查看更多>>摘要：Although many studies on the initiation of glass cracks under contact loads were carried out, specific problems arising under complex machining conditions remain poorly understood. In order to clarify the influence of particle trajectory curvature change on material grinding mechanism, a theoretical model of trochoid curvature radius was established, and the main factors affecting trochoid curvature were identified (moving circle radius R, spindle speed n and feed speed vw). Different curvature trochoid scratches were obtained by the experiment of trochoid feed scratches on quartz glass. The experimental results show that the scratch damage of the maximum curvature segment is larger than that of the minimum curvature segment. With the increase of trochoid curvature, material damage inside the scratch is more significant than outside. In order to further explain the distribution characteristics of trochoid scratches. Numerical simulation and theoretical analysis were used to verify the relationship between damage difference and curvature on both sides of the trochoid feed scratch groove. The curvature effect of trochoid feed scratch damage is proposed for the first time, which is used to explain stress field migration and damage distribution caused by trochoid curvature characteristics. This study further supplements and clarifies the removal mechanism of hard and brittle materials, which has theoretical guiding significance for quartz glass high-efficiency and controllable precision grinding.
查看更多>>摘要：A high-throughput stamping process technology is proposed for high-speed manufacturing of regular, accurate microdimple structures in large quantity. Due to the alternating nature of AC power and magnetic effect of current, the designed electromagnet is capable of creating sine vibrations at 120 Hz. Acceleration increases instantaneously when a tungsten carbide stamping head is subject to the electromagnet's sine vibrations, resulting in a jerk motion increasing kinetic energy of the stamping head. Desired shapes and depths are realized as the stamped material is subject to high-speed impact from the stamping head. To provide timely grinding with spherical and aspherical stamping heads, an on-process grinding mechanism is designed on the CNC high-frequency stamping system, where on-process calibration is not required for the stamping head and attached residues are removed instantly. It took only 3.4 s to finish an array of 400 highly regular aspherical microdimples with no burring around the dimples; in addition, the form of the arc length is 96% consistent with the design. The arc length of the stamping head overlaps nearly 99% the formed arc length. Metallographic testing shows that the proposed stamping jerking technique produces grain refinement and grain boundary indentation on the surface of stamped microdimples that prevents dislocation and expansion of micro-fractures. Moreover, compressive stress makes the lattice structure of stamped material more solid. The study proves that this high-frequency electromagnetic stamping technology combines high speed, density and consistency with an outstanding transcription-rate.
查看更多>>摘要：Multi-material design in additive manufacturing relies on fast and accurate prediction of the physical response of mesostructured parts with arbitrary material distributions, which is extraordinarily challenging owing to the unknown parameters in constitutive modeling, manufacturing uncertainties, and high computational cost. Here, we employ an interdisciplinary framework to address these challenges by integrating experimentation, mechanical modeling, and statistical learning. Using advanced Bayesian learning and inference to optimally combine the simulation and experimental data, this framework enables (1) parameter calibration, (2) fast and accurate prediction of the physical response, and (3) uncertainty quantification of additively manufactured multi-material components. We demonstrate the framework based on a mechanical design problem involving three-point bending of multi-material beams. By training the Bayesian learning model with simulation and experimental data from selected multi-material designs, the beam deflection with an arbitrary mesostructure is shown to be accurately and rapidly predicted. Correlation of data sampling with the emulator uncertainty and discrepancy is demonstrated; this correlation can be used to identify regions with insufficient data sampling. The posterior distribution of each calibrated parameter is determined, revealing physical insights into the relative importance of these parameters in the mechanical design problem. By leveraging the advantages from both physically-based and data-driven approaches, the Bayesian learning framework allows for prediction and calibration using a somewhat small dataset, and therefore has great potential for widespread use in multi-material additive manufacturing applications.
查看更多>>摘要：A Ni3Al-based alloy was prepared for the first time using a twin wire and arc additive manufacturing (T-WAAM) by adjusting the relative feeding speed of the Ni and Al wires, and the microstructure and mechanical properties of the deposited alloy were investigated. The results showed that the deposited Ni3Al-based alloy consisted of a dendritic γ + γ' dual-phase structure and an interdendritic γ' block. The interdendritic γ' block was partially transformed into a γ + γ' dual-phase structure owing to multiple thermal cycles during the deposition process. Thus, the proportion of the γ + γ' dual-phase structure gradually decreased with increasing building height. Meanwhile, the tensile strength and elongation of the deposited alloy exhibited a similar decline in the height direction. Fracture analysis revealed that the interdendritic γ' block hindered dislocation slip in the γ + γ' dual-phase structure, which caused dislocation stacking and stress concentration at the dendrite/interdendritic phase interface. The increase in the interdendritic γ' block was responsible for the decline in the tensile strength and elongation of the deposited alloy along the height direction. With the lowest proportion of interdendritic γ' block, the bottom deposited alloy had an optimal combination with a tensile strength of 817.3 MPa and elongation of 20.9%. Evaluation of the mechanical properties showed that the tensile strength and elongation of the T-WAAMed Ni3Al-based alloy were equivalent to those of commercial cast IC218 and IC221M Ni3Al-based alloys.
查看更多>>摘要：Sheet metal forming technologies, such as stamping and deep drawing, have been widely used in automotive, rail and aerospace industries for lightweight metal component manufacture. It requires specially customised presses and dies, which are very costly, particularly for low volume production of extra-large engineering panel components. In this paper, a novel recursive tool path prediction framework, impregnated with a deep learning model, is developed and instantiated for the forming sequence planning of a consecutive rubber-tool forming process. The deep learning model recursively predicts the forming parameters, namely punch location and punch stroke, for each deformation step, which yields the optimal tool path. Three series of deep learning models, namely single feature extractor, cascaded networks (including state-of-the-art deep networks) and long short-term memory (LSTM) models are implemented and trained with two datasets with different amounts of data but the same data diversity. The learning results show that the single LSTM model trained with the larger dataset has the most superior learning capability and generalisation among all models investigated. The promising results from the LSTM indicate the potential of extending the proposed recursive tool path prediction framework to the tool path planning of more complex sheet metal components. The analysis on different deep networks provides instructive references for model selection and model architecture design for sheet metal forming problems involving tool path design.
查看更多>>摘要：The defective regimes in metal-based Laser Powder Bed Fusion (LPBF) processes can be minimized by deploying in-situ monitoring strategies comprising Machine learning (ML) algorithms and sensing techniques. So far, algorithms trained for monitoring a particular material type cannot be re-used to monitor another material in Additive Manufacturing (AM). This is a topic rarely researched in AM. Inspired by the idea of transfer learning in ML, we demonstrate the knowledge learned by the two native Deep Learning (DL) networks, namely VGG and ResNets, on four LPBF process mechanisms such as balling, Lack of Fusion (LoF) pores, conduction mode, and keyhole pores in stainless steel (316L) can be transferred to bronze (CuSn8). In this work, the spectrograms computed using Wavelet Transforms (WT) on Acoustic Emissions (AE) during the LBPF process of stainless steel and bronze are used for training the two DL networks. Either network is first trained for classification by spectrograms representing four mechanisms during the processing of stainless steel. The trained model is then re-trained using transfer learning with spectrograms from bronze data for a similar classification task. The accuracy of the two networks during transfer learning shows that it is effectively possible to learn transferable features from one material to another with minimum network training time and dataset collection.
查看更多>>摘要：Cryogenic forming has been proposed for the fabrication of complex aluminum alloys tubular components to overcome the shortcomings of traditional forming processes. It is difficult to evaluate the cryogenic biaxial deformation behavior. In this study, a novel cryogenic hydro-bulging device was established to evaluate cryogenic deformation behavior and verify the feasibility of the new forming process. The cryogenic biaxial stress-strain relations were determined using a new analytical model to quantitatively characterize the hardening behavior. The cryogenic deformation mechanism was revealed by microstructure characterization. It is found that the maximum expansion rate of 6061 aluminum alloy tube at ? 196 ℃ increases to 34.0% ± 0.6%, being 99.8% higher than that at room temperature. The obtained cryogenic biaxial flow curve exhibits the significantly improved plasticity and hardening ability with a strain hardening exponent of 0.43 at ? 196 ℃. The high cryogenic strain hardening rate contributes to the stable uniform deformation. The improved cryogenic plasticity is associated with the diminished dislocations accumulation at grain boundaries. The enhanced cryogenic hardening ability is attributed to the impeded movable dislocations slip. The research demonstrates that cryogenic medium pressure forming has great potential for the fabrication of complex aluminum alloy tubular components.
查看更多>>摘要：Water quenching is an effective heat treatment process to produce high-quality metallic structures. Accurate and efficient prediction of the full-field temperature inside the part to capture and control the residual stresses and part quality remains a challenging task. This paper proposes a simple and easy-to-use model for full-field temperature recovery during water quenching processes, using physics-informed machine learning (ML). The novelty of the ML framework is that it only needs temperature measurements of sparse locations to efficiently/accurately recover the full spatio-temporal temperature field without invoking sophisticated multiphysics simulations. The ML framework consists of two tightly connected neural network (NN) models: (1) Firstly, a physics-informed neural network (PINN)-based surrogate model is constructed. The surrogate model, which approximates a high-fidelity finite element model, is responsible for quickly outputting the full-field temperature distribution from the parameterized thermal boundary conditions (BCs). (2) Then, another neural network is constructed to project the available experimental data onto the surrogate model and learn the optimal thermal BC from the parametric space, which produces the best full-field temperature prediction in the surrogate model. The proposed ML framework features high efficiency, accuracy, and universality for temperature prediction in quenching processes. These features are carefully demonstrated and the framework is validated using experimental measurements.
查看更多>>摘要：To achieve ultra-strong joint performance and high processing efficiency of joining dissimilar metals, a novel hybrid process, Expanding-Welding method (EW), was proposed. EW effectively combined metallurgical bonding and mechanical expanding effects in a single process. To demonstrate its effectiveness, EW was applied to join aluminum tube to steel tube-sheet hybrid structure. Molecular dynamics analysis showed that the Al/Fe diffusion coefficient was increased by 2 orders of magnitude under the compression and shear condition. Severe plastic deformation introduced by the welding tool induced rapid atom diffusion behavior, and the thickness of the diffusion layer reached 9.5 ± 0.4 μm. The maximum joint pull-out strength was 176.5 ± 13.7 MPa, reaching 73.5% of that of base Al alloys. The success of this hybrid deformation-welding process can increase energy efficiency in many industrial sectors, such as constructing reliable tube-to-tube-sheet structures with dissimilar metals for heat exchangers in the field of petroleum and chemical industries.
查看更多>>摘要：High quality plate-shaped A356 plate castings, with length much greater than the feeding distance, are difficult to make by conventional sand casting processes. This article describes our initial work on combining mold heating with ablation cooling in making plate-shaped castings of high internal quality. Our experimental results indicate that shrinkage porosity can be largely eliminated when the mold temperature is higher than 200 °C. Furthermore, the morphology of eutectic silicon is modified, from flake-like in sand casting to coral-like in castings subjected to ablation cooling. As a result, the mechanical properties of the plate casting, especially ductility, are significantly enhanced by the combined effect of mold heating and ablation cooling. Numerical modeling was performed in this study to quantify the cooling conditions involved.
NSTL
Elsevier