复杂零件产品虚拟开发技术研究

本文针对复杂零件产品制造精度要求高,工艺性差,残次品率高,生产周期长等特点,在探讨虚拟产品开发技术及其原型系统的基础上,提出了一种复杂零件产品开发的新模式：即在虚拟状态下对产品进行构思、设计、分析、仿真、装配、测试、性能及经济性评估、快速原型及产品数据管理等。     

气相苯加氢低Ni/改性Al2O3催化剂性能评价

通过对γ－Ａｌ２Ｏ３载体进行化学改性处理,把Ｎｉ的含量降低（在５％以内）制得气相苯加氢低Ｎｉ／改性Ａｌ２Ｏ３催化剂。考察反应条件对其催化活性影响,并对催化剂的活性稳定性、耐热性、抗毒物能力进行评价。结果表明：该催化剂的性能良好     

坦克火控系统的自适应滤波与自适应解命中

在非制导火控系统中,为实现目标信息的自适应滤波与自适应解命中问题,机动目标模型应能动态识别,又要适应工作周期的各种变化.本文提出的“参数辨识模型”能同时满足上述要求,并可将自适应滤波纳入到现代控制理论的正常思路内予以解决.     

UIMS中的虚拟窗口及实现

为了解决屏幕上安排功能的面积不能满足应用要求的问题,本文提出了虚拟窗口的概念,并简单地介绍了在UIMS中实现虚拟窗口的原理,最后以一个自行开发的用户界面管理系统┅Fastool为例,较为详细地说明了如何在UIMS中增加虚拟窗口的功能。将Fastool应用到实际开发中的结果表明,虚拟窗口能提高系统用户的工作效率,同时也使系统的界面产生了较好的视觉效果。     

运用速度增益制导实现对目标卫星的拦截

本文对运用速度增益制导实现对目标卫星的拦截方法进行了研究。其特点是在拦截器运动参数初值及目标轨道参数给定的条件下,首先确定考虑地球扁率J_2项影响下的拦截目标的预测命中点,然后采用Lambert方法确定给定碰撞时间下拦截器的需要速度,最后实现对目标的拦截。用该方法可保证向未制导转换时有较高精度的转换条件。     

金属铝弹性常数从头计算的一种新方法

本文报导用原子簇模型计算金属铝的弹性常数。具有面心立方结构的Al_(13)原子簇在不同晶格常数下总能由多重散射兄方法(MS—X_a)算出。采用Morse势函数对原子簇总能进行最小二乘法拟合,然后用拟合出的势参数算出金属铝的弹性常数。我们的计算结果与其他理论计算值及实验值符合甚好。     

带有大质量刚体圆锥壳的自由振动分析

本文针对壳体带有大质量刚体的特点,用有限元离散方法,导出了在不同周向波数下壳元节圆位移与刚体质心位移及转角之间的关系式,并将结果与实际运动进行对比分析,论证了结论的正确性。为带有大质量刚体的壳体结构的动力分析提供了理论依据。     

后勤指挥管理自动化系统建设中突破口的选择方法

本文论述了后勤指挥管理自动化系统建设中突破口的3种选择方法,即ABC法、“瓶颈”法和效益扩展法,并且分析了这些方法的应用条件和注意事项。     

康特洛维奇的数值方法在矩形薄板横向弯曲振动中的应用

本文提出的解决矩形薄板弯曲振动问题的康特洛维奇的数值方法,大大减少了所需要的计算机内存。通过算例表明它具有比较高的计算精度和计算速度,是一种有前途的计算方法。     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001