正交实验设计优选FDM_3D打印工艺参数

由于FDM_(3)D打印工艺参数对制件力学性能有较大影响,采用正交实验设计方法,系统地探讨了聚乳酸(PLA)材料在熔融沉积成型3D打印过程中,不同工艺参数对试样拉伸性能的影响程度。实验结果表明,在4个工艺参数中,填充密度对PLA试件的拉伸性能影响最为显著,而打印速度对拉伸强度的影响则相对较小。通过极差分析得出,当打印层厚为0.25 mm,填充密度为70%,填充速度为50 mm/s,并采用内六角填充模式时,可以制得拉伸性能最优的PLA制件,方差分析表明填充密度对抗拉强度影响较显著(P<0.05),层高、打印速度、填充单元对抗拉强度影响均不显著。最后,通过实验证实了上述工艺下的试件具有最高的拉伸强度。这一发现对于优化FDM 3D打印工艺参数,提升PLA制件力学性能具有重要的指导意义。

基于FDM工艺PLA材料制件成型精度研究

为研究熔融沉积(FDM)3D打印工艺参数对制件成型精度的影响,提高聚乳酸(PLA)材料在该技术中的应用水平,重点考察了打印速度、分层厚度、打印温度、填充率四个因素对制件尺寸精度的影响。基于四因素四水平正交试验法,对打印制件尺寸的测量结果进行极差分析,得到最优打印参数为打印速度50mm/s,分层厚度0.10mm,打印温度210℃,填充率15%。多因素方差分析结果表明:打印速度、填充率会对X,Y向尺寸精度产生影响;填充率、分层厚度会对Z向尺寸精度产生影响。

基于FDM 3D打印机机械结构和控制系统设计的研究

研究FDM技术支持下的3D打印机设计策略。使用步进控制精度在0.002 mm的丝杠滑块和步进电机的核心坐标控制系统,分别使用1组丝杠控制X轴和Y轴的定位,使用2组丝杠控制Z轴的定位,使用3组FDM挤出系统控制彩色打印过程,在探讨了坐标控制算法和冷却成型算法后,设计了一种包含7个执行机构且有LED综合状态显示功能的3D打印机机械结构和控制系统,且该系统可直接通过USB连接线从桌面端直接将文件传输到3D打印机的ARM系统中。基于FDM 3D打印机机械结构和控制系统设计的研究,可以得出:通过对坐标控制算法和冷却成型算法进行革新,提升对步进电机的控制策略,增加驱动程序可识别的3D模型文件种类,对FDM材料的成型过程进行进一步优化控制,是未来FDM技术支持下的3D打印机重点发展方向。

基于FDM工艺的多功能3D打印机结构设计

该文针对传统3D打印机对于零部件的打印精度、加工效率以及许多异形曲面结构打印难以达到加工需求,且设备在运作时还会发热振动,产生噪音等问题进行优化,采用Creo软件设计一种基于熔融沉积制造(FDM)工艺且具备打印、打磨、清扫功能的3D打印机,并对多个零部件3D模型进行仿真和力学分析,分析喷头的温度场分布及五孔连接盖板、脚垫支架的受力分布。结果表明,该设计可实现减少耗材,降低发热振动,有效提高打印精度,缩短打印时间,结构稳定合理,具有较好的推广应用价值。

3D Printing with a 5-Axis FDM Printer Using Bézier Techniques

In the last decade,3D printing,especially fused deposition modeling(FDM),has revolutionized manufacturing with intricate designs.Traditional 3-axis FDM printers face challenges with complex geometries,but 5-axis versions offer more design freedom.However,it requires specialized strategies.This research presents a model for 5-axis FDM printers using Bézier curves with an algorithm to enhance print quality.The result shows significant accuracy improvements,especially for curve-based tasks.In addition,this study deepens the understanding of 5-axis FDM technology,setting a solid basis for further research and potentially refining manufacturing methods.

基于FDM工艺的3D打印制作手办探究

作为动漫周边衍生品的手办随着动漫产业的蓬勃发展,受到了很多动漫爱好者的喜爱,传统的手办制作工艺在设计、生产、版权等因素的影响下单个成本较高,且在设计风格上不一定符合动漫爱好者的审美喜好。随着3D打印技术的发展,桌面级打印机逐渐走进了普通人的生活中,使得动漫爱好者可以自行制作符合自己喜欢的手办。本文研究了传统手办制作的难点与3D打印制作手办的优势,并使用基于熔融沉积制造(Fused Deposition Modeling,FDM)工艺的3D打印机分析了手办制作过程中影响其质量的因素,提出了合理的方法提高3D打印制作手办的质量。

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Surrogate modeling for unsaturated infiltration via the physics and equality-constrained artificial neural networks

Machine learning(ML)provides a new surrogate method for investigating groundwater flow dynamics in unsaturated soils.Traditional pure data-driven methods(e.g.deep neural network,DNN)can provide rapid predictions,but they do require sufficient on-site data for accurate training,and lack interpretability to the physical processes within the data.In this paper,we provide a physics and equalityconstrained artificial neural network(PECANN),to derive unsaturated infiltration solutions with a small amount of initial and boundary data.PECANN takes the physics-informed neural network(PINN)as a foundation,encodes the unsaturated infiltration physical laws(i.e.Richards equation,RE)into the loss function,and uses the augmented Lagrangian method to constrain the learning process of the solutions of RE by adding stronger penalty for the initial and boundary conditions.Four unsaturated infiltration cases are designed to test the training performance of PECANN,i.e.one-dimensional(1D)steady-state unsaturated infiltration,1D transient-state infiltration,two-dimensional(2D)transient-state infiltration,and 1D coupled unsaturated infiltration and deformation.The predicted results of PECANN are compared with the finite difference solutions or analytical solutions.The results indicate that PECANN can accurately capture the variations of pressure head during the unsaturated infiltration,and present higher precision and robustness than DNN and PINN.It is also revealed that PECANN can achieve the same accuracy as the finite difference method with fewer initial and boundary training data.Additionally,we investigate the effect of the hyperparameters of PECANN on solving RE problem.PECANN provides an effective tool for simulating unsaturated infiltration.

TPU导电丝材FDM打印及性能研究

为解决熔融沉积成型(FDM)打印器件力学性能低、功能性单一等问题,提出将聚氨酯(TPU)导电丝材用于FDM打印的方法,对打印件的力学性能、电磁屏蔽性能、压力传感性能进行了测试。结果表明:打印出的导电TPU试样表现出较好的力学性能,其抗拉强度为61.48 MPa,弹性模量为41.64 MPa,断裂伸长率为134.33%;在弯曲应变为10%时,弯曲强度为0.15 MPa,弹性模量为1.38 MPa,厚度为4 mm的打印试样在测试频率11.3 GHz时电磁屏蔽值为11.9 dB。制备的传感器工作范围为0~100 k Pa,其在0~10 kPa和10~100 kPa压力范围的灵敏度分别为0.107 kPa~(-1)和0.002 kPa~(-1),具有良好的稳定性和重复性。导电TPU丝材在电磁屏蔽、压力传感方面具有潜在的应用前景,拓宽了FDM打印的材料选择和应用潜力。

A methodology for damage evaluation of underground tunnels subjected to static loading using numerical modeling

We have proposed a methodology to assess the robustness of underground tunnels against potential failure.This involves developing vulnerability functions for various qualities of rock mass and static loading intensities.To account for these variations,we utilized a Monte Carlo Simulation(MCS)technique coupled with the finite difference code FLAC^(3D),to conduct two thousand seven hundred numerical simulations of a horseshoe tunnel located within a rock mass with different geological strength index system(GSIs)and subjected to different states of static loading.To quantify the severity of damage within the rock mass,we selected one stress-based(brittle shear ratio(BSR))and one strain-based failure criterion(plastic damage index(PDI)).Based on these criteria,we then developed fragility curves.Additionally,we used mathematical approximation techniques to produce vulnerability functions that relate the probabilities of various damage states to loading intensities for different quality classes of blocky rock mass.The results indicated that the fragility curves we obtained could accurately depict the evolution of the inner and outer shell damage around the tunnel.Therefore,we have provided engineers with a tool that can predict levels of damages associated with different failure mechanisms based on variations in rock mass quality and in situ stress state.Our method is a numerically developed,multi-variate approach that can aid engineers in making informed decisions about the robustness of underground tunnels.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

FDM连续挤压成型过程中温度场的有限元分析

在熔融沉积成型中,针对动态重新划分网格法描述金属流动变形存在计算复杂且工作量大的问题,提出一种连续挤压成型过程中温度场的有限元分析法。通过采用逐次单元法对熔丝在挤压槽内的应力进行分析,得出铝丝与轮槽各接触面上的热流密度,建立温度场有限元分析的数学模型。采用热流密度等效法对金属流动变形进行数值分析,减少计算时间,提高CPU效率。利用有限元分析软件对挤出喷头中的温度场进行热力耦合模拟仿真,获得在不同转速下挤压槽内温度场分布等值线图。仿真结果表明,熔丝在挤压成型过程中,温度场分布均匀,总体状态稳定,保证了成型质量,改进后程序运算时间减少30.9%,CPU运行效率优化17.3%。

一种新型六自由度FDM打印机结构方案设计

FDM技术(熔融层积成型技术)作为制造业企业运用最广泛的一项关键技术,在打印悬空、复杂几何形状试制件过程中产生大量的支撑结构,会造成打印丝材的浪费和降低打印效率,同时还会导致所打印的产品表面质量降低以及繁重的产品后处理工作。基于FDM龙门结构,通过增加打印平台2个旋转自由度和打印喷头的1个旋转自由度,提出了一种新型六自由度FDM打印机结构设计方案;并对打印平台和打印喷头的传动结构作进一步三维结构设计;对模型进行了仿真试验,验证了该装置能有效实现减少支撑结构效果,这对于FDM打印机结构设计优化提出了新的发展思路。

基于FDM的煤矿机械轴承劣化程度辨识

针对煤矿机械轴承运行过程中劣化程度较难确定致使无法针对性地进行维护更换的问题,提出了基于傅里叶分解方法(FDM)的煤矿机械轴承劣化程度辨识方法。首先对齿轮箱轴承中不同劣化程度的振动信号进行FDM分解,进而得到一系列傅里叶固有频带函数分量;然后通过各分量与原信号的相关度分析,筛选出优选分量,并对其进行奇异值分解得到一系列奇异值特征向量;最后将各劣化程度对应的奇异值特征向量作为极限学习机(ELM)的输入进行轴承劣化程度的辨识。实验结果表明,该方法能较好地应用于轴承的劣化程度辨识,对轴承的预知性维护、更换具有较大的现实意义。

Progressive fragmentation of granular assemblies within rockslides: Insights from discrete-continuous numerical modeling

Rock fragmentation plays a critical role in rock avalanches,yet conventional approaches such as classical granular flow models or the bonded particle model have limitations in accurately characterizing the progressive disintegration and kinematics of multi-deformable rock blocks during rockslides.The present study proposes a discrete-continuous numerical model,based on a cohesive zone model,to explicitly incorporate the progressive fragmentation and intricate interparticle interactions inherent in rockslides.Breakable rock granular assemblies are released along an inclined plane and flow onto a horizontal plane.The numerical scenarios are established to incorporate variations in slope angle,initial height,friction coefficient,and particle number.The evolutions of fragmentation,kinematic,runout and depositional characteristics are quantitatively analyzed and compared with experimental and field data.A positive linear relationship between the equivalent friction coefficient and the apparent friction coefficient is identified.In general,the granular mass predominantly exhibits characteristics of a dense granular flow,with the Savage number exhibiting a decreasing trend as the volume of mass increases.The process of particle breakage gradually occurs in a bottom-up manner,leading to a significant increase in the angular velocities of the rock blocks with increasing depth.The simulation results reproduce the field observations of inverse grading and source stratigraphy preservation in the deposit.We propose a disintegration index that incorporates factors such as drop height,rock mass volume,and rock strength.Our findings demonstrate a consistent linear relationship between this index and the fragmentation degree in all tested scenarios.

Discontinuity development patterns and the challenges for 3D discrete fracture network modeling on complicated exposed rock surfaces

Natural slopes usually display complicated exposed rock surfaces that are characterized by complex and substantial terrain undulation and ubiquitous undesirable phenomena such as vegetation cover and rockfalls.This study presents a systematic outcrop research of fracture pattern variations in a complicated rock slope,and the qualitative and quantitative study of the complex phenomena impact on threedimensional(3D)discrete fracture network(DFN)modeling.As the studies of the outcrop fracture pattern have been so far focused on local variations,thus,we put forward a statistical analysis of global variations.The entire outcrop is partitioned into several subzones,and the subzone-scale variability of fracture geometric properties is analyzed(including the orientation,the density,and the trace length).The results reveal significant variations in fracture characteristics(such as the concentrative degree,the average orientation,the density,and the trace length)among different subzones.Moreover,the density of fracture sets,which is approximately parallel to the slope surface,exhibits a notably higher value compared to other fracture sets across all subzones.To improve the accuracy of the DFN modeling,the effects of three common phenomena resulting from vegetation and rockfalls are qualitatively analyzed and the corresponding quantitative data processing solutions are proposed.Subsequently,the 3D fracture geometric parameters are determined for different areas of the high-steep rock slope in terms of the subzone dimensions.The results show significant variations in the same set of 3D fracture parameters across different regions with density differing by up to tenfold and mean trace length exhibiting differences of 3e4 times.The study results present precise geological structural information,improve modeling accuracy,and provide practical solutions for addressing complex outcrop issues.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Hybrid modeling for carbon monoxide gas-phase catalytic coupling to synthesize dimethyl oxalate process

Ethylene glycol(EG)plays a pivotal role as a primary raw material in the polyester industry,and the syngas-to-EG route has become a significant technical route in production.The carbon monoxide(CO)gas-phase catalytic coupling to synthesize dimethyl oxalate(DMO)is a crucial process in the syngas-to-EG route,whereby the composition of the reactor outlet exerts influence on the ultimate quality of the EG product and the energy consumption during the subsequent separation process.However,measuring product quality in real time or establishing accurate dynamic mechanism models is challenging.To effectively model the DMO synthesis process,this study proposes a hybrid modeling strategy that integrates process mechanisms and data-driven approaches.The CO gas-phase catalytic coupling mechanism model is developed based on intrinsic kinetics and material balance,while a long short-term memory(LSTM)neural network is employed to predict the macroscopic reaction rate by leveraging temporal relationships derived from archived measurements.The proposed model is trained semi-supervised to accommodate limited-label data scenarios,leveraging historical data.By integrating these predictions with the mechanism model,the hybrid modeling approach provides reliable and interpretable forecasts of mass fractions.Empirical investigations unequivocally validate the superiority of the proposed hybrid modeling approach over conventional data-driven models(DDMs)and other hybrid modeling techniques.

Exploring Capillary Fringe Flow:Quasilinear Modeling with Kirchhoff Transforms and Gardner Model

Recent studies have underscored the significance of the capillary fringe in hydrological and biochemical processes.Moreover,its role in shallow waters is expected to be considerable.Traditionally,the study of groundwater flow has centered on unsaturated-saturated zones,often overlooking the impact of the capillary fringe.In this study,we introduce a steady-state two-dimensional model that integrates the capillary fringe into a 2-D numerical solution.Our novel approach employs the potential form of the Richards equation,facilitating the determination of boundaries,pressures,and velocities across different ground surface zones.We utilized a two-dimensional Freefem++finite element model to compute the stationary solution.The validation of the model was conducted using experimental data.We employed the OFAT(One_Factor-At-Time)method to identify the most sensitive soil parameters and understand how changes in these parameters may affect the behavior and water dynamics of the capillary fringe.The results emphasize the role of hydraulic conductivity as a key parameter influencing capillary fringe shape and dynamics.Velocity values within the capillary fringe suggest the prevalence of horizontal flow.By variation of the water table level and the incoming flow q0,we have shown the correlation between water table elevation and the upper limit of the capillary fringe.