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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
From 3D Models to 3D Prints: an Overview of the Processing Pipeline
Due to the wide diffusion of 3D printing technologies, geometric algorithms
for Additive Manufacturing are being invented at an impressive speed. Each
single step, in particular along the Process Planning pipeline, can now count
on dozens of methods that prepare the 3D model for fabrication, while analysing
and optimizing geometry and machine instructions for various objectives. This
report provides a classification of this huge state of the art, and elicits the
relation between each single algorithm and a list of desirable objectives
during Process Planning. The objectives themselves are listed and discussed,
along with possible needs for tradeoffs. Additive Manufacturing technologies
are broadly categorized to explicitly relate classes of devices and supported
features. Finally, this report offers an analysis of the state of the art while
discussing open and challenging problems from both an academic and an
industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and
Innovation action; Grant agreement N. 68044
Towards early estimation of part accuracy in additive manufacturing
Additive manufacturing (AM) is becoming more diffused. In spite of its advantages: capability to manufacture complex internal feature and material efficiency, AM has inherent drawback from its layer-by-layer nature. "Staircase effect" is observed due to the slicing process of the computer model in which a rough surface from a theoretically smooth surface will be obtained. Hence, there will be a deviation of the produced part from its nominal model. A methodology to predict the deviation of computer model of an additive manufactured part after fabrication process is presented. A case study is proposed using cylindrical features due to its common real case application. Cylinder is a representation of pin-hole geometry. This geometry is an assembly feature which is very important to guarantee the parts can be assembled with their pair. The dimensional and geometric deviation of the cylindrical feature after fabrication is estimated and could be a useful information for the designer
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
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
Modeling, Simulation and Data Processing for Additive Manufacturing
Additive manufacturing (AM) or, more commonly, 3D printing is one of the fundamental elements of Industry 4.0. and the fourth industrial revolution. It has shown its potential example in the medical, automotive, aerospace, and spare part sectors. Personal manufacturing, complex and optimized parts, short series manufacturing and local on-demand manufacturing are some of the current benefits. Businesses based on AM have experienced double-digit growth in recent years. Accordingly, we have witnessed considerable efforts in developing processes and materials in terms of speed, costs, and availability. These open up new applications and business case possibilities all the time, which were not previously in existence. Most research has focused on material and AM process development or effort to utilize existing materials and processes for industrial applications. However, improving the understanding and simulation of materials and AM process and understanding the effect of different steps in the AM workflow can increase the performance even more. The best way of benefit of AM is to understand all the steps related to that—from the design and simulation to additive manufacturing and post-processing ending the actual application.The objective of this Special Issue was to provide a forum for researchers and practitioners to exchange their latest achievements and identify critical issues and challenges for future investigations on “Modeling, Simulation and Data Processing for Additive Manufacturing”. The Special Issue consists of 10 original full-length articles on the topic
Predicting Geometric Errors and Failures in Additive Manufacturing
Additive manufacturing is a process that has facilitated the cost effective
production of complicated designs. Objects fabricated via additive
manufacturing technologies often suffer from dimensional accuracy issues and
other part specific problems such as thin part robustness, overhang geometries
that may collapse, support structures that cannot be removed, engraved and
embossed details that are indistinguishable. In this work we present an
approach to predict the dimensional accuracy per vertex and per part.
Furthermore, we provide a framework for estimating the probability that a model
is fabricated correctly via an additive manufacturing technology for a specific
application. This framework can be applied to several 3D printing technologies
and applications. In the context of this paper, a thorough experimental
evaluation is presented for binder jetting technology and applications.Comment: This version has been published in the Rapid Prototyping Journal
(2023
Auralization of Air Vehicle Noise for Community Noise Assessment
This paper serves as an introduction to air vehicle noise auralization and documents the current state-of-the-art. Auralization of flyover noise considers the source, path, and receiver as part of a time marching simulation. Two approaches are offered; a time domain approach performs synthesis followed by propagation, while a frequency domain approach performs propagation followed by synthesis. Source noise description methods are offered for isolated and installed propulsion system and airframe noise sources for a wide range of air vehicles. Methods for synthesis of broadband, discrete tones, steady and unsteady periodic, and a periodic sources are presented, and propagation methods and receiver considerations are discussed. Auralizations applied to vehicles ranging from large transport aircraft to small unmanned aerial systems demonstrate current capabilities
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