旗袍三围规格的优化设计研究

旗袍需要拥有合身的廓形,贴体程度要求高且强调人体曲线美感,而旗袍的松量设计直接影响其外观造型的优美程度及穿着舒适感。文章首先采用正交实验法针对相同款式旗袍设计了9种不同水平组合的三围松量设置方案,并制作成相同款式不同三围规格的旗袍样衣;然后利用心理学标尺,对9件旗袍样衣的静态造型和穿着舒适感分别进行主观评价,并利用直观分析法和方差分析法综合分析主观评价结果;最后对分析结果进行验证,最终找出外观造型优美程度高和穿着舒适感佳的三围松量水平组合,为旗袍三围规格的优化设计提供理论依据。     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Computational Investigation of Effects of Grain Size on Ballistic Performance of Copper

Numerical simulations were conducted to compare ballistic performance and penetration mechanism of copper (Cu) with four representative grain sizes. Ballistic limit velocities for coarse-grained (CG) copper (grain size ≈ 90 µm), regular copper (grain size ≈ 30 µm), fine-grained (FG) copper (grain size ≈ 890 nm), and ultrafine-grained (UG) copper (grain size ≈ 200 nm) were determined for the first time through the simulations. It was found that the copper with reduced grain size would offer higher strength and better ductility, and therefore renders improved ballistic performance than the CG and regular copper. High speed impact and penetration behavior of the FG and UG copper was also compared with the CG coppers strengthened by nanotwinned (NT) regions. The comparison results showed the impact and penetration resistance of UG copper is comparable to the CG copper strengthened by NT regions with the minimum twin spacing. Therefore, besides the NT-strengthened copper, the single phase copper with nanoscale grain size could also be a strong candidate material for better ballistic protection. A computational modeling and simulation framework was proposed for this study, in which Johnson–Cook (JC) constitutive model is used to predict the plastic deformation of Cu; the JC damage model is to capture the penetration and fragmentation behavior of Cu; Bao–Wierzbicki (B-W) failure criterion defines the material's failure mechanisms; and temperature increase during this adiabatic penetration process is given by the Taylor–Quinney method.     

Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated Python code validated with a machined test piece

Achieving high workpiece accuracy is the long-term goal of machine tool designers. There are many causes for workpiece inaccuracy, with thermal errors being the most common. Indirect compensation (using prediction models for thermal errors) is a promising strategy to reduce thermal errors without increasing machine tool costs. The modelling approach uses transfer functions to deal with this issue; it is an established dynamic method with a physical basis, and its modelling and calculation speed are suitable for real-time applications. This research presents compensation for the main internal and external heat sources affecting the 5-axis machine tool structure including spindle rotation, three linear axes movements, rotary C axis and time-varying environmental temperature influence, save for the cutting process. A mathematical model using transfer functions is implemented directly into the control system of a milling centre to compensate for thermal errors in real time using Python programming language. The inputs of the compensation algorithm are indigenous temperature sensors used primarily for diagnostic purposes in the machine. Therefore, no additional temperature sensors are necessary. This achieved a significant reduction in thermal errors in three machine directions X, Y and Z during verification testing lasting over 60 h. Moreover, a thermal test piece was machined to verify the industrial applicability of the introduced approach. The results of the transfer function model compared with the machine tool's multiple linear regression compensation model are discussed.     

Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization

Overhead line energization can be performed under normal or faulty conditions. The latter indicates an occurrence of a fault along the line that exists at the moment of energization. This can lead to significant over-voltages that could endanger proper line operation. The issue gets more complex to protective devices when it comes to high-resistance faults, which impair the ability of relays to react promptly and accurately. Consequently, installation of additional detection devices becomes necessary. This paper proposes a novel method that properly detects and classifies line operating conditions at the moment of energization. The method is designed to be useful for utilities and it can be considered as a low-cost, fast, and accurate detection and classification approach, suitable for dealing with both low-resistance and high-resistance faults. Through comprehensive mathematical modeling it was found that both normal and faulty conditions during line energization are accompanied by the zero-sequence voltages of specific characteristics. The differences between zero voltage sequences are reflected in harmonic content, magnitudes of dominant frequencies, and their phase angles in regard to supply voltage. These findings are taken as the method's detection and classification criteria. The validity of the proposed model is verified by simulations and by field measurements.     

Numerical studies on heat and fluid flow of nanofluid in a partially heated vertical annulus

A numerical investigation on natural convective heat transfer of nanofluid (Al₂O₃+water) inside a partially heated vertical annulus of high aspect ratio (352) has been carried out. The computational fluid dynamics solver Ansys Fluent is used for simulation and results are presented for various volume fraction of nanoparticles (0‐0.04) at different heat flux values (3‐12 kW/m²). Two well‐known correlations for evaluating thermal conductivity and viscosity have been used. Thus different combinations of the available correlations have been set to form four models (I, II, III, and IV). Therefore, a detailed analysis has been executed to identify effects of thermophysical properties on heat transfer and fluid flow of nanofluids using different models. The results show enhancement in heat transfer coefficient with volume fraction of nanoparticles. Highest enhancement achieved is found to be 14.17% based on model III, while the minimum is around 7.27% based on model II. Dispersion of nanoparticles in base fluid declines the Nusselt number and Reynolds number with different rates depending on various models. A generalized correlation is proposed for Nusselt number of nanofluids in the annulus in terms of volume fraction of nanoparticles, Rayleigh number, Reynolds number, and Prandtl number.     

FDM用PETG系列耗材的改性研究

对市售聚对苯二甲酸乙二醇酯-1，4-环己烷二甲醇酯（PETG）丝材及原材料进行研究，选用PETG 2012作为制备熔融堆积成型技术（FDM）用PETG丝材的基体树脂。选用聚碳酸酯（PC）对基体PETG进行增韧增强改性，并制成3D打印高分子丝材进行打印测试，探讨了PC含量对PETG耗材及制件性能的影响。结果表明，在低PC含量下，PETG与PC的相容性较好，PETG与PC制件未出现明显的分层，分布比较均匀；共混物的流动性随着PC含量的增加而降低，且打印件的维卡软化温度和热变形温度随着PC用量的增加而增加；此外，PC材料的加入使得PETG打印件的力学性能得到较大改善：打印件的拉伸强度随着PC用量的增加而增大，缺口冲击强度却随着PC用量的增加呈现先增加后减小的趋势； PETG/PC的配比为1∶1时，可制得力学性能优异的3D打印耗材。