Mechanical behavior of PA12 lattice structures produced by SLS

Dissertação de mestrado integrado em Engenharia de PolímerosTaking into account the rapid technological evolution and the growing demand, for the industrial sector to meet the most diverse needs of the market, Additive Manufacturing (AM) technology appears as a transformative approach to industrial production that enables the creation of lighter, stronger parts and systems. The versatility of this type of technology allows a reduction in production time and energy consumption, as well as, reducing material waste in the production of a product. It is in this last point that the technologies of AM stand out when comparing to the technologies of conventional manufacture. In AM technologies, it is possible to carry out the deposition of material in a controlled manner, where it is really necessary and, at the same time, ensure the necessary mechanical properties to meet the product requirements. Due to its versatility and rapid technological advances, it has become possible to implement typological optimization in AM. In this context, this study aims to investigate the mechanical behavior of lattice structures to support further investigations based on Topology Optimization (TO). The study of the mechanical behavior of these structures allows an intelligent distribution of these structures along a given structure in order to absorb the amount of energy needed for the impact, presenting competitive manufacturing times and costs. In the course of this research, the manufacturing technique to be used will focus on the Powder Bed Fusion (PBF) process, more specifically in the EOS P396 equipment with the polymeric material polyamide 12 (PA12), that will shape the desired lattice structures, which are constituted by different topologies and volume fractions. The purpose of this development is focused on obtaining the experimental mechanical properties of certain types of cellular structures in order to compare them with the properties obtained from the simulations. Thus, strut-based (BCC) and Triply Periodic Minimal Surfaces (Schwarz-P and Neovius) lattice structures were defined based on different independent variables, such as, cell size, strut diameter/ surface thickness and shell thickness. The defined structures were evaluated by compression and impact mechanical tests. It was found that beside geometrical design, the relative densities of the unit cells could also significantly influence the impact energy absorption performance.Tendo em conta a rápida evolução tecnológica e a crescente procura do sector industrial para satisfazer as mais diversas necessidades do mercado, as tecnologias de Fabrico Aditivo (FA) aparece como uma abordagem transformadora da produção industrial que permite a criação de peças e sistemas mais leves e fortes. A versatilidade deste tipo de tecnologia permite uma redução do tempo de produção e do consumo de energia, bem como a eliminação do desperdício de material na produção de um produto. É neste último ponto que as tecnologias de FA se destacam no que diz respeito às tecnologias de fabrico convencional. Nas tecnologias FA, é possível realizar a deposição de material de forma controlada, onde é realmente necessário, e ao mesmo tempo, garantir as propriedades mecânicas necessárias para satisfazer os requisitos do produto. Neste contexto, este estudo destina-se a investigar o comportamento mecânico de lattice structures para apoiar investigações posteriores que têm por base a Otimização Topológica (OT). O estudo do comportamento mecânico destas estruturas permite uma distribuição inteligente destas mesmas ao longo de uma determinada estrutura de forma a absorverem a quantidade de energia necessária ao impacto, apresentando tempos e custos de fabrico competitivos. No decurso desta investigação, a técnica de fabrico a ser utilizada centrou-se no processo de Powder Bed Fusion (PBF), mais especificamente no equipamento EOS P396 com o material polimérico poliamida 12 (PA12), que dará forma às lattice structures, constituídas por diferentes células unitárias e frações de volume. O objetivo deste desenvolvimento focou-se na obtenção das propriedades mecânicas experimentais das estruturas celulares de maneira a compará-las com as propriedades obtidas a partir das simulações. Assim, as lattice structures baseadas em strut-based (BCC) e Triply Periodic Minimal Surface (TPMS) (Schwarz-P e Neovius) foram definidas com base em diferentes variáveis independentes, tais como, tamanho da célula unitária, diâmetro da viga/ espessura da superfície e espessura da casca. As estruturas definidas foram avaliadas mecanicamente através de testes de compressão e impacto. Verificou-se assim que, para além do desenho geométrico, as densidades relativas das células unitárias também podiam influenciar significativamente o desempenho de absorção de energia de impacto

Design and Application of Additive Manufacturing

Additive manufacturing (AM) is continuously improving and offering innovative alternatives to conventional manufacturing techniques. The advantages of AM (design freedom, reduction in material waste, low-cost prototyping, etc.) can be exploited in different sectors by replacing or complementing traditional manufacturing methods. For this to happen, the combination of design, materials and technology must be deeply analyzed for every specific application. Despite the continuous progress of AM, there is still a need for further investigation in terms of design and applications to boost AM's implementation in the manufacturing industry or even in other sectors where short and personalized series productions could be useful, such as the medical sector. This Special Issue gathers a variety of research articles (12 peer-reviewed papers) involving the design and application of AM, including innovative design approaches where AM is applied to improve conventional methods or currently used techniques, design and modeling methodologies for specific AM applications, design optimization and new methods for the quality control and calibration of simulation methods

Analysing the evolution of aerospace ecosystem development.

Aerospace manufacturing industry is predicted to continue growing. Rising demand is triggering the current global aerospace ecosystem to evolve and adapt to challenges never faced before. New players into the aerospace manufacturing industry and the development of new ecosystems are evidencing its evolution. Understanding how the aerospace ecosystem has evolved is thus essential to prepare optimal conditions to nurture its growth. Recent studies have successfully combined economics and network science methods to map, analyse and predict the evolution of industrial ecosystems. In comparison to previous studies which apply network science-based methodologies to macro-economic research, this paper uses these methods to analyse the evolution of a particular industrial ecosystem, namely the aerospace sector. In particular, we develop bipartite country-product networks based on trade data over 25 years, to identify patterns and similarities in the evolution of developed aerospace manufacturing countries ecosystems. The analysis is elaborated at a macroscopic (network) and microscopic (nodes) levels. Motivated by studies in ecological networks, we use nestedness analysis to find patterns depicting the distribution and evolution of exported products across ecosystems. Our analysis reveals that developed ecosystems tend to become more analogous, as countries lean towards having a revealed comparative advantage (RCA) in the same group of products. Countries also tend to become more nested in their aerospace product space as they start developing a higher RCA. It is revealed that although countries develop an advantage on unique products, they also tend to increase competition with each other. Further analysis shows that manufactured products have a stronger correlation to an aerospace ecosystem than primary products; and in particular, the automotive sector shows the highest correlation with positive aerospace sector evolution. Competition between countries with well-developed aerospace ecosystems tends to centre on automotive parts, general industrial machinery, power generating machinery and equipment, and chemical materials and products

Fibre-reinforced additive manufacturing: from design guidelines to advanced lattice structures

In pursuit of achieving ultimate lightweight designs with additive manufacturing (AM), engineers across industries are increasingly gravitating towards composites and architected cellular solids; more precisely, fibre-reinforced polymers and functionally graded lattices (FGLs). Control over material anisotropy and the cell topology in design for AM (DfAM) offer immense scope for customising a part’s properties and for the efficient use of material. This research expands the knowledge on the design with fibre-reinforced AM (FRAM) and the elastic-plastic performance of FGLs. Novel toolpath strategies, design guidelines and assessment criteria for FRAM were developed. For this purpose, an open-source solution was proposed, successfully overcoming the limitations of commercial printers. The effect of infill patterns on structural performance, economy, and manufacturability was examined. It was demonstrated how print paths informed by stress trajectories and key geometric features can outperform conventional patterns, laying the groundwork for more sophisticated process planning. A compilation of the first comprehensive database on fibre-reinforced FGLs provided insights into the effect of grading on the elastic performance and energy absorption capability, subject to strut-and surface-based lattices, build direction and fibre volume fraction. It was elucidated how grading the unit cell density within a lattice offers the possibility of tailoring the stiffness and achieving higher energy absorption than ungraded lattices. Vice versa, grading the unit cell size of lattices yielded no effect on the performance and is thus exclusively governed by the density. These findings help exploit the lightweight potential of FGLs through better informed DfAM. A new and efficient methodology for predicting the elastic-plastic characteristics of FGLs under large strain deformation, assuming homogenised material properties, was presented. A phenomenological constitutive model that was calibrated based upon interpolated material data of uniform density lattices facilitated a computationally inexpensive simulation approach and thus helps streamline the design workflow with architected lattices.Open Acces

Modeling and analysis for unit cell size, material anisotropy and material imperfection effects of cellular structures fabricated by powder bed fusion additive manufacturing.

Cellular structures are networks of interconnected struts or walls with porosities and are widely found in many natural load-bearing structures such as plants and bones. Cellular structures offer unique functional characteristics such as high stiffness to weight ratio, tailorable heat transfer coefficient, and enhanced mechanical energy absorption, which makes them highly attractive in various engineering disciplines such as biomedical implants, electrodes, heat exchangers, and lightweight structures. There exists an abundance of literatures that have investigated various mechanical properties of various cellular structures such as Poisson’s ratio, elastic modulus, ultimate strength, yield strength, and failure characteristics. Based on the classic cellular structure model, these mechanical properties are highly dependent on both the relative density and the topologies of the unit cell designs. Cellular structures with higher relative densities generally exhibit higher overall mechanical properties. In addition, there also exist multiple general design rules for cellular unit cell topology designs, such as nodal connectivity-based deformation mechanism and re-entrant auxetic mechanism. However, currently most theoretical knowledge for cellular structures are established based on infinite pattern sizes, i.e. infinite numbers of unit cells along all principal symmetry directions. On the other hand, for the cellular structures with finite sizes that are commonly designed in real-world applications, in addition to relative density and cell topology, the cellular pattern size effects, which are introduced by the non-ideal boundary conditions, also plays important roles in determining the overall mechanical characteristics of the cellular structure. As a result, many equations and conclusions from the classic Ashby and Gibson models cannot be directly applied to these finite-size cellular structures, which significantly limits the designability of cellular structures for various dimension-limited applications. Besides, due to the complex geometry of cellular structures, additive manufacturing (AM) processes have been considered as the only practically viable option for their fabrication, which introduce various manufacturing-related design variables with material properties such as material anisotropy and material imperfection. In order to adequately design for cellular structures realized by AM processes, a modeling approach that enables comprehensive analysis of all these factors are desirable. In this work, an analytical model framework was established for the analysis of mechanical characteristics of the finite-size cellular structure with imperfect local material properties. The model was verified by the experimental results for both mechanical properties and cellular fracture failure propagation patterns with samples fabricated via powder bed fusion (PBF) process. The results showed that the models could not only provide good predictions to both average mechanical properties and their variabilities, but also adequately capture the effects of the finite pattern size effects and local material heterogeneity effects. Based on the established model, the topology-material-mechanical properties of the finite-size AM cellular structures were investigated in detail. More specifically, the effects of pattern size-topology, material anisotropy and the material imperfections were studied systematically. Various new insights were obtained, including: 1. Based on the modeling analysis, the effects of size and topology on the tensile failure behavior of multiple representative cellular structures (2D auxetic, 2D diamond, 2D triangular1 and 2D triangular2) under various geometry design conditions (including cell topology, cell size and number of unit cells) were systematically investigated. It was found that the 2D bending-dominated structures with lower nodal connectivity (number of struts that meet in joints) (2D auxetic and 2D diamond) exhibited a relatively progressive crack propagation pattern, while the 2D stretching-dominated structures with higher nodal connectivity (2D triangular1 and 2D triangular2) appear to exhibit rather catastrophic brittle fracture failure. During the failure fracture propagation, the energy absorption of the 2D stretching-dominated structures were significantly higher than that of the 2D bending-dominated structures. Moreover, for all cellular designs, the tensile failure behaviors tend to converge to more consistent patterns when the cellular structure pattern sizes increase beyond certain thresholds that are dependent on the cellular topology designs. 2. The material anisotropy effects, which are characteristic to AM processes, were explored through both analytical modeling analysis and experiments on three representative 3D cellular structures (auxetic, BCC and octahedral). The established models were verified via experimentation with samples fabricated by electron beam PBF (EB-PBF) process using Ti6Al4V as material, using the material anisotropy information established experimentally using single struts with different build orientations (0°, 15°, 30°, 45°, 60°, 75° and 90°). The predicted mechanical properties of the Ti6Al4V cellular structures showed good agreement with experimental results. It was shown that both the strength and elastic modulus anisotropy of the materials affect the strength of the cellular structures, which must be determined based on the topology design. In addition, the material anisotropy-topology effects on cellular structures of varying cellular pattern sizes were also investigated in order to quantify the pattern size effects. It was also found that the pattern size effects and the material anisotropy effects can be decoupled during the design of the mechanical properties of these cellular structures. 3. The local material property fluctuation caused by the material imperfection is another important factor to consider for adequate design of AM cellular structures. The local material and feature imperfections affect the overall structural properties of cellular structures and are typically unavoidable with the current AM process technologies. Three representative 2D cellular designs including auxetic, diamond and triangular structures were modeled and analyzed based on the established model, which allows for the implementation of heterogeneous material imperfection at full-scale cellular structure level. The material property imperfection was represented by 3 levels of variabilities (2%, 5% and 10%) for both elastic modulus and strength, defined at local cellular element level. Experimental verification using Ti6Al4V cellular structures fabricated via laser PBF (L-PBF) process demonstrated the potential of the established model in providing accurate predictions to the mechanical property variability of the cellular structures. In addition, the results also revealed new insights into the topology-material imperfection coupling relationships for the cellular structures

Automatic identification of the number of clusters in hierarchical clustering

Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrogram and this represents a major challenge in making a fully automatic system such as the ones required for decision support in Industry 4.0. This research proposes a general criterion to perform the cut of a dendrogram automatically, by comparing six original criteria based on the Calinski-Harabasz index. The performance of each criterion on 95 real-life dendrograms of different topologies is evaluated against the number of classes proposed by the experts and a winner criterion is determined. This research is framed in a bigger project to build an Intelligent Decision Support system to assess the performance of 3D printers based on sensor data in real-time, although the proposed criteria can be used in other real applications of hierarchical clustering.The methodology is applied to a real-life dataset from the 3D printers and the huge reduction in CPU time is also shown by comparing the CPU time before and after this modification of the entire clustering method. It also reduces the dependability on human-expert to provide the number of clusters by inspecting the dendrogram. Further, such a process allows applying hierarchical clustering in an automatic mode in real-life industrial applications and allows the continuous monitoring of real 3D printers in production, and helps in building an Intelligent Decision Support System to detect operational modes, anomalies, and other behavioral patterns.Peer ReviewedPostprint (author's final draft

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm