Complex Structural Components Research Articles

On the Development of an Open-Source Toolbox for the Structural Analysis of Composite Materials

Commonly used in structural applications since the ’90s, composite materials represent a valid alternative to metallic solutions. An intense research activity accompanied the spread of this technology to the point that, nowadays, a large variety of modeling techniques and tools are at the designers’ disposal. Although the literature on this topic is vast, detailed, and thorough, the need for new, faster, and more efficient design tools and methodologies remains. In this context, this work covers the development of an Open-Source computational laminate analysis toolbox, its structure, and its validation. The objective of the research was the creation of a simple and accessible mean for the calculation of laminates’ stresses in complex structural components with a reduced computational cost. To this end, the authors implemented the basics of micro, meso, and micromechanics in a Python-based toolbox and then performed a validation campaign against the results of an FE analysis. The work resulted in an input-generation/output-analysis instrument to be used by designers alongside the FEA.

An integrated cell-to-pack design based on an origami sandwich structure to enhance cooling and mechanical performances of battery packs in electric vehicles

The rapid growth in the energy density of batteries in electric vehicles has led to the need for complex structural components and cooling systems to ensure the safety of the battery pack. However, these inactive materials occupy a significant portion, accounting for at least 35% of the pack's volume. To enhance the volume utilization rate of the battery pack, this paper proposes a novel origami structure with its optimized geometry that enhances thermal and mechanical performances as well as crashworthiness performance. The simulation results of temperature evaluation show that the proposed origami channels for a battery pack can reduce the maximum temperature by more than 2.6 °C at a 5C discharging rate and by over 20 °C under internal short-circuit conditions compared with a flat cooling channel. The experimental results of a quasi-static punch compression test demonstrate the enhanced mechanical properties of the origami channels with a significant increase in peak crushing force and impact energy absorption compared with flat cooling channels.

Design of fast braiding process based on reconstructing mesh with complex and irregular mandrels

Design of fast braiding process based on reconstructing mesh with complex and irregular mandrels

Mechanical Characterisation of Bond Formation during Overprinting of PEEK Laminates.

The latest generation of high-temperature 3D printers enables the production of complex structural components from aerospace-grade thermoplastics such as PEEK (polyether ether ketone). However, adding long or continuous fibres is currently limited, and thermal stresses introduced during the process restrict the maximum part dimensions. Combining 3D-printed components with continuous fibre-reinforced components into one hybrid structure has the potential to overcome such limitations. This work aims to determine whether in situ bonding between PEEK laminates and PEEK 3D printing during overprinting is feasible and which process parameters are significantly responsible for the bonding quality. To this end, the bonding is analysed experimentally in two steps. Firstly, the influence of the process parameters on the thermal history and the strength of the bond is investigated. In the second step, a detailed investigation of the most critical parameters is carried out. The investigation showed the feasibility of overprinting with bonding strengths of up to 15 MPa. It was shown that the bonding strength depends primarily on the temperature in the interface. Additionally, the critical parameters to control the process were identified. The process influences that were displayed form the basis for future hybrid component and process designs.

Thermal analytical modeling of machine tool structural components via dual-layer equivalence

Precision machine tools are sensitive to thermal effects. Thermal deformation of machine tool structural components can lead to changes in the relative pose between the spindle tool and the workpiece, causing thermally induced machining errors. Gaining insight into the mechanistic correlation between critical thermal condition parameters of heat sources and thermal errors in machine tools is essential to elevate the design of machine tool thermal attributes. To address this issue, this paper proposes a dual-layer equivalent analytical modeling method for characterizing the thermal properties of structural components. The construction of the temperature field analytical model for the dual-layer equivalent structure is achieved by setting and handling different interlayer heat flows and temperature boundary conditions. The dual-layer equivalent approach simplifies complex structural components into top and bottom layers, providing a more flexible and accurate capability for characterizing the thermal properties of structural components. This modeling method enables rapid prediction and evaluation of machine tool thermal characteristics. Furthermore, based on this analytical approach, the calculation method of sensitivity coefficient of thermal power to thermal error is proposed to elucidate the mapping relationship between different thermal condition parameters and thermal errors. This method facilitates the fast and accurate prediction of six thermally induced errors under various thermal conditions. Experimental results demonstrate that the key point temperature deviation predicted using the dual-layer analytical modeling method is less than 0.8 °C, and the thermally induced axial linear error deviation is less than 1.8 μm. The dual-layer analytical modeling approach introduced in this study presents a versatile technique applicable to enhancing the design and control of machine tool thermal characteristics, holding significant potential for advancing the stability of machine tools thermal accuracy.

Spray Trajectory Planning for Complex Structural Components in Robotized Cold Spray Additive Manufacturing

Spray Trajectory Planning for Complex Structural Components in Robotized Cold Spray Additive Manufacturing

Phase-based motion estimation and SVR smooth for target-free 3D deformation measurement using stereophotogrammetry

Non-contact measurement methods based on computer vision have shown great potential for measuring structural surface displacement and strain due to their low cost and high efficiency. However, the traditional computer vision technology based on digital image correlation (DIC) is limited by its dependence on natural texture features of the surface and its susceptibility to environmental conditions, making it challenging to obtain effective and robust measurements of surface deformation. Additionally, these methods typically only focus on the structural response in the image plane, which makes it difficult to perform a comprehensive health assessment of complex structural components with asymmetry. Therefore, a time-domain visual measurement technique based on image phase information is proposed for measuring three-dimensional (3D) deformation of structures using stereophotogrammetry. The method involves extracting image phase information using 2D Gabor filters and applying it to dense optical flow estimation based on polynomial approximation and semi-global block matching (SGBM) for obtaining robust 3D displacement measurements. The support vector regression (SVR) is then used to perform hyperplane fitting of the original displacements for improving the smoothness of the 3D displacements. Finally, an intuitive strain conversion method is introduced to convert the 3D displacements into 3D strains. The performance of the proposed technique has been validated through numerical simulation experiments based on a physics-based graphics model (PBGM) in a synthetic environment. The robustness of the proposed method has been further proved through free attenuation vibration experiments on a steel ruler. The initial evaluation of the expected performance of the measurement plan has been enabled by performing a 1:1 reduction of the field measurement process in a synthetic environment. The results indicate that the proposed method is a promising method for accurately and reliably measuring structural deformation in both synthetic and real-world environments, especially for complex structural components with asymmetry.

Controllable reduction of absorbance and two-step reaction for 3D-printed SiC ceramics with micron-level periodic structure

Digital light processing (DLP) is increasingly widely used to manufacture high-precision complex structural ceramic components quickly and efficiently. However, due to the high absorbance of silicon carbide (SiC) micron fine powder and the high refractive index difference between the powder and the photosensitive resin, it is extremely challenging to DLP print SiC with high precision complex structure. Here, the surface of SiC powder was coated with SiO2 nano-layer of different thickness by controllable oxidation coating method, thus realizing the photocurable printing of SiC@SiO2 green bodies with structural precision up to 80 μm. Through the two-step reaction method of carbothermal reduction combined with gas-phase siliconizing (GSI), not only the SiO2 coated layer was effectively removed, but also the reaction-bonded silicon carbide (RB-SiC) ceramics with 80–300 μm periodic structure was successfully realized. Ultimately, the comprehensive performance of the RB-SiC samples is effectively improved by chemical vapor infiltration of silicon carbide (CVI-SiC). It is noteworthy that the maximum elastic modulus and hardness of the ultima SiC specimens (RB-SiC@CVI-SiC) are as high as 394.599 GPa and 50.241 GPa, respectively. Shockingly, the nanomechanical properties of postgenetic RB-SiC bound to initial α-SiC on the cross-section of ultima specimens were much higher than those of CVI-SiC on the surface. This study fills the gap that liquid-phase siliconizing (LSI) or pyrolysis process could not achieve high-performance SiC ceramics with micron-level periodic structure.

Preparation of low residual silicon content Si-SiC ceramics by binder jetting additive manufacturing and liquid silicon infiltration

Complex silicon carbide (SiC) ceramic components are difficult to fabricate due to their strong covalent bonds. Binder jetting (BJ) additive manufacturing has the outstanding advantages of high forming efficiency and no thermal deformation, especially suitable for printing complex structure SiC components. This study tried to obtain low silicon content silicon carbide ceramics by binder jetting followed by phenolic resin impregnation and pyrolysis (PRIP) and liquid silicon infiltration (LSI). BJ was used for the SiC green parts fabrication, and the highest compressive strength (7.7 ± 0.3 MPa) and lowest dimensional deviations (1.2–1.6 mm) were obtained with the printing layer thickness of 0.15 mm. Subsequently, PRIP treatments were introduced to increase the carbon content for the following LSI process. As the number of PRIP cycles increased, the carbon density of SiC/C preform increased and the porosity decreased. After the LSI treatment, the final Si-SiC composites processed with 2 PIRP cycles reached the highest flexural strength (257 ± 14.26 MPa) and the best wear resistance. This was attributed to the low residual silicon content (10.2 vol%) and almost no residual carbon. Furthermore, several complex structural components were fabricated using these methods. The preparation of complex components verifies the feasibility of BJ and LSI for manufacturing high-strength and high-precision SiC ceramics. Besides, this work hopes to provide technical guidance for the preparation of complex SiC composites in the future.

A systematic review on high cycle and very high cycle fatigue behavior of laser powder bed fused (L-PBF) Al-Si alloys

The recent attention towards additive manufacturing (AM) of metallic alloys, particularly Al-Si alloys, has been significant due to its important role in the design and development of complex structural components in aerospace, defense, and automotive industries. The use of AM technology, specifically Laser powder bed fusion (L-PBF), enables the creation of lightweight structures with flexible geometries that were previously not achievable through conventional manufacturing methods. Al-Si components fabricated through L-PBF are often subjected to long periods of loading in high cycle fatigue (HCF) and very high cycle fatigue (VHCF) conditions. Therefore, it would be extremely important to investigate fatigue characteristics of these components under both HCF and VHCF regimes. However, it is important to recognize that the fatigue performance of L-PBF Al-Si alloys in HCF and VHCF regimes is not fully understood due to the complex nature of fatigue responses in these regimes. Various factors such as microstructures, defects, porosity, and processing parameters, can potentially influence the fatigue behavior in these regimes, further complicating the analysis. Therefore, optimizing process parameters and post-treatments is essential to achieve the optimal fatigue performance for load-bearing components in both HCF and VHCF conditions. In the past few years, several studies have been conducted to investigate the effect of critical parameters on the fatigue performance of L-PBF Al-Si parts in HCF and VHCF regimes. This article aims to provide a comprehensive and in-depth analysis of recent research findings regarding important factors such as residual stress, surface roughness, build parameters (e.g. build orientation), microstructural characteristics, post-processing methods, manufacturing flaws, specimen shapes, load ratios. These factors can have a significant impact on fatigue properties of L-PBF Al-Si alloys, particularly in the HCF and VHCF ranges. In order to realize the full potential of the L-PBF Al-Si alloys in the development of advanced structural components, this study primarily focuses on identifying key areas that require attention.

Role of intra-ply hybridization and nano reinforcement on crack growth of epoxy

Purpose Epoxy resins are widely used in a variety of engineering applications, including composite wind turbine blades used in the renewable energy industry, highly complex structural components for aircraft, paints, coatings, industrial tooling, biomedical systems, adhesives, electronics and automotive. Epoxies' low fracture toughness is one of the key obstacles preventing its adoption in a wider range of applications. To address epoxy's low fracture toughness, this paper aims to examine the roles of intra-ply hybridization and nano reinforcing. Design/methodology/approach This paper investigates the role of intra-ply hybridization of glass-carbon woven fibers and adding 0.8 wt.% of multiwall carbon nanotube (MWCNT) nano reinforcement to overcome the low fracture toughness of epoxy. A bending test is used to calculate the composites elastic parameters, and a notched sample three-point bending test is used to show crack behavior in addition to using materials characterization methods to reveal the effect of the MWCNT on structure, bonding, glass transition temperature (Tg) and dispersion of MWCNT in the matrix. Furthermore, this paper suggests using the finite element method to overcome the difficulty in calculating the crack extension. Findings Intra-ply hybridization and MWCNT reinforcement decrease the crack extension of epoxy with time. The inclusion of high-strength carbon fiber increased the fracture toughness of glass composite. Furthermore, the existence of MWCNT in the surrounding area of the notch in epoxy composites hinders crack propagation and provides stiffness at the interface by bridging the crack and eventually enhancing its fracture toughness. Originality/value Studying the role of intra-ply hybridization of glass-carbon woven fibers and adding 0.8 wt.% of MWCNT nano reinforcement to overcome the low fracture toughness of epoxy. Additionally, this research recommends using the finite element method to overcome the challenge of computing the crack extension.

Achieving isotropic microstructure in an additively manufactured Ti-6Al-4V alloy enabled by dual laser processing

With the development of science and technology and the popularization of intelligent manufacturing, laser additive manufacturing of titanium alloys is being attractive in industrial adoption due to its advantages of near-net shape and cost-saving features, and especially laser melting deposition is an effective additive manufacturing technique for large-scale and lightweight components such as TC4 alloy. However, inside the as-prepared alloy, large scale columnar crystals are formed along the forming direction, which will lead to anisotropic macroscopic properties of the alloy parts, further affecting their service life. In view of this, we report an innovative strategy of dual laser processing integrating laser melting deposition and laser shock peening, which can be used to achieve isotropic microstructure along three building directions in Ti-6Al-4V alloy. Interestingly, the isotropic microstructure is basket-weave structure of ultrafine α and nanometer β lamellar colonies together with abundant nanometer β fragments/particles distributed dispersedly inside ultrafine α matrix. Especially, the as-fabricated alloy exhibits approximately isotropy of mechanical properties along three building directions, and circumvents the strength–ductility trade-off of Ti-6Al-4V alloy with different microstructures reported so far. This study validates the feasibility of manufacturing high performance complex structural components by coupling technology of laser melting deposition and laser shock peening, for grain refinement and isotropic microstructure of additive manufactured titanium alloys in a low-cost and effective method. Impact statementThe proposed dual laser processing technology integrating additive manufacturing of laser melting deposition and synchronous laser shock peening, can provides insight into fabricating isotropic metal components with higher performance through additive manufacturing of one-step forming.

Numerical Simulations and Experimental Verification of T-Structure Welding Deformation Using the Step-by-Step Loading Inherent Strain Method

The existing inherent strain method is improved in this paper to address the shortcomings of the existing inherent strain method in the process of loading inherent strain. Unlike the traditional inherent strain method, which uses one-step loading inherent strain for each weld seam for one-time elastic calculation, the improved inherent strain method uses step-by-step loading inherent strain for each weld seam for multiple elastic calculations to predict welding deformation. The step-by-step loading inherent strain method (SBS-ISM) is more in line with the actual welding deformation generation process. Firstly, the local finite element model of the T-joint was used to analyze the welding deformation and extract the inherent strain by using the thermal elastic–plastic finite element method (TEP-FEM). Subsequently, the one-step loading inherent strain method (OS-ISM) and the step-by-step loading inherent strain method (SBS-ISM) were used to predict the welding deformation for the same local finite element model, respectively. The comparative results showed that the trend and magnitude of welding deformation calculated using SBS-ISM was much closer to those calculated using TEP-FEM. The OS-ISM and SBS-ISM were used to predict the welding deformation of the backward centrifugal fan impeller under different welding sequences, respectively. By comparing the welding deformation results calculated using the two inherent strain methods with the experimental results, it was demonstrated that the step-by-step loading inherent strain method (SBS-ISM) provides more accurate and reliable predictions of welding deformation for large and complex thin-walled T-shaped structural components compared to the one-step loading inherent strain method (OS-ISM).

Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing

The degradation of infrastructures such as bridges, highways, buildings, and dams has been accelerated due to environmental and loading consequences. The most popular method for inspecting existing concrete structures has been visual inspection. Inspectors assess defects visually based on their engineering expertise, competence, and experience. This method, however, is subjective, tiresome, inefficient, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow us to figure out the cause of the propagation and extent of the damage, and rehabilitation can be suggested based on them. This research proposes an algorithm based on a pre-trained convolutional neural network (CNN) and image processing (IP) to obtain the crack angle, width, endpoint length, and actual path length in a concrete structure. The results show low relative errors of 2.19%, 14.88%, and 1.11%, respectively for the crack angle, width, and endpoint length from the CNN and IP methods developed in this research. The actual path length is found to be 14.69% greater than the crack endpoint length. When calculating the crack length, it is crucial to consider its irregular shape and the likelihood that its actual path length will be greater than the direct distance between the endpoints. This study suggests measurement methods that precisely consider the crack shape to estimate its actual path length.

Additive Manufacturing of Advanced Ceramics Using Preceramic Polymers

Ceramic materials are used in various industrial applications, as they possess exceptional physical, chemical, thermal, mechanical, electrical, magnetic, and optical properties. Ceramic structural components, especially those with highly complex structures and shapes, are difficult to fabricate with conventional methods, such as sintering and hot isostatic pressing (HIP). The use of preceramic polymers has many advantages, such as excellent processibility, easy shape change, and tailorable composition for fabricating high-performance ceramic components. Additive manufacturing (AM) is an evolving manufacturing technique that can be used to construct complex and intricate structural components. Integrating polymer-derived ceramics and AM techniques has drawn significant attention, as it overcomes the limitations and challenges of conventional fabrication approaches. This review discusses the current research that used AM technologies to fabricate ceramic articles from preceramic feedstock materials, and it demonstrates that AM processes are effective and versatile approaches for fabricating ceramic components. The future of producing ceramics using preceramic feedstock materials for AM processes is also discussed at the end.

Fused deposition modeling of carbon‐reinforced polymer matrix composites: A comprehensive review

AbstractCarbon‐reinforced polymer matrix composites (PMCs) have been thoroughly applied in different fields because of their benefits, such as low specific gravity, corrosion resistance, good electrical conductivity, and robust mechanical properties. Especially, with the emergence of fused deposition modeling (FDM) technology has further promoted the application of such materials in complex structural components. Recently, FDM printing carbon‐reinforced PMCs have become a hot topic in composites research, and many promising results have been achieved around related research. In order to help readers have a comprehensive and systematic understanding of the latest research progress of FDM printing carbon‐reinforced PMCs in terms of material modification, processing, material properties, and application levels, this paper reviews the properties and processes of FDM printed carbon‐reinforced PMCs and their potential applications in aerospace, flexible sensing, electrochemistry, and biomedical fields. The effects of commonly used carbon reinforcing materials on the performance of FDM printed PMCs were contrasted and analyzed. Moreover, the process optimization of printing carbon‐reinforced PMCs was introduced and highlighted. Finally, the current challenges and future research directions of FDM printing carbon‐reinforced PMCs were analyzed and prospected.

Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions

The challenge underlying superconducting quantum computing is to remove materials bottleneck for highly coherent quantum devices. The nonuniformity and complex structural components in the underlying quantum circuits often lead to local electric field concentration, charge scattering, dissipation and ultimately decoherence. Here we visualize interface dipole heterogeneous distribution of individual Al/AlOx/Al junctions employed in transmon qubits by broadband terahertz scanning near-field microscopy that enables the non-destructive and contactless identification of defective boundaries in nano-junctions at an extremely precise nanoscale level. Our THz nano-imaging tool reveals an asymmetry across the junction in electromagnetic wave-junction coupling response that manifests as hot (high intensity) vs cold (low intensity) spots in the spatial electrical field structures and correlates with defected boundaries from the multi-angle deposition processes in Josephson junction fabrication inside qubit devices. The demonstrated local electromagnetic scattering method offers high sensitivity, allowing for reliable device defect detection in the pursuit of improved quantum circuit fabrication for ultimately optimizing coherence times.

Development of a Deep Learning Platform for Sheet Stamping Geometry Optimisation under Manufacturing Constraints

Sheet stamping is a widely adopted manufacturing technique for producing complex structural components with high stiffness-to-weight ratios. However, designing such components is a non-trivial task that requires careful consideration of manufacturing constraints to avoid introducing defects in the final product. To address this challenge, this research introduces a novel deep-learning-based platform that optimises 3D component designs by considering manufacturing capabilities. This platform was realised by developing a methodology to combine two neural networks that handle non-parametric geometry representations, namely a geometry generator based on Signed Distance Fields (SDFs) and an image-based manufacturability surrogate model. This combination enables the optimisation of complex geometries that can be represented using various parameterisation schemes. The optimisation approach implemented in the platform utilises gradient-based techniques to update the inputs to the geometry generator based on manufacturability information from the surrogate model. The platform is demonstrated using two geometry classes, Corners and Bulkheads, each having three geometry subclasses, with four diverse case studies conducted to optimise these geometries under post-stamped thinning constraints. The case studies demonstrate how the platform enables free morphing of complex geometries, while also guiding manufacturability-driven geometric changes in a direction that leads to significant improvements in component quality. For instance, one of the cases shows that optimising the complex component geometry can reduce the maximum thinning from 45% to satisfy the thinning constraint of 10%. By utilising the proposed platform, designers can identify optimal component geometries that ensure manufacturing feasibility for sheet stamping, reducing design development time and design costs.

The influence of keyhole dynamic behaviors on the asymmetry characteristics of weld during dissimilar materials laser keyhole welding by experimental and numerical simulation methods

The joining of dissimilar materials has been attracted more and more attention and widely used in the manufacturing of complex structural components. Due to the difference of thermophysical parameters of materials, the formed weld tends to show the asymmetry characteristics including geometric morphology and alloy compositions during the process of dissimilar materials laser welding. Therefore, a 3D multiphase flow model is proposed in this paper to investigate the asymmetry characteristics of weld formed in the dissimilar materials laser keyhole welding. The results obtained from the molten pool behaviors calculation under different welding conditions are compared and the effects of welding conditions on the molten pool behaviors induced by keyhole generation are discussed in detail. The weld geometries and alloy compositions distribution under different welding process conditions are obtained. Simultaneously, the effects of keyhole dynamic behaviors on the geometrical morphology of weld and asymmetry distribution of alloy compositions are analyzed. The good agreement between the calculated and experimental results is found, which approves that the developed model can be used to improve the weld geometric morphology and alloy compositions. Hence, the proposed method is efficient for welding process improvement to obtain the desired welded joints in practical dissimilar materials laser keyhole welding.

Surface characterization of SAE 304 after WED cutting: an experimental investigation and optimization

Stainless steel 304 is an iron-chromium-nickel-based alloy developed for structural applications. This alloy exhibits good mechanical strength along with excellent resistance to various atmospheric conditions. Due to the increasing demand for complex, precise, and high-quality structural components, the wire-electrical-discharge-cutting (WEDC) process is recommended as a powerful technique instead of conventional machine tools. The experimental layout is designed based on L27 Taguchi orthogonal array where pulse-on-time, pulse-off-time, servo voltage, and wire feed are selected as control variables. Post experimentation, roughness profile, topography, morphology, recast surface, and subsurface microhardness of the cut section of SAE 304 alloy were evaluated. Thereafter, the desirability function approach is used to find the optimum cutting conditions. The experimental investigation reveals lower profile roughness, smoother topography, no micro-cracks, least recast layer, and minimum hardness alteration under a trim-cut strategy which is found to be useful for safe, durable, and high–strength complex structural components.