Optimization of Tube Hydroforming Process Using Simulated Annealing Algorithm

AbstractIn this paper, forming parameters of tube hydroforming (THF) process are investigated and optimized using Simulated Annealing optimization algorithm linked with a finite element commercial code. The goal of this research is to obtain the maximum formability of two dimensional (2D) axisymmetric tubes under a failure criteria based on material's forming limit diagram (FLD). The initial approximated pressure loading path is determined by proved theoretical equations. Then the Simulated Annealing algorithm written in Matlab software is combined with a nonlinear structural finite element code ANSYS/ LS-DYNA in order to optimize internal hydraulic pressure. The results are compared by experimental observations and a good agreement was observed between them

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

Emerging multi-material 3D printing techniques have paved the way for the rational design of metamaterials with not only complex geometries but also arbitrary distributions of multiple materials within those geometries. Varying the spatial distribution of multiple materials gives rise to many interesting and potentially unique combinations of anisotropic elastic properties. While the availability of a design approach to cover a large portion of all possible combinations of elastic properties is interesting in itself, it is even more important to find the extremely rare designs that lead to highly unusual combinations of material properties (e.g., double-auxeticity and high elastic moduli). Here, we used a random distribution of a hard phase and a soft phase within a regular lattice to study the resulting anisotropic mechanical properties of the network in general and the abovementioned rare designs in particular. The primary challenge to take up concerns the huge number of design parameters and the extreme rarity of such designs. We, therefore, used computational models and deep learning algorithms to create a mapping from the space of design parameters to the space of mechanical properties, thereby (i) reducing the computational time required for evaluating each designand (ii) making the process of evaluating the different designs highly parallelizable. Furthermore, we selected ten designs to be fabricated using polyjet multi-material 3D printing techniques, mechanically tested them, and characterized their behavior using digital image correlation (DIC, 3 designs) to validate the accuracy of our computational models. The results of our simulations show that deep learning-based algorithms can accurately predict the mechanical properties of the different designs, which match the various deformation mechanisms observed in the experiments.Comment: 28 pages, 4 figure

Crumpling-based soft metamaterials: The effects of sheet pore size and porosity

Crumpled-based materials are relatively easy to fabricate and show robust mechanical properties for practical applications, including meta-biomaterials design aimed for improved tissue regeneration. For such requests, however, the structure needs to be porous. We introduce a crumpled holey thin sheet as a robust bio-metamaterial and measure the mechanical response of a crumpled holey thin Mylar sheet as a function of the hole size and hole area fraction. We also study the formation of patterns of crease lines and ridges. The area fraction largely dominated the crumpling mechanism. We also show, the crumpling exponents slightly increases with increasing the hole area fraction and the total perimeter of the holes. Finally, hole edges were found to limit and guide the propagation of crease lines and ridges

Determinants of bone damage: An ex-vivo study on porcine vertebrae

Bone\u2019s resistance to fracture depends on several factors, such as bone mass, microarchitecture, and tissue material properties. The clinical assessment of bone strength is generally performed by Dual-X Ray Photon Absorptiometry (DXA), measuring bone mineral density (BMD) and trabecular bone score (TBS). Although it is considered the major predictor of bone strength, BMD only accounts for about 70% of fragility fractures, while the remaining 30% could be described by bone \u201cquality\u201d impairment parameters, mainly related to tissue microarchitecture. The assessment of bone microarchitecture generally requires more invasive techniques, which are not applicable in routine clinical practice, or X-Ray based imaging techniques, requiring a longer post-processing. Another important aspect is the presence of local damage in the bony tissue that may also affect the prediction of bone strength and fracture risk. To provide a more comprehensive analysis of bone quality and quantity, and to assess the effect of damage, here we adopt a framework that includes clinical, morphological, and mechanical analyses, carried out by means of DXA, \u3bcCT and mechanical compressive testing, respectively. This study has been carried out on trabecular bones, taken from porcine trabecular vertebrae, for the similarity with human lumbar spine. This study confirms that no single method can provide a complete characterization of bone tissue, and the combination of complementary characterization techniques is required for an accurate and exhaustive description of bone status. BMD and TBS have shown to be complementary parameters to assess bone strength, the former assessing the bone quantity and resistance to damage, and the latter the bone quality and the presence of damage accumulation without being able to predict the risk of fracture

Shape-matching soft mechanical metamaterials

Architectured materials with rationally designed geometries could be used to create mechanical metamaterials with unprecedented or rare properties and functionalities. Here, we introduce "shape-matching" metamaterials where the geometry of cellular structures comprising auxetic and conventional unit cells is designed so as to achieve a pre-defined shape upon deformation. We used computational models to forward-map the space of planar shapes to the space of geometrical designs. The validity of the underlying computational models was first demonstrated by comparing their predictions with experimental observations on specimens fabricated with indirect additive manufacturing. The forward-maps were then used to devise the geometry of cellular structures that approximate the arbitrary shapes described by random Fourier's series. Finally, we show that the presented metamaterials could match the contours of three real objects including a scapula model, a pumpkin, and a Delft Blue pottery piece. Shape-matching materials have potential applications in soft robotics and wearable (medical) devices

Rational design of soft mechanical metamaterials: Independent tailoring of elastic properties with randomness

The elastic properties of mechanical metamaterials are direct functions of their topological designs. Rational design approaches based on computational models could, therefore, be used to devise topological designs that result in the desired properties. It is of particular importance to independently tailor the elastic modulus and Poisson's ratio of metamaterials. Here, we present patterned randomness as a strategy for independent tailoring of both properties. Soft mechanical metamaterials incorporating various types of patterned randomness were fabricated using an indirect additive manufacturing technique and mechanically tested. Computational models were also developed to predict the topology-property relationship in a wide range of proposed topologies. The results of this study show that patterned randomness allows for independent tailoring of the elastic properties and covering a broad area of the elastic modulus-Poisson's ratio plane. The uniform and homogenous topologies constitute the boundaries of the covered area, while topological designs with patterned randomness fill the enclosed area

Rational positioning of 3D printed micro-bricks to realize high-fidelity, multi-functional soft-hard interfaces

peer reviewedLiving organisms have developed design principles, such as functional gradients (FGs), to interface hard materials with soft ones (e.g., bone and tendon). Mimicking such design principles can address the challenges faced when developing engineered constructs with soft-hard interfaces. To date, implementing these FG design principles has been primarily performed by varying the ratio of the hard phase to that of the soft phase. Such design approaches, however, lead to inaccurate mechanical properties within the transition zone. That is due to the highly nonlinear relationship between the material distribution at the microscale and the macroscale mechanical properties. Here, we 3D print micro-bricks from either a soft or a hard phase and study the nonlinear relationship between their arrangements within the transition zone and the resulting macroscale properties. We carry out experiments at the micro- and macroscales as well as finite element simulations at both scales. Based on the obtained results, we develop a co-continuous power-law model relating the arrangement of the micro-bricks to the local mechanical properties of the micro-brick composites. We then use this model to rationally design FGs at the individual micro-brick level and create two types of biomimetic soft-hard constructs, including a specimen modeling bone-ligament junctions in the knee and another modeling the nucleus pulposus-annulus fibrosus interface in intervertebral discs. We show that the implemented FGs drastically enhance the stiffness, strength, and toughness of both types of specimens as compared to non-graded designs. Furthermore, we hypothesize that our soft-hard FGs regulate the behavior of murine preosteoblasts and primary human bone marrow-derived mesenchymal stromal cells (hBMSCc). We culture those cells to confirm the effects of soft-hard interfaces on cell morphology as well as on regulating the expression of focal adhesion kinase, subcellular localization, and YAP nuclear translocation of hBMSCs. Taken together, our results pave the way for the rational design of soft-hard interfaces at the micro-brick level and (biomedical) applications of such designs

Emerging topics in nanophononics and elastic, acoustic, and mechanical metamaterials:An overview

This broad review summarizes recent advances and “hot” research topics in nanophononics and elastic, acoustic, and mechanical metamaterials based on results presented by the authors at the EUROMECH 610 Colloquium held on April 25–27, 2022 in Benicássim, Spain. The key goal of the colloquium was to highlight important developments in these areas, particularly new results that emerged during the last two years. This work thus presents a “snapshot” of the state-of-the-art of different nanophononics- and metamaterial-related topics rather than a historical view on these subjects, in contrast to a conventional review article. The introduction of basic definitions for each topic is followed by an outline of design strategies for the media under consideration, recently developed analysis and implementation techniques, and discussions of current challenges and promising applications. This review, while not comprehensive, will be helpful especially for early-career researchers, among others, as it offers a broad view of the current state-of-the-art and highlights some unique and flourishing research in the mentioned fields, providing insight into multiple exciting research directions

Formation Of Universal Educational Movements In Primary School Pupils

The article focuses on improving the primary school students' learning effectiveness. It was highlighted the importance of forming universal learning movements in primary school students in order to increase the education effectiveness

Analyzing the mechano-bactericidal effect of nano-patterned surfaces on different bacteria species

Mechano-bactericidal effects exhibited by specific nano-patterns have brought in the prospect of developing sustainable antibacterial materials. Contrary to the standard practices of administrating anti-bacterial agents or chemical surface functionalization, nano-patterns manage to inactivate a wide variety of bacteria species with no risk of toxicity, antibiotic resistance or replenishment. Herein, the experimental data on the bactericidal effect of nano-patterns were collected to develop in-silico models for identifying the impact of individual geometrical features. An artificial neural network was developed considering the three prevalent species of Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The roles of individual geometrical features were analyzed and comprehensive parametric and sensitivity analyses were performed to determine the most favorable range for each parameter against different species. Geometrical features that would demonstrate bactericidal effects simultaneously against all the three studied species were identified. The efficient geometrical parameters, obtained from the artificial neural network analysis, were then used to develop a series of finite element models to simulate the physical interaction between the bacteria and the nano-patterns that result in inactivation. The obtained results can pave the way for unlocking the role of geometrical features towards optimized development of artificial materials with sustainable intrinsic antibacterial characteristics