Investigation of Layer Based Thermal Behavior in Fused Deposition Modeling Process by Infrared Thermography

There are numerous research efforts that address the monitoring and control of additive manufacturing (AM) processes to improve part quality. Much less research exists on process monitoring and control of Fused Deposition Modeling (FDM). FDM is inherently a thermal process and thus, lends itself to being study by thermography. In this regard, there are various process parameters or process signatures such as built-bed temperature, temperature mapping of parts during deposition of layers, and the nozzle extrusion temperature that may monitor to optimize the quality of fabricated parts. In this work, we applied image based thermography layer by layer with the usage of an infrared camera to investigate the thermal behavior and thermal evolution of the FDM process for the standard samples printed by ABS filament. The combination of the layer based temperature profile plot and the temporal plot has been utilized to understand the temperature distribution and average temperature through the layers under fabrication. This information provides insights for potential modification of the scan strategy and optimization of process parameters in future research, based on the thermal evolution. Accordingly, this can reduce some frequent defects which have roots in thermal characteristics of the deposited layers and also, improve the surface quality and/or mechanical properties of the fabricated parts. In addition, this approach for monitoring the process will allow manufacturers to build, qualify, and certify parts with greater throughput and accelerate the proliferation of products into high-quality applications

A Study of Fused Deposition Modeling (FDM) 3-D Printing Using Mechanical Testing and Thermography

Indiana University-Purdue University Indianapolis (IUPUI)Fused deposition modeling (FDM) represents one of the most common techniques for rapid proto-typing in additive manufacturing (AM). This work applies image based thermography to monitor the FDM process in-situ. The nozzle temperature, print speed and print orientation were adjusted during the fabrication process of each specimen. Experimental and numerical analysis were performed on the fabricated specimens. The combination of the layer wise temperature profile plot and temporal plot provide insights for specimens fabricated in x, y and z-axis orientation. For the x-axis orientation build possessing 35 layers, Specimens B16 and B7 printed with nozzle temperature of 225 C and 235 C respectively, and at printing speed of 60 mm/s and 100 mm/s respectively with the former possessing the highest modulus, yield strength, and ultimate tensile strength. For the y-axis orientation build possessing 59 layers, Specimens B23, B14 and B8 printed with nozzle temperature of 215 C, 225 C and 235 C respectively, and at printing speed of 80 mm/s, 80 mm/s and 60 mm/s respectively with the former possessing the highest modulus and yield strength, while the latter the highest ultimate tensile strength. For the z-axis orientation build possessing 1256 layers, Specimens B6, B24 and B9 printed with nozzle temperature of 235 C, 235 C and 235 ➦C respectively, and at printing speed of 80 mm/s, 80 mm/s and 60 mm/s respectively with the former possessing the highest modulus and ultimate tensile strength, while B24 had the highest yield strength and B9 the lowest modulus, yield strength and ultimate tensile strength. The results show that the prints oriented in the y-axis orientation perform relatively better than prints in the x-axis and z-axis orientation

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

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

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

Spatio-temporal Analysis of Thermal Profiles in Extrusion-based Additive Manufacturing

Extrusion-based Additive Manufacturing (AM) processes have recently gained increasing attention in the scientific and industrial communities because of the wide range of processible materials (from thermoplastics to composite and biomaterials), printable volumes, and industrial applications. As for many other AM processes, the actual problems with process stability and repeatability are still limiting the industrial process adoption, as these problems can significantly impact on the final part quality. In this framework, a latest research trend aims at developing in-situ monitoring solutions for inline defect detection, in a zero-waste production perspective. Among the existing in-situ sensing techniques, many studies showed that in-situ thermography represents a viable solution to describe the temperature dynamic and validate the thermal models but very few approaches have been proposed to quantitively study the temperature evolution to quickly detect process instabilities. This paper presents a new approach to quickly analyse the temporal dynamic of temperature in the printed layer while providing a spatial mapping of the temperature homogeneities. Compared with previous methods, the current one has the main novelty feature of combining both the spatial and temporal signature in a synthetic mapping that allows to detect unstable or unusual problems. In order to show the effectiveness of the proposed solution, a real case study of Big Area Additive Manufacturing (BAAM) for composite materials is considered. The study shows that the provided method can clearly enhance defect detection and represents a new solution for detecting anomalous areas where thermal profiles behave differently with respect to the surrounding areas. The same methodology underlined the thermal evolution complexity in the BAAM case study and enabled the detection of local flaws, i.e., hot and cold spots

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure