High Efficiency Axial Deep Creep-Feed Grinding Machining Technology of Engineering Ceramics Materials

Axial deep creep-feed grinding machining technology is a high efficiency process method of engineering ceramics materials, which is an original method to process the cylindrical ceramics materials or hole along its axis. The analysis of axial force and edge fracture proved the cutting thickness and feed rate could be more than 5-10 mm and 200 mm/min respectively in once process, and realized high efficiency, low-cost process of engineering ceramics materials. Compared with high speed-deep grinding machining, this method is also a high efficiency machining technology of engineering ceramics materials as well as with low cost. In addition, removal mechanism analyses showed that both median/radial cracks and lateral cracks appeared in the part to be removed, and the processed part is seldom destroyed, only by adjusting the axial force to control the length of transverse cracks.

Development of twin wheel creep-feed grinding machine using continuous dressing for machining of aircraft rotary wing

The purpose of this study is to develop a twin wheel creep-feed grinding machine using continuous dressing to machine precise axisymmetric turbine blades that have been difficult to machine using a conventional creep-feed machine.In order to develop such a machine,3D-modeling and machine simulations were performed and a twin wheel creep-feed grinding machine was manufactured.Furthermore,the axisymmetric precision of the machined workpieces through practical machining was evaluated and the quality of the continuous dressing effect of the developed machine was established.In addition,experimental considerations for a proper dresser-to-wheel speed ratio and proper feed rate of the dresser were carried out.As a result,a twin wheel creep-feed grinding machine with continuous dressing is developed through machine simulation,manufacturing and performance evaluation.Optimum condition for the dresser feed rate is 0.3μm/rev.In cases of large dressor-to-wheel speed ratio,grinding efficiency can be enhanced,but the surface roughness shows a conflicting trend.Developed twin wheel creep-feed grinding machine has satisfactory appraisal with regard to surface roughness,flatness,and parallelism.Satisfactory surface roughness below 0.1μm can be obtained for the blade of aircraft.However,in order to perform precise machining,it is necessary to improve the structure of the twin wheel creep-feed grinding machine.

INVESTIGATION OF SURFACE TEMPERATURE IN HIGH-EFFICIENCY DEEP GRINDING

A new thermal model with triangular heat flux distribution is given in high-efficiency deep grinding. The mathematical expressions are driven to calculate the surface temperature. The transient behavior of the maximum temperature on contact area is investigated in different grinding conditions with a J-type thermocouple. The maximum contact temperatures measured in different conditions are found to be between 1 000 ℃ and 1 500 ℃ in burn-out conditions. The experiment results show good agreement with the new thermal model.

Applications of High-Efficiency Abrasive Process with CBN Grinding Wheel

High-efficiency abrasive process with CBN grinding wheel is one of the important techniques of advanced manufacture. Combined with raw and finishing machining, it can attain high material removal rate like turning, milling and planning. The difficult-to-grinding materials can also be ground by means of this method with high performance. In the present paper, development status and latest progresses on high-efficiency abrasive machining technologies with CBN grinding wheel relate to high speed and super-high speed grinding, quick point-grinding, high efficiency deep-cut grinding, creep feed deep grinding, heavy-duty snagging and abrasive belt grinding were summarized. The efficiency and parameters range of these abrasive machining processes were compared. The key technologies of high efficiency abrasive machining, including grinding wheel, spindle and bearing, grinder, coolant supplying, installation and orientation of wheel and workpiece and safety defended, as well as intelligent monitor and NC grinding were investigated. It is concluded that high efficiency abrasive machining is a promising technology in the future.

耳后磨骨槽技术在脑深部电刺激术中的应用

脑深部电刺激术已广泛应用于帕金森病、特发性震颤、肌张力障碍、难治性癫痫等神经精神疾病,操作步骤复杂,临床应用易引起一定的并发症如颅内出血、感染等手术并发症,电极断裂、电极移位等硬件并发症,以及感觉异常、肌肉痉挛等刺激并发症,其中电极断裂、皮肤破溃感染是常见并发症且主要位于耳后。首都医科大学附属北京天坛医院于2012年将耳后磨骨槽技术应用于临床,显著降低耳后电极断裂和皮肤破溃感染的风险。本文重点介绍耳后磨骨槽技术及其临床应用注意事项,供同道参考借鉴。

基于综合熵权-信噪比的球轴承外圈沟道ELID磨削加工参数优化

为提高球轴承外圈沟道表面的加工质量,实现对电解在线砂轮修整ELID磨削加工试验中摆动参数、磨削参数的精确控制,设计正交试验并基于试验数据建立表面质量与加工参数之间的二阶响应模型和指数回归模型;通过熵权理论确定表面粗糙度与轮廓度的综合权重,最终获得最优加工参数,并结合信噪比的基本特性分析加工参数对表面质量的影响程度。结果表明:指数回归模型对表面粗糙度预测较为精确,二阶响应模型对轮廓度预测较为精确,误差均在5%以下;表面粗糙度、轮廓度综合权重分别为0.4568,0.5432,最优加工参数为摆动幅度11°、摆动速度6°/s、砂轮转速19500 r/min、工件转速50 r/min;加工参数的影响程度由大到小依次为摆动幅度、摆动速度、工件转速、砂轮转速。

基于改进YOLOv5的焊缝识别算法研究

针对机器人自适应打磨焊缝的问题,文章提出一种基于YOLOv5改进的焊缝检测算法,实现焊缝的识别和初定位。使用焊接机器人制作各类不同参数和形貌的焊缝,自制一个包含3 996张焊缝图像的数据集用来深度学习。选用YOLOv5s模型进行训练,在Backbone中添加了GAM注意力机制模块;同时引入GhostNet,用GhostConv模块和C3Ghost模块替换原模型的Conv模块和C3模块。改进后的YOLOv5s-GhostNet-GAM模型的mAP@0.5达到了90.21%,相比原YOLOv5s模型提高了4.05%,同时参数量减少了5.64%,FLOPs降低了27.44%,检测速率为23.47 FPS,达到了机器人自适应打磨焊缝对识别精度与后期软件部署的要求。

基于深度学习的镍基高温合金叶片磨削烧伤识别研究

针对目视检查镍基高温合金叶片磨削烧伤时易出现误检、漏检等问题,提出一种基于深度学习的镍基高温合金叶片磨削烧伤识别分类模型(Tenon Grinding Burn Net,TenonG-BNet).以K4125镍基高温合金叶片为研究对象,通过磨削烧伤试验和试件组织检测获得不同烧伤程度的叶片榫齿磨削烧伤分级标准和对应的图片集;将ODConv动态卷积融合Inception V2模块和Coordinate Attention注意力机制,保证模型轻量化的同时提高模型的特征提取能力;使用全连接层进行识别分类.结果表明,与其他4个经典分类模型相比,TenonGBNet在具有较小的模型复杂度和参数量的同时保持了96.50%的平均分类准确率,且各烧伤等级的分类准确率均超过95%.

稀缺炼焦中煤精准分选-深度脱灰提质试验研究

针对我国炼焦中煤的资源现状,总结了炼焦洗中煤的破碎再选方法和研究现状;以某稀缺炼焦洗中煤为研究对象,根据洗中煤灰分含量、灰分矿物赋存等特性,进行精准分选-深度降灰试验研究。研究结果表明:“细碎-重选联合细磨-深度浮选”为最佳流程方案,浮选采用“一粗-二扫-三精”的闭路试验流程,最终得到了粗粒级重选精煤产品、细粒级浮选精煤产品和高灰尾泥(细粒级浮选尾矿)3种产品,2种精煤产品灰分在11.5%以内,尾泥灰分在74%以上,获得了较大的精煤回收率,经济效益显著,研究结果为稀缺中煤的提质再利用提供了参考和技术支持。

基于选矿生产数据的矿石可磨性智能识别算法

本文以在线识别矿石可磨度为研究目标,通过详细分析磨矿过程参数间的相互影响关系,得出磨矿过程运行指标的变化能从一定程度上反应矿石可磨度变化的结论。针对工业数据维度高、耦合性强等问题,在着重研究了KPLSR和SAE两种可提取关键特征算法机理的基础上,以实际采集的磨矿过程数据为样本,对多算法进行横纵对比,最终验证了深度学习算法SAE在工业复杂数据分析中的优势。并以测试试验中性能最佳的算法结构作为矿石可磨性智能识别系统的在线识别模型,该模型在工业化应用中绝对值误差平均可达0.61 kW·h/t,平均误差率约为4.79%,可满足现场生产的需求。

基于磨-浮联合技术的无烟煤深度脱灰工艺探讨

以焦作某矿区无烟煤为研究对象,分析其矿物颗粒与煤颗粒的附着状态,研究磨矿时间对煤样粒径组成和矿物解离的影响,进而对浮选药剂制度与工艺条件进行优化,最终实现深度脱灰。实验结果表明当研磨时间为70 s时,所得煤样产品的D[4,3]和D[90]达到最小值,分别为21.61、52.37μm,实现煤与矿物的充分单体解离。通过对浮选药剂和工艺制度的优化,确定最佳单因素条件为:捕收剂用量1 500 g/t,起泡剂用量为800 g/t,抑制剂六偏磷酸钠500 g/t。最终在闭路实验中采用最佳浮选工艺流程,1次粗选4次精选,将原煤灰分由10.84%降至1.89%,产率为18.59%。

基于改进VGG-16深度学习网络的防护面罩佩戴识别

为高效识别打磨焊接作业人员是否佩戴防护面罩,提出了改进VGG-16的深度学习模型,构建了基于VGG-16的深度特征提取网络挖掘图像的重要信息。为解决VGG-16网络对图像局部特征和全局结构信息捕捉的不足,建立基于坐标注意力的空间位置信息感知机制,增强对图像位置和通道信息的关注。最后,建立基于多层全连接层的分类网络输出识别结果。实验表明,该模型对打磨焊接作业人员是否佩戴防护面罩的识别准确率、精确率、召回率和F1分数分别达到95.88%、96.48%、95.25%和95.86%,具有比传统人工巡检方法更好的效果。

Fretting wear behaviour of machined layer of nickel-based superalloy produced by creep-feed profile grinding

Fretting wear has an adverse impact on the fatigue life of turbine blade roots.The current work is to comparatively investigate the fretting wear behaviour of the nickel-based superalloy surfaces produced by polishing and creep-feed profile grinding,respectively,in terms of surface/subsurface fretting damage,the friction coefficient,wear volume and wear rate.Experimental results show that the granulated tribolayer aggravates the workpiece wear,while the flat compacted tribolayer enhances the wear resistance ability of workpiece,irrespective of whether the workpiece is processed by polishing or grinding.However,the wear behaviors of tribolayers are different.For the polished surface,when the normal load exceeds 100 N,the main defects are crack,rupture,delamination and peeling of workpiece materials;the wear mechanism changes from severe oxidative wear to fatigue wear and abrasive wear when the loads increase from 50 to 180 N.As for the ground surface,the main wear mechanism is abrasive wear.Particularly,the ground surface possesses better wear-resistant ability than the polished surface because the former has the lower values in coefficient friction(0.23),wear volume(0.06×10^(6)μm^(3))and wear rate(0.25×10^(-16)Pa^(-1)).Finally,an illustration is given to characterize the evolution of wear debris on such nickel-based superalloy on the ground surface.

深度学习在选矿领域的应用进展

深度学习作为一门新兴技术,可以很好地处理非线性问题以及排除由主观经验导致的稳定性低等问题,已成为选矿领域的研究热点。阐述了基于计算机图像处理技术的深度学习原理,综述了深度学习在矿物识别、碎矿和磨矿、浮选、重选、磁选等方面的研究进展,总结了其优势和不足,并对未来的研究方向提出了若干建议。

四川某磁铁矿矿石改扩建选矿流程中细筛工艺的应用

四川某铁矿石主要有用矿物为磁铁矿。随着矿石向深部开采,现有工艺流程已不适于其深部矿石,在对深部矿石进行选矿工艺实验研究时,引入细筛工艺,大大降低了第二段磨矿的细度,降低其能耗。研究结果表明,采用干式磁选预选-二段磨矿二段磁选-磁选精矿细筛的选矿工艺流程,可获得全铁品位60.24%、全铁回收率88.67%的铁精矿。

基于深度强化学习算法的机器人浮动打磨执行装置研究

为实现机器人恒力打磨的需求,文章设计了浮动打磨执行器,进行了打磨控制算法研究和浮动打磨执行器的结构设计,并对浮动打磨执行器系统进行受力分析和动力学建模。在传统PID控制算法的基础上,采用DDPG深度强化学习算法进行PID控制参数的整定,并开展浮动打磨执行器恒力性能实验验证。实验结果表明,文章设计的浮动打磨执行器能够满足恒力控制的要求。通过DDPG深度强化学习算法对PID控制参数整定,减少了繁琐的调参步骤,且具有更好的恒力控制性能。

双向水眼自动换向磨鞋的研制与应用效果评价

川渝地区页岩气开发向深层迈进,连续油管作业在长水平段下入过程中容易发生螺旋锁定、钻压无法传递等问题,严重制约了连续油管在深层页岩气井中的应用。传统磨鞋采用正向或者反向单一水眼设计,如果要切换磨鞋水眼方向将起下管串增加时效。双向水眼自动换向磨鞋,可通过触障自动切换流道,集反向水眼磨鞋与正向水眼磨鞋的优点于一体。反向水眼可提供向前拖拽力,增加碎屑运移能力,正向水眼能保证桥塞的正常钻磨,有效解决螺旋锁定与钻压传递的问题,提高施工效率。现场试验结果表明,双向水眼自动换向磨鞋不仅能够延伸下入深度,提高作业效率,还能够增加碎屑运移、减少卡钻风险。

大跨度中厚板边缘视觉识别与打磨引导研究

中厚板切割自由边打磨质量影响后续焊接等工艺性能,机器人打磨的高效一致性能很好地保证后续工艺要求,对大跨度中厚板的边缘打磨提出了视觉图像识别引导的方法。首先对随机尺寸或形状大跨度中厚板进行远视场全局图像采集,利用基于HED融合算法的全局图像边缘稳定识别,然后基于全局边缘路径引导,对局部近视场边缘3D点云图像采集分析并提取边缘点集,最后根据工艺进行参数调整后进行定位引导动态打磨去毛刺测试。结果表明:打磨深度和打磨宽度均符合打磨要求,并且局部的点云检测可以有效进行边缘异物实时检测,避免设备异常碰撞损坏。对于难以精准定位大跨度大重量甚至自由边不规则的多类型中厚板的机器人打磨,以上方法可以极大提高打磨效率以及保证边缘打磨效果的一致性,极好地满足后续焊接工艺的需求。

云南某煤气化煤泥细磨浮选试验研究

目前我国年产大量的煤气化煤泥,高灰高含水率的特性导致其难以被直接利用,因而对其采取大部分填埋处理,由此产生严重的环境污染及资源浪费。拟对灰分含量约36%的云南某煤气化煤泥进行细磨-深度浮选研究,分别探究磨矿细度、捕收剂种类及用量、抑制剂种类及用量、浮选浓度的影响规律,并采用复配抑制剂D503以及新型捕收剂M602进行闭路浮选实验。在流程结构试验的基础上采用“细磨-深度浮选”技术对煤气化煤泥进行全流程闭路试验,分选得到的精煤产品以及尾泥的灰分为11.94%、97.29%,产率分别达到72.85%、27.15%,实现了煤气化煤泥中残碳和灰质较为彻底的分选。富集所得的精煤产品可作为喷吹煤或用作原料返还气化工艺,尾泥可作搅拌站的原料,从而为煤气化煤泥的全资源化及增值化利用提供技术支撑。

小模数齿轮的缓进深切成形磨削实验

针对高精度齿轮加工工艺路线长的问题,提出小模数齿轮缓进深切成形磨削方法并开展磨削实验,分析不同进给速度对磨削功率、工件磨削烧伤、砂轮磨损等的影响规律,且采用复刻法研究砂轮的磨损过程。结果表明:磨削功率峰值随进给速度的增加而增大;当进给速度超过150 mm/min时,工件表面发生磨削烧伤,其磨削表面的硬度明显高于工件初始表面的硬度,且工件表层发生了马氏体相变;距离磨削表面的深度增加,其硬度值均呈下降趋势,且齿底位置的硬化层深度小于齿廓两侧的硬化层深度;砂轮的磨损过程分为初期磨损、稳定磨损和快速磨损阶段3个阶段,其中稳定磨损阶段的材料去除体积约为2000 mm^(3),过大的进给速度将引起砂轮的严重磨损。