Local toolpath smoothing for five-axis machine tools

When five axis CNC machine tools follow series linear toolpath segments, the drives experience velocity, acceleration and jerk discontinuities at the block transition points. The discontinuities result in fluctuations on machine tool motions which lead to poor surface quality. This paper proposes to insert quintic and septic micro-splines for the tool tip and tool-orientation, respectively, at the adjacent linear toolpath segments. Optimal control points are calculated for position and orientation splines to achieve C³ continuity at the junctions while respecting user-defined tolerance limits. The geometrically smoothed corners are traveled at a smoothly varying feed with cubic acceleration trajectory profile. The proposed method is experimentally demonstrated to show improvements in motion smoothness and tracking accuracy in five-axis machining of free-form surfaces found in dies, molds and aerospace parts.     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

参数化图形库技术

本文论述了参数化图形库的实现原理，建立了以尺寸叠加算法为基础的参数化图形库开发工具，利用这种开发工具建立的图形数据可以由数据库管理，方便有效。     

涡轮钻具在高陡构造的应用及展望

介绍了涡轮钻具在秋参1井的应用情况,详细叙述了涡轮钻具在山前高陡构造快速钻进及防斜打直方面的优点,与常规钻井相比,机械钻速明显提高,钻井周期明显缩短,钻井成本明显下降,经济效益显著。     

用仿真工具优化车辆动力系统(一)

在本文的第一部分，介绍了一种理想的车辆动力系统的计算机辅助开发过程。它显示了现代仿真技术在产品持续发展过程中的作用，该技术采用硬件回路作非在线的仿真，并通过现代化的试验手段对车辆进行匹配。特别重要的是提供了将电控变速器和发动机控制单元综合的可能性。接着是在线仿真在诸如发动机和变速器模式方面的应用和发展，它是上述开发过程的基础。根据上述的方法论，本文论述了仿真技术已取得的成绩和将来的应用可能性。     

石油测井仪器可靠性指标探讨

在分析常用可靠性指标及其应用现状的基础上，根据可靠性理论及石油测井仪器的特点。提出了石油测井仪器的可靠性模型。在探讨常用可靠性指标对石油测井仪器适用性的同时，提出将平衡稳定工作时间NWTURE和免维修测井井次NOLWNM作为石油测井仪器可靠性指标的思想；探讨了获取可靠性指标的途径。给出了推荐使用的石油测井仪器可靠性指标。     

液压集成块校核软件的编制和应用

本文叙述了液压集成块校核软件的编制思想和方法，并重点介绍了编制过程中所使用的相关技术，同时给出部分源程序。     

电潜泵大修打捞配套工具研制

随着采液时间的延长,电潜泵井事故不断增加,仅辽河油田每年就有30口左右的电泵井需要转大修.由于没有专用的打捞工具,作业设备的能力有限,其处理事故的周期往往较长,效果不理想.因此,在已有φ139.7mm套管内电泵打捞工具的基础上,研制开发了φ177.8 mm套管内电泵打捞工具,解决了油田电泵打捞的难题.