. This paper outlines the digital
twin framework's components, promoting its integration into a comprehensive
system for AM.Comment: 12 Pages, 10 Figures, 1 Table, NAMRC Conferenc
Energy management system for biological 3D printing by the refinement of manifold model morphing in flexible grasping space
The use of 3D printing, or additive manufacturing, has gained significant
attention in recent years due to its potential for revolutionizing traditional
manufacturing processes. One key challenge in 3D printing is managing energy
consumption, as it directly impacts the cost, efficiency, and sustainability of
the process. In this paper, we propose an energy management system that
leverages the refinement of manifold model morphing in a flexible grasping
space, to reduce costs for biological 3D printing. The manifold model is a
mathematical representation of the 3D object to be printed, and the refinement
process involves optimizing the morphing parameters of the manifold model to
achieve desired printing outcomes. To enable flexibility in the grasping space,
we incorporate data-driven approaches, such as machine learning and data
augmentation techniques, to enhance the accuracy and robustness of the energy
management system. Our proposed system addresses the challenges of limited
sample data and complex morphologies of manifold models in layered additive
manufacturing. Our method is more applicable for soft robotics and
biomechanisms. We evaluate the performance of our system through extensive
experiments and demonstrate its effectiveness in predicting and managing energy
consumption in 3D printing processes. The results highlight the importance of
refining manifold model morphing in the flexible grasping space for achieving
energy-efficient 3D printing, contributing to the advancement of green and
sustainable manufacturing practices.Comment: 33 pages, 10 figures, Journa
Layer-to-Layer Feedback Control for Direct Energy Deposition Additive Manufacturing
Additive manufacturing (AM) has garnered much attention in recent years, some calling it the fourth industrial revolution. It was first used to create rapid prototypes, although recent efforts have been made to advance the technology towards production of functional parts. This requires advancement in the materials used in AM, as well as the ability to produce quality parts repeatably. More specifically, direct energy deposition (DED) of metal powders is a process capable of producing and repairing parts with complex geometries; however, it is not widely used in industry due to challenges with quality control. In this process, metal powder is dispensed from a nozzle and a laser beam melts the incident powder particles and a portion of the underlying surface. The melt pool is translated along the desired toolpath and parts are constructed in a layer-by-layer fashion. Several process inputs determine the deposition quality, including powder feed rate, laser power and nozzle speed. These inputs, as well as the environmental conditions, are subject to random fluctuations that can cause geometric defects during deposition. Such defects often propagate through to subsequent layers and are amplified, rendering the final part unusable. The objective of this work is to improve the geometric accuracy of parts produced by metal powder DED by designing and implementing process feedback control. The strategy involves measuring the part height after each layer and adjusting the nozzle speed trajectory for the next layer according to a designed control law. The major contributions of this work are 1. the construction of an open-architecture metal powder DED system that is capable of implementing layer-to-layer control and 2. the development of controllers to improve part morphology accuracy in metal powder DED --Abstract, p. i
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