基于逆向工程的闸门底枢磨损量测量

闸门底枢蘑菇头磨损量小且为曲面,传统的测量方法难以精确获得磨损量,文中提出采用基于逆向工程技术的蘑菇头磨损量测量方法。首先通过三维扫描仪获取磨损试验前后的蘑菇头点云数据,其次采用逆向工程软件算法将磨损前后的蘑菇头点云数据对齐,通过3D比较和体积计算,测算出磨损区域的最大磨损高度。结果表明,该方法能够较为准确地实现蘑菇头磨损量的测量,同时再现磨损区域的空间几何形状,实现磨损量测量过程的数字化、定量化和可视化。     

相位法曲面三维全场无接触检测的研究

采用光学函数分析法和傅里叶频谱分析技术，研究了相位法曲面三维全场无接触检测的基本原理和本质特点。在分析条纹类测量图象的幅频待性的基础上，实现了被测曲面调制栅线图象的精确解相和三维信息的求解，从而建立了计算机曲面三维自动检测系统，并应用于汽车覆盖件的检测，获得较好的测量精度。     

复合材料曲面构件缺陷超声三维成像方法

针对目前复合材料曲面结构缺陷检测技术存在的检测结果不直观、效率低等问题,提出一种基于超声相控阵的缺陷三维成像方法。使用三维激光扫描仪获取曲面的点云模型,通过平行截面法规划检测路径,然后使用相控阵轮式探头采集超声图像数据。利用均匀三次B样条函数拟合检测路径与曲面,根据扫查步长和图像序列关系计算超声图像数据点的空间位置以生成超声点云集。最后利用体素化降采样方法对超声检测结果进行重建,实现复合材料内部缺陷的三维成像。实验结果表明,本文方法的缺陷成像结果与CT检测结果的平均误差为1.14 mm,能够快速准确地重建缺陷的位置、形状与尺寸信息,实现复合材料曲面样件内部缺陷的精确表征。     

砂轮磨损自动检测补偿三维曲面数控磨削系统研制

本文主要研制开发了数控磨削控制系统、砂轮测量仪及磨头．利用上述控制系统及磨削设备，解决了三维曲面磨削以及磨削过程中砂轮磨损自动检测和补偿；保证了三维曲面磨削的质量．     

复杂曲面的四边形网格生成算法

介绍了曲面网格化的铺砌法(Paving)，利用包络区域检测技术对网格生成过程中的冲突进行检测。将二维的铺砌方法应用于三维曲面的网格生成，本算法适用于复杂曲面的网格生成。     

上冠流道面开槽的数控加工研究

介绍水轮机转轮模型上冠流道面配合槽的数控加工方法,指出上冠流道面配合槽的作用、加工难点和解决方法。使用三维区域清除的加工策略实现高速、安全的开粗加工,克服了粗加工中的碰撞和磨损刀具的现象,大幅提高了加工的效率。通过SWARF精加工策略,用驱动曲线控制产生高效、光顺、均匀的刀具路线轨迹,减少空走刀的数量,达到非常优化的加工;通过三维偏置精加工完成曲面的槽底,在实际加工中效果良好。     

基于表面轮廓的人字闸门底枢蘑菇头磨损量测量方法

为精确测量人字闸门底枢摩擦副蘑菇头磨损量,采用摩擦磨损试验机对蘑菇头进行模拟摩擦磨损试验,应用千分表测量磨损试样表面轮廓,获取表面三维轮廓数据,并构建表面轮廓图;采用微元法和拟合法计算了曲面磨损体积。其中微元法是以千分表的步长为微元,首先计算出每个微元磨损体积,然后再将球面微元磨损量相加得到球面磨损体积;拟合法是将测量球面数据转化为直角坐标系中数据,然后对坐标系中数据进行曲面拟合得到拟合函数,再对拟合函数求积分得到球面体积,磨损试验前后球面体积差即为球面磨损体积。结果表明:微元法和拟合法与称重法的误差分别为4.66%和4.03%,均满足工程测量要求,为球面磨损量的测量与计算提供了更准确的方法。     

应用三坐标测量机检测刀片模具复杂三维曲面形状精度的方法

为完成模具的复杂三维曲面形状精度的检测,运用三坐标测量机对工件三维曲面形状采集数据,再通过Geomagic Qualify软件的功能对数据进行处理、比对和测量,最后直观地、完整地将工件的三维曲面形状和尺寸的检测结果通过颜色差异区别和列数值的方法呈现出来.     

逆向工程中曲面零件的快速成型方法探究

以结构复杂的曲面类零件为研究对象,着重介绍了该零件的逆向建模,三维检测及快速成型这三个过程。通过对曲面类零件进行三维扫描、点云处理、逆向建模、三维检测和快速成型等试验设计,得到了表面质量高,精度高的新产品,实现了现有产品的快速更新换代,并且提出了一条基于逆向工程的产品设计,检测及快速成型的路线。此方法提高了设计效率,降低设计成本,缩短研发周期,便于定制化生产和工业化改造。     

基于结构光的路面裂缝检测方法研究

路面裂缝检测是路面检测的重要组成部分。针对传统裂缝检测易受到路面纹理及光照条件等干扰因素影响问题,进行了线结构光三维检测系统在裂缝检测方面的应用研究。其中,针对当前路面三维形貌表示方法不能很好地分离实际路面中裂缝细节信号和道路本身复杂的波动信号,提出一种将轮廓线相对拟合直线的高度作为深度值拼接成裂缝深度图像的方法。这种方法很好地克服了获得路面数据中的路面复杂的波动信号影响,突出了裂缝信号,更有利于后续裂缝提取。     

Gas–Solid Erosive Wear of Biomimetic Pattern Surface Inspired from Plant

Gas–solid erosive wear is a phenomenon in which serious mechanical damage is caused by the impact of solid particles on a wall. In this study, we investigated the erosive wear characteristics and mechanism of biomimetic groove surfaces in gas–solid erosive wear. Orthogonal experimental results showed that the order of the factors that influenced the erosive wear of the biomimetic groove surface was morphology > space > feature size. The V-shaped groove surface exhibited the best erosive wear resistance over the smooth, square, and U-shaped groove surfaces. The surface microstrain calculated by X-ray diffraction lines was used to study the mechanism of erosive wear resistance enhancement of the biomimetic surface. The microstructure of the eroded surface was analyzed by scanning electron microscopy. The appearance of ribs on the biomimetic groove surface increased the erosive wear of the surface in a distal position with respect to the ribs themselves. This article shows more opportunities for bionic application in improving the anti-erosion performance of moving parts that work under dirt and sand particle environments.     

螺旋槽水润滑橡胶合金轴承沙粒侵蚀磨损研究

螺旋槽水润滑橡胶轴承橡胶面的破坏除了与运动轴直接接触造成磨损外,水中泥沙颗粒等污染物对水润滑橡胶的侵蚀磨损也是影响水润滑橡胶工作面的主要破坏方式。对液-固两相流中固体颗粒的受力情况进行分析;借助于Finne磨损基本数学模型,对不同转速、不同大小沙粒和水组成的流场进行分析,得到内部颗粒随液态流场变化的趋势;对中高转速下颗粒对壁面磨损的过程和位置进行初步的预测。结果表明,在不同转速下,不同粒子对橡胶面不同位置的侵蚀磨损是显著不同的:低转速下,接近层流状态时,对承载面的磨损较大;高转速下,流体呈现紊流状态,并在导流槽内出现独立的漩涡现象,导致颗粒在漩涡内聚集,从而造成漩涡面的磨损侵蚀较大。螺旋槽结构导致流场内漩涡出现,对于排污具有较好的作用。     

Single asperity abrasion of coated nodular cast iron

In the sliding contact between a rigid indenter and a softer deforming surface, a wear scar is formed in the softer surface. This wear scar consists of a groove flanked by two shoulders, where part of the material from the groove is transferred to these shoulders and the rest is actually removed from the surface as wear debris. The proportion of actually removed material is characterised by the degree of wear parameter ξ. This paper focusses on the experimental determination of the degree of wear for nodular cast iron with a range of hard surface layers. This is done using a pin-on-disk set-up that is located in the chamber of a scanning electron microscope. From these experimental results, an empirical model that describes the volumetric abrasive wear of coated nodular cast iron in single indenter scratching is developed.     

An automatic system based on vibratory analysis for cutting tool wear monitoring

The aim of this work is to develop a new, simple to use and reliable automatic method for detection and monitoring wear on the cutting tool. To achieve this purpose, the vibratory signatures produced during a turning process were measured by using a three-axis accelerometer. Then, the mean power analysis was proposed to extract an indicator parameter from the vibratory responses, to be able to describe the state of the cutting tool over its lifespan. Finally, an automatic detector was proposed to evaluate and monitor tool wear in real time. This detector is efficient, simple to operate in an industrial environment and does not require any protracted computing time.     

无缝钢管斜轧穿孔顶头表面缺陷非接触在线检测方法

针对无缝钢管斜轧穿孔顶头表面缺陷在线检测的现实需求，提出了一种基于激光扫描、空间点云数据处理及深度学习的非接触测量方法。根据无缝钢管产线特点确定了检测位置、系统构成和顶头轮廓数据采集方案，并引入迭代最近点（ICP）配准方法，实现了测量点云与标准CAD模型的配准。针对头部缺陷设计了相应的分类数集和渐变形态，使用点云深度学习方法实现了缺陷精确分类和量化预警。针对表面磨损缺陷，设置磨损深度上限阈值以实现磨损程度的精确监测。为验证系统可靠性，搭建了顶头检测物理模拟平台，并利用3D打印技术定制了含有不同缺陷特征的顶头实物模型。测试结果表明，表面轮廓检测误差在0.06 mm以内，头部缺陷分类精度可达97.7%、准确度可达98.1%，满足在线检测要求。     

基于LabVIEW和Mask R-CNN的柱塞式制动主缸内槽表面缺陷检测

为了解决传统图像处理方法对于铸铝材料表面缺陷检测通用性不高、准确度低等问题,研究了一种基于Mask R-CNN神经网络的缺陷检测系统。首先,采用自主研发的缺陷检测装置采集柱塞式制动主缸内槽表面图像,对其进行预处理,制作成Microsoft COCO格式数据集;其次,搭建适用于该数据集的Mask R-CNN神经网络结构,并绘制训练过程损失函数与平均精度均值曲线;最后,将检测结果与基于SVM和Faster R-CNN模型的检测结果进行比较,统计了3种神经网络模型的单图检测平均时间和识别率。试验结果表明,在相同样本条件下,该方法的识别率比另外2种方法高,达到了93.6%,能够更精确地检测柱塞式制动主缸内槽的表面缺陷。     

Influence of Surface Groove Width on Tribological Performance for Cylinder Liner–Piston Ring Components

Surface groove width is of great significance to the performance of the cylinder liner–piston ring (CLPR) with the different surface textures in marine diesel. However, little is understood about the specific application (e.g., the geometric parameters of surface textures and operating conditions) of surface texture in actual marine diesel engines. To address this issue, different surface groove textures including grooves structures with 1-, 2-, 3-, and 4-mm widths were designed based on previous results related to marine diesel engine applications. A series of experimental tests was conducted in a reciprocation tester, and data on the friction characteristics were obtained under different operating conditions. Comparative studies on the friction coefficients, worn surface features, and oil film characteristics were performed. Results showed that the 2-mm groove structure of the cylinder liner was more favorable for improving the wear performances at low speed, whereas a 3-mm groove structure of the cylinder liner was more suitable for improving the wear performance at higher speeds, though its wear performance needs to be improved under high load. These results help to understand the specific application of surface texture on the wear performance of the CLPR pair.     

不同条件下不同尼龙的摩擦磨损性能

分别制备了MC尼龙、含5%(质量分数)润滑油的MC油尼龙及35%(体积分数)碳纤维增强的复合MC尼龙,研究了3种尼龙在干摩擦、洁净水、干砂、水砂条件下的摩擦磨损性能。结果表明:MC油尼龙表面由于存在润滑油膜,4种条件下的摩擦因数和磨损率均最小;干摩擦和水润滑条件下,复合MC尼龙表面的纤维凸起使其磨损率和摩擦因数均较MC尼龙的小;水润滑下的尼龙磨损程度均较干摩擦下的小;干砂和水砂条件下,石英砂的犁削作用使MC尼龙表面出现较多犁沟,M C油尼龙表面由于存在润滑油膜,仅出现少量犁沟。水砂条件下的尼龙磨损程度均较干砂条件下的小。     

液压缸往复密封圈表面接触应力监测研究

在液压缸中，往复动密封圈表面接触应力是决定其密封有效性的关键，但由于在工作过程中对往复密封表面接触状态进行监测的难度很大，因此对其变化规律仍缺乏深入了解。针对这一问题，以液压缸活塞杆Y形密封圈为对象，通过有限元仿真分析密封圈内唇磨损对密封圈表面接触应力的影响，确定密封圈表面接触应力的最佳监测部位；采用光纤光栅传感器(FBG)进行密封槽表面接触应力监测试验，通过铺设于密封槽的FBG传感器采集应力数据，得出密封圈周向和轴向接触应力均随内唇磨损增加呈现先增大后减小的趋势，与仿真结果一致；接触应力对密封磨损程度变化的响应灵敏度会随密封压力的增加而增大。研究结果为液压缸实际运行过程中往复动密封状态的监测提供了依据。     

Sensor Fusion for the Detection of Landmines

The detection of buried anti-personnel mines (APMs) is widely considered as a problem which may only be solved with a combination of two or more complementary sensors. We present processing and fusion results obtained from a multisensor data set, acquired with a pulse induction metal detector (MD), a pulsed ultra wide band ground penetrating radar (GPR) and a 3–5 thermal infra-red (IR) camera. Various types of soils, clutter objects and burial conditions were recorded. Anti-personnel mines included minimum metal mines as well as mines with a significant metal content. We use a special projection to map a 3D GPR data cube, with time or depth as vertical co-ordinate, into a horizontal plane view 2D image. Object contours are then derived, based on an edge extraction method, followed by an automatic detection of circular shapes with a Hough-transform. In the association step, the stand-off IR image, the metal detector and GPR images and related detections are mapped onto a common cartesian grid on the ground surface. Detection results are fused on a decision level, using a Bayesian approach. Our results indicate that the GPR performance approximately matches that of the metal detector. With both sensors all metallic mines and around 60% of the minimum metal mines were detected. In the case of two false alarms per square meter combined detection probability clearly exceeds single sensor performance.