Experimental and numerical study regarding H-steel all-bolted connection steel frame with composite wall boards

H-steel all-bolted connection steel frame structures with heat preservation and decoration composite wall boards were investigated and the seismic performances of three scaled specimens were studied.The failure modes,hysteresis curves,bearing capacity,ductility,energy dissipation capacity,stiffness degradation and strain distribution were discussed.The calculation method of structural theoretical internal force was presented.The results showed that the overall structural seismic performance was better,and the structural ductility met the demands of elastic-plastic inter-story drift angle for seismic design.The H-steel weak-axis connection structure obtained better energy dissipation capacity,and its bearing capacity and stiffness were slightly different from the strong-axis connection.The heat preservation and decoration performance of composite wallboard and the all-bolted connection of the steel frame realized prefabrication during the whole construction period.The plastic hinge of the steel beam can be moved outwards because of the L-angles,which effectively avoids stress concentration in joint areas and expands the plastic hinge range.The errors between the theoretical structural capacity calculated by the plastic analysis method and the test results were within 2.44%.In addition,structural failure mechanisms and bearing capacities were verified by the finite element(FE)analysis,and the effects of the main parameters on the structures were investigated.The FE verification results were the same as in the test.The research results provide theoretical support and technical guidance for the application of thermal insulation and decorative composite wall panels in H-shaped steel all-bolted steel frames.

Effect of Low pH on Forming Process of Desulfurization Gypsum Composite Boards Strengthened by Melamine-formaldehyde Resin

Through exploring the effects of low pH on the composite system of desulfurization gypsum(DG)enhanced by melamine-formaldehyde resin(MF),it is found that the inducing of sulfate-ion,in contrast to chloride and oxalate ions,favors the longitudinal growth of the crystalline form of the hydration product,which was relatively simple and had the highest length to width(L/D)ratio.At the same time,MF can also improve L/D ratio of gypsum hydration products,which favors the formation of hydrated whiskers.Finally,in a composite system composed of hemihydrate gypsum,MF,and glass fibers,when dilute sulfuric acid was used to regulate pH=3-4,the tight binding formed among the components of the composite system compared to pH=5-6.The hydration product of gypsum adheres tightly to glass fiber surface and produces a good cross-linking and binding effect with MF.The flexural strength,compressive strength,elastic modulus,and water absorption of the desulphurized gypsum composite board is 22.7 MPa,39.8 MPa,5608 MPa,and 1.8%,respectively.

Women on Boards of Directors

This research aims to measure the current gender representation in membership of boards of directors,and to study the factors affecting the presence of women on boards.The results of the study will support the decision makers and policy makers at all levels by providing knowledge that contributes to bridging the current gap in participation of women boards.The methodology used in this research is the descriptive analytical approach,which relies on quantitative methods in the process of collecting and analyzing data.This study represents the boards of directors in the three sectors(public,private,and non-profit)in the Kingdom of Saudi Arabia,The study sample contained 350 targets,where a random sample of 150 people in the public sector,100 people in the private sector,and 100 people in the non-profit sector were selected.The research result found that the majority of the responses believe that the lack of experience and training,and social factors,are the most impact areas of explaining the reasons for the existence of a gap in women’s membership in boards of directors in the three sectors.And the most important reasons for the existence of a gender gap in board membership are:there is no statutory minimum percentage for women’s representation on boards of directors,and scarcity of female competencies in general,and the lack of women occupying leadership positions.

YOLO-RLC:An Advanced Target-Detection Algorithm for Surface Defects of Printed Circuit Boards Based on YOLOv5

Printed circuit boards(PCBs)provide stable connections between electronic components.However,defective printed circuit boards may cause the entire equipment system to malfunction,resulting in incalculable losses.Therefore,it is crucial to detect defective printed circuit boards during the generation process.Traditional detection methods have low accuracy in detecting subtle defects in complex background environments.In order to improve the detection accuracy of surface defects on industrial printed circuit boards,this paper proposes a residual large kernel network based on YOLOv5(You Only Look Once version 5)for PCBs surface defect detection,called YOLO-RLC(You Only Look Once-Residual Large Kernel).Build a deep large kernel backbone to expand the effective field of view,capture global informationmore efficiently,and use 1×1 convolutions to balance the depth of the model,improving feature extraction efficiency through reparameterization methods.The neck network introduces a bidirectional weighted feature fusion network,combined with a brand-new noise filter and feature enhancement extractor,to eliminate noise information generated by information fusion and recalibrate information from different channels to improve the quality of deep features.Simplify the aspect ratio of the bounding box to alleviate the issue of specificity values.After training and testing on the PCB defect dataset,our method achieved an average accuracy of 97.3%(mAP50)after multiple experiments,which is 4.1%higher than YOLOv5-S,with an average accuracy of 97.6%and an Frames Per Second of 76.7.The comparative analysis also proves the superior performance and feasibility of YOLO-RLC in PCB defect detection.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Development and Characterization of Particle Boards Based on Sorghum Husk

This study tackles current environmental challenges by developing innovative and eco-friendly particle boards utilizing sorghum husk, combined with recycled expanded polystyrene (EPS). This dual eco-responsible approach valorizes sorghum husk, often deemed agricultural waste, and repurposes EPS, a plastic waste, thus contributing to CO2 emission reduction and effective waste management. The manufacturing process involves dissolving recycled polystyrene within a solvent to create a binder, which is then mixed with sorghum husk and cold-pressed into composite boards. The study explores the impact of two particle sizes (fine and coarse) and two different concentrations of the recycled EPS binder. Results demonstrate significant variations in the boards’ mechanical properties, displaying a range of Modulus of Rupture (MOR) from 0.84 MPa to 3.85 MPa, and Modulus of Elasticity (MOE) spanning from 658.13 MPa to 1313.25 MPa, influenced by the binder concentration and particle size. These characteristics suggest that the boards can be effectively used in various construction applications, including interior decoration, false ceilings, and potentially for furniture and door manufacturing when combined with appropriate coatings. This study not only exemplifies the valorization of plastic and agricultural wastes but also offers a practical, localized solution to global climate change challenges by promoting sustainable construction materials.

基于深度学习的PCB缺陷检测技术

印刷电路板(printed circuit board,PCB)是电子产品的关键部件。在实际生产过程中,PCB难免会产生多种缺陷,对缺陷进行及时、精准检测具有一定的研究意义与应用价值。传统的检测方法存在速度慢、成本高、精度低的问题。针对PCB缺陷检测问题,开展基于YOLO系列算法研究,在相同的实验环境下,以平均精度、精确率、召回率、每秒传输帧数作为评价性能指标。实验研究发现,YOLOv7在精度方面比YOLOv5有一定的提升,而YOLOv5在训练和推理的速度上比YOLOv7更快。提出融合CBAM(convolutional block attention module)注意力机制模块的YOLOv5改进算法用于PCB缺陷检测。经实验验证,改进算法在PCB缺陷检测的精确性和速度性能上均得到提升,其中,平均精度、精确度和召回率分别提升了7.40%,3.57%和5.63%。

基于SMT-YOLOv8的PCB缺陷检测研究

针对PCB缺陷检测中目标尺寸小、权重文件庞大难以部署的问题,提出一种改进的YOLOv8小目标缺陷检测方法。该方法将SE注意力机制融入C2f中,使网络能够根据通道域的信息给图像不同位置赋予不同的权重,获取更重要的特征信息;在SPPF中引入Basic RFB以增强网络感受野,提升网络的特征提取能力;新增小目标检测尺度,提升模型对微小缺陷的检测能力;舍弃大目标检测尺度,降低计算负荷并缩小权重文件。实验结果表明,在公开的PCB缺陷数据集,改进后的YOLOv8较原算法平均精度提升了2.6%、权重文件缩小了27.3%、FPS达到34.4 ms/帧。

PCB可靠性测试评估方法简述

印制电路板(PCB)技术向高密度、高集成化等方向发展,导致PCB可靠性问题日益凸显。针对PCB失效机理,并结合现有PCB标准及实践应用情况,详细介绍了3种PCB可靠性测试评估的方法——失效率预计法、加速试验预计法和试验鉴定法,以期为从业者开展PCB可靠性测试评估提供一定的帮助。

基于融合MBAM与YOLOv5的PCB缺陷检测方法

随着电子信息产业迅速发展,PCB行业作为电子信息产业的基础,其产品质量对后续生产的电子产品有着决定性影响。针对PCB缺陷目标较小,缺陷类型多,特征不明显,在实际生产过程中易产生误检、漏检等问题,提出了一种多分支注意力MBAM模块方法,在3个不同维度对特征图进行关注,以增强特征提取的能力,对缺陷区域给予更多的注意力表示。通过改进YOLOv5结构,将MBAM与YOLOv5网络结合,有效的提升了对PCB中小目标的检测性能。最后通过在网络不同位置添加MBAM模块进行对比实验,选取了最佳的添加位置。通过在PCB缺陷数据集上的实验结果表明,改进后的PCB缺陷检测算法具有良好的检测性能,优于其他对比算法,最终的AP达到了96.7%,对比标准YOLOv5的94.7%提高了2个百分点,其他项指标均有涨点,在保持检测速度基本不变的情况下,精准地识别PCB缺陷类型。

基于298/77 K循环处理回收PCB中非金属组分

为解决废印制电路板(PCB)中非金属组分回收难且二次污染率大的问题,引入298/77 K循环处理技术,首先分析温度改变对PCB内部结构所造成的影响及PCB内部力学性能的变化,测试静电分选和离心分选对PCB非金属组分回收率的实际影响,之后引入CaF_(2)作为精炼过程中的强氧化剂,测试PCB内部结构发生改变后的硅组分回收率,最后测定回收后硅元素的实际纯度。结果表明:PCB在经过298/77 K循环处理后,内部结构受温度循环影响发生明显变化;各项力学性能均实现明显下降;PCB非金属组分实际产出量和硅组分回收率,相比于传统处理方式实现了明显提升,且对原PCB中各项杂质有较好的去除效果,硅元素占比在97%以上。

PCB中耐高温有机可焊保护剂成膜机理及性能研究

印制电路板铜焊盘表面生成耐高温有机可焊保护剂(HT-OSP)膜是克服无铅高温回流焊工艺并获得良好焊点的关键。选用2-[(2,4-二氯苯基)甲基]-1H-苯并咪唑(C_(14)H_(10)Cl_(2)N_(2))作为成膜剂,在铜层表面生成了HT-OSP膜。理论计算结合对比实验,研究C_(14)H_(10)Cl_(2)N_(2)分子与Cu原子反应生成HT-OSP膜机理。基于量子化学密度泛函理论,模拟C_(14)H_(10)Cl_(2)N_(2)分子与Cu^(+)之间的络合反应;利用红外光谱对HT-OSP膜中的特征官能团进行表征;借助X射线光电子能谱测试HT-OSP膜中Cu元素的化合价;设计对比实验分析Cu^(2+)对生成HT-OSP膜的影响。结果表明:HT-OSP膜生成机理是C_(14)H_(10)Cl_(2)N_(2)分子与Cu原子发生反应生成HT-OSP膜并沉积在铜层表面,Cu^(2+)通过络合反应促进HT-OSP膜生长。另外,HT-OSP膜的分解温度高达531℃,HT-OSP膜保护的铜层放置在自然环境中180天没有被氧化,证明HT-OSP膜具有优异的耐热性和抗氧化性。

高功率微波电磁脉冲对铁路PCB类设备的危害影响及防护措施研究

随着我国铁路朝着电气化和信息化方向不断发展,各种由精密PCB设计集成的电力电子设备广泛应用于各种强弱电系统,当这些设备遭受高功率微波等强电磁脉冲武器照射攻击时,若自身防护能效不足,则会导致设备出现异常状态,直接或间接地影响行车安全。文章通过建模仿真分析高功率微波电磁脉冲对铁路PCB设备的辐射影响和危害评估,并从工程角度提出了一些针对铁路PCB设备应对强电磁冲击的防护建议。

TIA组态控制功能在PCB组装工艺中的应用

针对印刷电路板(PCB)组装工艺装贴方式多样,生产线工艺切换需要改变硬件组态和优化工艺流程,导致生产线停机时间加长、维护成本升高、生产效率降低等问题。以西门子PLC和分布式IO模块为例,提出了组态控制功能在PCB组装系统中的应用方案。采用最大硬件方案组态形成标准机器项目。根据不同的生产工艺、不同的生产方案,存储在专门的组态控制数据块中。当需要切换PCB组装工艺时,加载相应的组态方案,就可以运行新的组态项目和生产工艺,达到快速完成生产工艺切换的目的。使用西门子TIA博途V17的仿真检验,验证了组态控制功能的有效性和可靠性。

ViT和注意力融合的类别不均衡PCB缺陷检测方法

针对实际环境下印刷电路板(PCB)缺陷样本难以收集造成的数据长尾分布和检测精度低以及ViT用于检测时计算复杂度高等问题,提出多尺度ViT特征提取和注意力特征融合的端到端PCB缺陷检测算法。首先结合ViT和部分卷积构建多尺度特征提取网络,利用层级多头注意力对不同尺度的特征图执行自适应的注意力操作,使网络能够更好地捕捉局部和全局信息进而增强其特征提取能力,部分卷积可以降低计算开销。其次,基于能量空域抑制的无参数注意力机制将多尺度特征有效融合,提升网络融合特征图的表达能力。最后,引入对类别不均衡敏感的分类函数对网络的损失函数进行改进,增强网络对类别不平衡数据的拟合程度,提高网络的泛化能力。在3种不同类型的公开PCB数据集上的实验结果表明,所提出的检测算法在PCB表面缺陷数据集的平均精度均值(mAP)均有提升,分别为99.13%、98.67%,99.82%;在类别不均衡的PCB缺陷检测任务上,相较于改进前方法,mAP提升了11.94%,网络检测速度达到25 FPS,为PCB缺陷的检测提供了一种快速、有效的方法。

基于FPCB绕组的导管泵用电动机设计及损耗分析

基于无槽无刷直流电动机具有体积小、功率密度高、无齿槽转矩的特点,采用柔性印刷电路板(FPCB)技术设计了一种导管泵用无槽无刷微型直流电动机,提出了基于3D多物理场耦合模型分析方法,对导管泵用无槽无刷微型直流电动机进行了损耗分析和优化设计.结果表明:导管泵用无槽无刷微型直流电动机的铜损占整个电动机损耗的88%左右,是导致电动机发热的主要原因;在温度场仿真中,温度变化会改变电动机损耗,影响电动机输出转矩;在流场仿真模型中,电动机温度会使人体血液温度升高,影响血液相容性;流动的血液会带走部分热量,降低电动机温度;绕组优化后的导管泵用电动机输出转矩得到提高,损耗得以降低.

基于改进YOLOv5的PCB缺陷检测方法研究

随着工业制造业的发展,印刷电路板(PCB)在电子产品制造中愈发重要。在PCB生产过程中,存在着各种各样的不良缺陷,因此急需一种高效的PCB缺陷检测方法。针对传统的YOLOv5目标检测算法中对于PCB图像检测中存在小目标缺陷检测准确率低的问题,提出了一种基于改进的YOLOv5的PCB缺陷检测方法。首先,针对小目标缺陷存在漏检的问题,在YOLOv5的特征提取网络中加入了高效通道注意力机制(SE)模块,提高对小目标缺陷的特征提取能力,从而提高小目标缺陷的检测精度;其次,为了优化和改进原YOLOv5算法,采用加权损失函数代替原来的损失函数,以充分学习图像的各种特征。在北京大学机器人实验室公开的PCB瑕疵数据集上进行测试,实验结果显示,改进后的模型提高了小目标缺陷检测效果,其mAP值为94.54%,比原算法模型提高了2.1%。可以准确地完成工业生产的印制电路板的缺陷检测任务。

Experimental study of polyurea-coated fiber-reinforced cement boards under gas explosions

Five types of polyurea elastomers were synthesized by changing the isocyanate component and the mechanical properties of polyurea materials were measured. Fiber-reinforced cement boards(FRCB)strengthened by polyurea with different formulations were processed, and a series of experiments were carried out on the specimens with gas explosion devices. The results showed that the conventional mechanical properties of different types of polyureas had their own advantages. Based on the gas explosion overpressure criterion, the blast resistances of reinforced plates were quantitatively evaluated,and the best polyurea was selected to guide the formulation design. The three typical failure modes of polyurea-reinforced FRCBs were flexural, shear, and flexural-shear failure. Dynamic thermodynamics and shock wave spectral analysis revealed that the polyurea did not undergo a glass transition in the gas explosion tests but retained its elastic properties, allowing it to effectively wrap the fragments formed by the brittle substrates.

基于YOLOv5-TGs的PCB缺陷检测算法研究

针对目前PCB缺陷检测算法在实际应用中检测精度低等问题,提出基于改进YOLOv5s的PCB缺陷检测算法YOLOv5-TGs。该算法以YOLOv5s算法模型为基础,首先在主干网络中引入Swin Transformer结构,并取代C3模块中的Bottleneck模块,并使用Ghost卷积模块替换Conv模块,降低了模型的计算复杂度,实现轻量化,同时增加了其接收域,增强PCB缺陷的小目标的特征表达能力;其次,在颈部网络的C3结构后面添加全局注意力机制,更大程度地保留通道和空间信息,在减少特征信息弥散的情况下放大全局跨纬度的交互特征,提高检测效率。最后用SIoU损失函数来代替原有的CIoU损失函数,通过在损失函数代价中引入方向性,加快模型收敛速度,提高回归精度。本文实验使用的是北京大学实验室公开发布的PCB缺陷数据集,结果表明:改进算法的平均精度均值达到98.2%,精确率达到95.5%;相较于YOLOv5s,改进算法的平均精度均值提升了7.3%,精确率提升了7.5%。

基于轻量化的YOLOv5的PCB缺陷检测算法

针对在印刷电路板(printed circuit board,PCB)缺陷检测上网络模型过大且精度较低的缺点,使用Python在Windows上提出了一种基于YOLOv5l改进的PCB缺陷检测算法,并对六种常见的缺陷作为数据集进行检测。采用轻量化网络EfficientNetLite0作为模型的骨干网络﹐并通过对特征金字塔加入P2检测头来获取更小的目标特征。试验结果表明:所提算法对印刷电路板的缺陷有识别精度高、模型小和检测快速的特点;单张图片检测速度达到43.6 ms﹐模型大小为49.1 MB﹐所有类别精度指标达到98.9%。所提算法为未来部属在边缘设备上的工业缺陷检测提供了新思路。