Material Design & Processing Communications最新文献

Finite element (FE) modeling is a powerful tool for the virtual testing of components, especially for high-value manufacturing like additive manufacturing (AM). AM often involves lattice structures in parts, imparting unique mechanical properties. Numerical models allow for cost-effective virtual testing, but computational limitations hinder comprehensive investigations on lattice structures, and idealized models may not fully represent actual manufactured behavior. This study proposes a simplified numerical model for analyzing lattice structure compression behavior before failure, incorporating X-ray microcomputed tomography (CT) scan data. The model includes real manufacturing defects, such as geometrical inaccuracies, internal porosity, and surface roughness. It closely fits compression test results from samples with varied defects, with a maximum error of 17% for stiffness, 13% for yield stress, and 7% for peak stress. The model offers promise for developing manufacturing defect-incorporated lattice representative volume elements (RVEs) to design AM parts with lattice regions. Replacing complex lattice structures with solid-infilled RVEs in simulations reduces computational costs significantly. This approach allows efficient exploration of lattice AM components’ mechanical behavior, accounting for manufacturing defects and offering insights for design optimization and material selection.

In the experiment, strain gauges and dynamic signal acquisition instruments are used to collect and record data, and the stochastic subspace algorithm is used to extract the first three strain modal parameters of each case. The damage amount identified by the second natural frequency based on the modified Timoshenko beam theory is more in line with the actual situation. The damage depth of case 2 and case 4 is 2 mm, and the identified damage amount is 10% and 9%, respectively. The damage depth of case 3 and case 5 is 4 mm, and the identified damage amount is 16% and 23%, respectively. The damage location information of case 6 is well identified by using the normalized strain modal shape difference index and the enhanced strain modal shape difference index. Taking the strain response signal of case 6 as an example, it is proved that the stochastic subspace strain modal parameter identification algorithm has strong anti-interference ability under the action of 1.5 times, 4 times, and 9 times noise. In addition, the method is verified by theoretical calculation and numerical simulation, and the damage law has a high degree of coincidence with the test. The experimental results show that this method expands the theoretical basis of foam metal damage degree information identification and improves the accuracy of damage location information identification and the anti-interference of parameter identification.

Poly(lactic acid) (PLA) is one of the most popular biodegradable thermoplastics in the market of 3D printing filaments used in the material extrusion (ME) technique. This is because it can be printed easily at low temperatures. However, its inherent brittleness limits its use in many applications. In this work, the toughness of PLA filament was improved by blending with various types of rubbers including natural rubber (NR), acrylic core–shell rubber (CSR), and thermoplastic polyurethane (TPU) in the amount of 15% by weight. PLA/TPU filament was found to have the smoothest surface with the best shape and dimension stability, while PLA/NR filament rendered the highest tensile toughness. In term of the effect of printing temperature, the highest printing temperature in this study (210°C) provided the highest smoothness with the best shape stability and dimension accuracy. Interestingly, the tensile toughness and elongation at break of 3D printed specimens were found to be higher than those of compression-molded specimens for all filament types. This could be explained by the ability of the 3D printing technique to produce specimens that aligned in the printing direction in a fiber-like pattern.

The friction stir welding (FSW) tool is a critical component to the success of the welding process. The aim of the paper is to investigate the effect of tool temperature on the microstructure and mechanical properties of the aluminium alloy during the friction stir welding process. The welding experiment was conducted at a tool rotational speed of 550 rpm, and tool temperature was measured with the increment of a 60 mm distance. Three different tool temperatures were obtained, and samples were characterised by scanning electron microscopy (SEM). The ASTM E384 standard was followed when conducting the Vickers hardness test, and material wear behaviour was tested using the ASTM G99 tribology testing standard. The results show that the tool temperature increases with distance during the FSW process (40.5, 46, and 54°C). A high tool temperature produces the weld butt with high mechanical properties (87.5 HV). The wear rate is low at a high tool temperature (1.169E − 006 mm³/N/m).

Gelatin crosslinking using conventional methods is usually associated with some toxic side effects. In this research, therefore, the vacuum heating method at 10 Pascal and 140°C under different times of 8, 16, and 32 h was used to cross-link strontium-loaded gelatin microparticles with varying degrees obtained by the oil/water mixing method on titanium scaffolds by the dip-coating method to avoid toxicity and also to control the strontium release rate to the surrounding tissue. The possible phases formed on the surface of the porous titanium scaffolds, the gelatin microparticle distribution, gelatin strontium loading, and strontium release were characterized using thin film X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), and inductively coupled plasma-mass spectrometer (ICP-MS) machines, respectively. The results indicated that at 600°C, the rutile phase was formed on the surface of the heat-treated titanium scaffolds. Furthermore, strontium was successfully loaded in the spherical gelatin microparticles, and the strontium-loaded gelatin microparticles were distributed uniformly on the surface of the titanium scaffolds, while the rate of the in vitro strontium release decreased by increasing the time of the gelatin microparticle vacuum-heat crosslinking, whereas at the burst release step, the in vitro strontium release rates were around 5, 4.4, and 2.5 ppm/h, for the 8, 16, and 32 h vacuum-heat cross-linked gelatin microparticles, respectively.

Refractory alloys often possess superior thermomechanical properties compared to conventional materials, such as steels, Ni-based superalloys, and Ti alloys, especially in high-temperature environments. While these materials promise to revolutionize numerous industries, significant hurdles remain for insertion into applications due to an incomplete understanding of structure-property relationships and conventional processing challenges. We explore laser-based additive manufacturing (AM) to construct refractory alloys consisting of combinations of Mo, Nb, Ta, and Ti with systematically increasing compositional complexity. Microstructure, composition, and hardness of the AM-processed alloys were characterized. Results are discussed in the context of pairing additive manufacturing with refractory metals to enable next-generation alloys.

Mild steel is a common material used extensively in the manufacturing industry. This manuscript investigates the effect of cooling processes on the hardness, toughness, coefficient of friction, and wear rate of mild steel heat treated at different temperatures. The material was heat treated in a furnace at two different temperatures (500 and 900°C) and cooled by water, oil, and air. Microhardness and impact tests were conducted using ASTM E384 and ASTM E23-12C. For dry conditions, the tribology ASTM G99 test standard was used to determine the coefficient of friction and wear rate per sample. The results show that mild steel heat treated at 900°C and cooled with water increased the material’s hardness by 24% and toughness by 23.3% as compared to oil- and air-cooling media. The same heating temperature and water-cooling media produce the material with a low wear rate (3.223E-008).

Mechanical properties of polymer biocomposites are influenced by the interaction between the matrix and the filler surface. In this work, composites based on poly(butylene-adipate-terephthalate) (PBAT) filled with micrometric particles of zein-TiO₂ complex (ZTC) were realized via solvent casting technique at different concentrations, equal to 0, 5, 10, and 20 wt%. After pelletization, the resulting materials were injection molded into standard specimens, employed for the uniaxial tensile test (UTT) characterization. From the stress-strain curves, Young’s modulus (E), yield stress (σ_y), stress at break (σ_B), elongation at break (ε_B), and toughness (T) were collected. The addition of the ZTC proved to show a reinforcing effect on the polymeric matrix, with an increase in both E and σ_y. Modelling of the mechanical properties was performed by applying Kerner’s and Pukánszky’s equations. Kerner’s model, applied on experimental E values, returned a very good correspondence between collected and theoretical values. From the application of Pukánszky’s model to σ_y, the obtained B value showed a good interfacial interaction between the matrix and the filler. Due to the enhanced stiffness of the composites, a reduction in the true stress at break (σ_T,B) was observed. The modified Pukánszky’s model gave a B value lower than the one obtained for the yield, but still in the range of acceptable values for microcomposites.

In this study, the static deflection of non-prismatic axial function graded tapered beam (A-FGB) under distribution load has been analyzed using ANSYS workbench (17.2). According to a power-law model, the elastic modulus of the beam varies continuously in the axial direction of the beam. Also, the beam’s geometry, i.e., width, thickness, or both width and thickness of the beam, varies linearly in the axial direction with different values of non-uniformity parameter (1, 0.5,0, −0.5, and −0.75). The effects of martial distribution, i.e., power-law index, and non-uniformity parameter on the static deflection for A-FGB with different boundary conditions, in such free-clamped, clamped-free, and simply-supported, are studied. This research deals with functionally graded materials FGMs in more than one aspect in terms of using different boundary conditions; in addition, it studies the response of the non-prismatic beam non-uniformity parameter (α); therefore, this research studies comprehensively the deflection of the beam. The results show that the increase in power-law index causes decreasing in dimensionless deflection and its rate of change depends on the supporting types of the beam and non-uniformity parameters. The variation in both width and thickness for a free-clamped axial function–graded beam gives a significant decrease in dimensionless deflection at decreasing in non-uniformity parameter, whereas the variation in thickness for clamped-free axial function graded beam gives a significant decrease in dimensionless deflection at decreasing of non-uniformity parameter.

Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.