Study on suppression strategy of jet lag effect in melt electrowriting

Zhongfei Zou,Yu Wang,Zhen Shen,Nan Luo 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9

The melt electrowriting (MEW) has broad applications in regenerative medicine and micro-nano electronics. It is an efficient micro-nano scale additive manufacturing technology; however, the fiber jet lag effect of MEW limits the deposition precision and resolution of fiber shape. In this study, the principle of the jet lag effect is studied to overcome the defect of printed structure distortion and improve the ability to print complex structures. A mathematical model of trailing fiber trajectory is established. The study covers jet lag and liquid rope coiling analysis at different speeds. A strategy is adopted by introducing a buffer zone at the corner of the printing structure. The printing path is subdivided and optimized to suppress the influence of jet lag. The results show that the deposited fibers' corner radius is around 63.81±5.66 μm, which is significantly smaller than that of unoptimized groups. Finally, by utilizing the improved printing paths, the high-precision and complex structures are printed, which demonstrates the feasibility of optimizing the buffer zone for the MEW.

NPC-IGCT Phase Module Clamp Circuit LRC Parameters Design considering FRD snappy Recovery

Yang Ju Zou,Jia Xi Hu,Zhen Yu Ma,Jian Ping Liu,Run Qing Guo,Zhi Xue Zhang 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

In this paper, a method for neutral point clamping-integrated gate commutated thyristor (NPCIGCT) phase module clamp circuit parameters design considering FRD snappy recovery is present. Based on the snappy recovery theory, and analysis of snappy recovery factors, the paper has shown decreasing current commutating slope can attenuated snappy recovery affect effectively. Then, the paper has shown that it is reasonable for decreasing di/dt by increasing inductance of NPC-IGCT phase module clamp circuit, based on the circuit working principle. Then, the new clamp circuit parameters design method which combine multi-objective optimization solution mathematical module of the circuit, fast recovery diode (FRD) snappy recovery, devices overvoltage and loss is shown in paper. A design example and its test results have demonstrated both IGCT and FRD characteristics have been guaranteed and ensuring safety and reliability of the NPC - IGCT phase module.

Clinical Study of Tumor Angiogenesis and Perfusion Imaging Using Multi-slice Spiral Computed Tomography for Breast Cancer

Xu, Na,Lei, Zhen,Li, Xiao-Long,Zhang, Jun,Li, Chen,Feng, Guo-Quan,Li, Di-Nuo,Liu, Jing-Yi,Wei, Qiang,Bian, Ting-Ting,Zou, Tian-Yu Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.1

Objectives: To explore the correlation between multi-slice spiral CT (MSCT) perfusion parameters and the expression of vascular endothelial growth factor (VEGF) as well as matrix metalloproteinase-2 (MMP-2) in breast cancer. Methods: Forty five breast cancer patients and 16 patients with benign breast tumor, both confirmed by pathology examination, were enrolled. All underwent MSCT perfusion imaging to obtain perfusion maps and data for parameters including blood flow (BF), blood volume (BV) and permeability surface (PS). Cancer patients did not receive treatment prior to surgery. The expression of VEGF and MMP-2 were examined with both immunohistochemistry and Western blotting. Results: The levels of VEGF and MMP-2 by immunohistochemistry were significantly higher in the breast cancer group (P < 0.01) than the benign tumor group. Relative OD values from Western blotting were also higher in cancer cases (P < 0.05). Similarly, the mean MSCT perfusion parameters (BF, BV, PS) were significantly higher in the breast cancer group (P < 0.01), BF and BV positively correlating with VEGF expression (r = 0.878 and 0.809 respectively, P < 0.01); PS and VEGF and MMP-2 expression were also positively correlated (r= 0.860, 0.786 respectively, P < 0.01). Conclusion: There is a correlation between breast cancer MSCT perfusion parameters and VEGF andMMP-2 expression, which might be useful for detection of breast lesions, qualitative diagnosis of breast cancer, and evaluation of breast cancer treatment.

Multi-Variable and Bi-Objective Optimization of Electric Upsetting Process for Grain Refinement and Its Uniform Distribution

Guo-zheng Quan,Le Zhang,Chao An,Zhen-yu Zou 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.6

It is significant to adjust the microstructures of preforms in pursuit of high-quality exhaust valves. This work is a novel attempt to identify the optimum process parameters in electric upsetting of 3Cr20Ni10W2 high-alloy to achieve grain refinement and uniform distribution by multi-objective genetic algorithm (MOGA) optimization. A finite element (FE) model on basis of electric-thermalmechanical and macro-micro sequential multi-physics analysis methods was developed in software MSC. Mar. And different schedules of four independent process variables (heating current (I), clamping length (L), upsetting pressure (Pset) and velocity of the anvil cylinder (v)) were performed aiming to achieve two objective indicators (average grain size (dav) and inhomogeneous degree of grain distribution (σd)). Then, two objective response surfaces were constructed as the functions between the two indicators and the four independent process variables. As per the criterion that simultaneously minimize dav and σd, the processing parameters (Pset, L, v, I) were optimized by MOGA, and corresponding numerical simulation were performed. The results show that both dav and σd are improved significantly at the optimal process condition as verified by the trial-manufacture experiments, which validated the optimal design and corresponding simulation based on grain refinement and uniform distribution by MOGA was credible and effective.

Modelling of the Hot Flow Behaviors for Ti-13Nb-13Zr Alloy by BP-ANN Model and Its Application

Guo-zheng Quan,Shi-ao Pu,Zong-yang Zhan,Zhen-yu Zou,Ying-ying Liu,Yu-feng Xia 한국정밀공학회 2015 International Journal of Precision Engineering and Vol. No.

The plastic deformation mechanisms and the constitutive model of flow behaviors at different deformation conditions in biomedical titanium alloy are an essential step to optimize the design of any forging process for implant productions. A series of isothermal compressions tests on Ti-13Nb-13Zr alloy in a wide range of true strain, temperature and strain rate were conducted on a thermomechanical simulator. The hot flow behaviors with different softening mechanisms, including dynamic recrystallization and dynamic recovery, were characterized based on true strain-stress curves. A back-propagational artificial neural network (BP-ANN) method was conducted to evaluate and predict this non-linear problem by self-training to be adaptable to the material characteristics. The flow stress of this material a wide deformation condition range can be predicted accurately by the BP-ANN model obtained in this study. The prediction ability of this BP-ANN Model was evaluated by three accuracy indexes, Absolute error, Relative error and Average absolute relative error. Sequently, the developed BP-ANN model was programed and implanted into the finite element (FE) analysis platform, Msc.Marc software. The results have sufficiently articulated that the well-trained ANN model has excellent capability to deal with the complex flow behaviors and has great application potentiality in hot deformation processes.

Construction of Processing Maps based on Expanded Data by BP-ANN and Identification of Optimal Deforming Parameters for Ti-6Al-4V Alloy

Guo-zheng Quan,Hai-rong Wen,Jia-Pan,Zhen-yu Zou 한국정밀공학회 2016 International Journal of Precision Engineering and Vol.17 No.2

The intrinsic relationships between deforming parameters and microstructural mechanisms for Ti-6Al-4V alloy were analyzed by processing maps. A series of thermal compression tests were carried out in the temperatures range of 1023~1323 K (across β-transus) and strain rates range of 0.01~10 s-1 on a Gleeble-3500 thermo-mechanical simulator. Based on the stress-strain data collected from compression tests, a back-propagation artificial neural network (BP-ANN) model was developed, which presents reliable performance in tracking and predicting strain-stress data. By utilizing this model, the volume of stress-strain data was expanded. According to the intensive stress-strain data, the apparent activation energy was calculated to be 564.05 kJ mol-1 and 300.20 kJ mol-1 for α+β-phase field and single β-phase field, respectively. Moreover, the processing maps were constructed at finer intervals of temperature, from which, the stable regions with higher power dissipation efficiency (η > 0.3) and unstable regions with negative instability parameter (ξ < 0) were clarified clearly. By combining processing map with microstructure observations, two main stable softening mechanisms, i.e., globularization and dynamic recovery (DRV) were identified, and globularization-predominant (0.3 < η < 0.55) parameter domain ( < 0.1 s-1) in α+β-phase field and DRV-predominant (0.25 < η < 0.41) parameter domain (0.032 s-1< <1 s-1) in β- phase field were recommended. Manuscript

The Prediction Research of Population Density Based on Deep Learning in Grain Stored Insects

Wu Jian-Jun,Dang Hao,Li Miao,Sun Fu-Yan,Zhu Yu-Hua,Zhen Tong,Zou Bing-Qiang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.10

Precision of pests, in stored grain insect population density, has been a hot and difficult research in pest detection and control system. The accuracy of prediction of pest density will directly affect to warehouse grain temperature and the food quality etc. In order to improve the accuracy, the paper which using the depth study method, established an insects density prediction mode with the depth of the belief network as the core. The model is applied to the algorithm of deep learning predictive control. According to the temperature and humidity of the grain obtained from the actual measurement and the initial density of the pest, we predicted the pest density. Simulation results show that the root mean square error is small between the predictive value and actual value, high prediction accuracy. The deep learning algorithm is applied to the population density of pests is effective.