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

On the use of laser-scanning vibrometry for mechanical performance evaluation of 3D printed specimens

In this study, we explored the suitability of laser-scanning vibrometry (LSV) for evaluation of the mechanical behavior of rectangular prisms produced by Fused Filament Fabrication (FFF). Our hypothesis was that LSV would be able to discriminate the mechanical behavior of specimens fabricated with different process parameters combinations. Build orientation, raster angle, nozzle temperature, printing speed and layer thickness were the process parameters of interest. Based on a factorial design of experiment approach, 48 different process parameter combinations were taken into account and 96 polylactic acid (PLA) rectangular prisms were fabricated. The characterization of their dynamical behavior provided frequency data, making possible the computation of an equivalent elastic modulus metric. Statistical analysis of the equivalent elastic modulus dataset confirmed the significant influences of raster angle, build orientation and nozzle temperature. Moreover, multivariate regression models served to rank, not only the significant influences of individual process parameters, but also the significant quadratic and cubic interactions between them. The previous knowledge was then applied to generate an ad hoc model selecting the most important factors (linear and interactions). The predicted equivalent elastic moduli provided by our ad hoc model were used in modal analysis simulations of both 3D printed rectangular prisms and a complex part. The simulated frequencies thus obtained were generally closer to the experimental ones (=11%), as compared to modal analysis simulations based on internal geometry modelling (=33%). The use of LSV appears very promising in the characterization of the mechanical behavior and integrity of 3D printed parts. Other additive manufacturing technologies may benefit from the use of this technique and from the adoption of the presented methodology to test, simulate and optimize the properties of 3D printed products. © 2021 The Author

The application of rapid prototyping technology and FMEA quality tool in the development of automotive component

The purpose of this research is to study and apply the rapid prototyping technique using fused deposition modeling (FDM) in improving the quality of an automotive part. A machine that works under FDM principle will be used to produce the final product. An automotive component has been selected as the product for this research and application. Before continuing the fabrication of product, the critical path that must be considered is the design improvement to the target product. Failure mode effect analysis (FMEA) introduced as a tool in to improve the quality design to a better product level. The analysis from FMEA tool is translated to the design and product development. The research's findings however focus to only a selected part in automotive component. This result is inapplicable to other automotive component. Selection of material for fabrication limited to the machine availability and ability. This is the first known research that adopts a FDM machine to improve the existing product without ignoring the product functionality in real condition

Development of mirror coatings for gravitational-wave detectors

Gravitational waves are detected by measuring length changes between mirrors in the arms of kilometrelong Michelson interferometers. Brownian thermal noise arising from thermal vibrations of the mirrors can limit the sensitivity to distance changes between the mirrors, and, therefore, the ability to measure gravitational-wave signals. Thermal noise arising from the highly reflective mirror coatings will limit the sensitivity both of current detectors (when they reach design performance) and of planned future detectors. Therefore, the development of coatings with low thermal noise, which at the same time meet strict optical requirements, is of great importance. This article gives an overview of the current status of coatings and of the different approaches for coating improvement. This article is part of a discussion meeting issue ‘The promises of gravitational-wave astronomy’

Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.Comment: 8 pages, 10 figures. This paper has been accepted to be published in the proceedings of IDETC-CIE 202

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

3D printing of medicines: current challenges

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, 2021, Universidade de Lisboa, Faculdade de Farmácia.A impressão tridimensional tem vindo a ganhar relevância no desenvolvimento científico e, inevitavelmente, na área farmacêutica. Esta tecnologia permite o desenvolvimento de formulações individualizadas, ajustadas às necessidades do doente e, por isso, pode vir a tornar-se uma ajuda valiosa na área dos medicamentos órfãos. Para além disto, também permite o desenvolvimento de formas farmacêuticas com várias substâncias ativas e/ou diferentes perfis de libertação de fármaco, que poderá vir a permitir um aumento da adesão à terapêutica por parte dos doentes polimedicados. Apesar de atualmente já haver um fármaco impresso aprovado pela FDA desde 2015, o Spritam®, ainda há várias limitações associadas a esta tecnologia, nomeadamente a regulamentação, matérias-primas, controlo do processo e validação do mesmo, controlo de qualidade, estabilidade e a localização na cadeia de fabrico. Quanto à regulamentação, não havendo diretivas regulamentares específicas para esta tecnologia na área farmacêutica, acaba por se adaptar a regulamentação existente. A escolha das matérias-primas é limitada pela capacidade de impressão e a estabilidade físico-química, reduzindo a panóplia de materiais adequados para esta técnica. Para o controlo do processo seria benéfico adaptar um controlo em tempo real optando, preferencialmente, por métodos não destrutivos, pois não sendo esta tecnologia a ideal para produção em larga escala, a perda de qualquer unidade teria um peso negativo significativo no balanço geral do processo. A validação do processo deve ser elaborada de forma a garantir a qualidade, segurança e eficácia do medicamento. Para isso, é necessário validar não só o software, como todo o processo. No controlo de qualidade, mais uma vez, deve-se optar por métodos não destrutivos e selecionar, pelo menos, um para avaliar o sucesso da impressão, sendo que pode ser utilizada o Quality by Design como uma ferramenta para otimizar o processo. A estabilidade, tal como nos outros processos, também deve ser testada e a localização da impressão tridimensional no ciclo do medicamento é outra questão levantada, uma vez que tanto poderá ter um papel na farmácia hospitalar ou comunitária, como na indústria farmacêutica ou, já numa hipótese remota, na casa do doente.Three-dimensional printing is a technique that has been drawing attention recently in the scientific community and, inevitably, in the pharmaceutical field. As allows the development of personalized medicine, adapted to the patient’s needs, it can be a valuable tool for orphan drugs. On the other hand, it also allows the development of dosage forms with various active pharmaceutical ingredients and/or with different drug release profiles, which can improve patient compliance. Although there is a printed medicine approved by FDA since 2015, Spritam®, there are still a few limitations in this methodology, as regulation, raw materials, process controls and validation, quality control, stability, and even location. In terms of regulation, there are no specific regulatory guidelines regarding this technology in the pharmaceutical area, however, a 3D printed drug product should be produced following the existing guidelines that can be adapted. In terms of raw materials, the range available is limited by printability and physicochemical stability, reducing the suitable materials. For process control, it would be advantageous to adopt a real-time control and, favour non-destructive techniques, as the loss of any unit would harm the overall balance of the process. Process validation should be designed to ensure the quality, safety, and efficacy of the drug product. Taking this into account is necessary to validate the software to the process itself. In terms of quality control, should go for non-destructive methods, once again, and is going to be needed to assess the success of the print. Quality by design can be used as a tool to optimize the process. As in other methodologies, stability test must be conducted and the location of the three-dimensional impression on the drug cycle is another issue that arises, as it may play a role in the hospital or community pharmacies, as in the pharmaceutical industry or, in a more remote hypothesis, at the patient’s home

Modelling, additive layer manufacturing and testing of interlocking structures for joined components

Modelling, additive layer manufacturing and testing of interlocking structures for joined component

Optimal design and freeform extrusion fabrication of functionally gradient smart parts

An extrusion-based additive manufacturing process, called the Ceramic On-Demand Extrusion (CODE) process, for producing three-dimensional ceramic components with near theoretical density was developed. In this process, an aqueous paste of ceramic particles with a very low binder content (\u3c1 vol%) is extruded through a moving nozzle at room temperature. After a layer is deposited, it is surrounded by oil (to a level just below the top surface of most recent layer) to preclude non-uniform evaporation from the sides. Infrared radiation is then used to partially, and uniformly, dry the just-deposited layer so that the yield stress of the paste increases and the part maintains its shape. The same procedure is repeated for every layer until part fabrication is completed. Sample parts made of alumina and fully stabilized zirconia were produced using this process and their mechanical properties including density, strength, Young\u27s modulus, Weibull modulus, toughness, and hardness were examined. Microstructural evaluation was also performed to measure the grain size, and critical flaw sizes were obtained. The results indicate that the proposed method enables fabrication of geometrically complex parts with superior mechanical properties. Furthermore, several methods were developed to increase the productivity of the CODE process and enable manufacturing of functionally graded materials with an optimum distribution of material composition. As an application of the CODE process, advanced ceramic components with embedded sapphire optical fiber sensors were fabricated and properties of parts and sensors were evaluated using standard test methods --Abstract, page iv

In-Situ Process Monitoring for Metal Additive Manufacturing (AM) Through Acoustic Technique

Additive Manufacturing (AM) is currently a widely used technology in different industries such as aerospace, medical, and consumer products. Previously it was mainly used for prototyping of the products, but now it is equally valuable for commercial product manufacturing. More profound understanding is still needed to track and identify defects during the AM process to ensure higher quality products with less material waste. Nondestructive testing becomes an essential form of testing for AM parts, where AE is one of the most used methods for in situ process monitoring. The Acoustic Emission (AE) approach has gained a reputation in nondestructive testing (NDT) as one of the most influential and proven techniques in numerous engineering fields. Material testing through Acoustic Emission (AE) has become one of the most popular techniques in AM because of its capability to detect defects and anomalies and monitor the progress of flaws. Various AE technique approaches have been under investigation for in-situ monitoring of AM products. The preliminary results from AE exploration show promising results which need further investigation on data analysis and signal processing. AE monitoring technique allows finding the defects during the fabrication process, so that failure of the AM can be prevented, or the process condition can be finely tuned to avoid significant damages or waste of materials. In this work, recorded AE data over the Direct Energy Deposition (DED) additive manufacturing process was analyzed by the Machine Learning (ML) algorithm to classify different build conditions. The feature extraction method is used to obtain the required data for further processing. Wavelet transformation of signals has been used to acquire the time-frequency spectrum of the AE signals for different process conditions, and image processing by Convolutional Neural Network (CNN) is used to identify the transformed spectrum of different build conditions. The identifiers in AE signals are correlated to the part quality by statistical methods. The results show a promising approach for quality evaluation and process monitoring in AM. In this work, the assessment of deposition properties at different process conditions is also done by optical microscope, Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and nanoindentation technique