智能材料在微机械中的应用及发展

论述了微机械的发展现状,综述了硅、形状记忆合金、压电陶瓷、超磁致伸缩等智能材料在微型机械中的最新应用及发展趋势。提出了智能材料结构在微型机械中应用的几个关键问题。     

CAM后置处理研究

分析了数控加工后置处理技术的特征、面临的问题和当前的发展趋势,介绍了应用通用后置处理器开发定制专用后置处理器的实践。     

机械加工切削用量的优化

本文探讨了机械加工中,切削用量优化目标函数,优化数学模型,优化方法步骤。以期用该模式取代凭经验选定切削用量的方法,从而提高经济效益。     

机床实体模型复模态空间动位移显示技术的研究

介绍了用实体模型在复模态空间显示机床整机动位移的原理和方法。并在VC++6.0开发平台上用OpenGL语言开发了相应软件。解决了以往同类软件动画效果不能真实地表现实际机床整机的动态位移情况的难题。     

云制造环境下机床装备资源选择方法

针对云制造环境下机床装备资源具有数量大、异质异构、服务制约因素多等问题，提出了基于多准则决策的机床装备资源选择方法。首先，利用本体论对机床装备资源建模，将物理资源虚拟化封装，实现虚拟资源与物理资源的逻辑映射。进一步地，使用语言变量量化决策者评估信息，采用模糊决策与试验评价实验室（FDEMATEL）和熵值法（EW）相结合的组合赋权法，引入模糊VIKOR（FVIKOR）多属性决策方法对机床装备资源进行综合评估，建立FDEMATEL-EW-FVIKOR的多准则混合决策模型。最后，将该方法应用于某生产制造企业，实验对比决策结果验证了多准则混合决策模型的可行性和有效性。     

Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

A drilling tool design and in situ identification of planetary regolith mechanical parameters

The physical and mechanical properties as well as the heat flux of regolith are critical evidence in the study of planetary origin and evolution. Moreover, the mechanical properties of planetary regolith have great value for guiding future human planetary activities. For planetary subsurface exploration, an inchworm boring robot (IBR) has been proposed to penetrate the regolith, and the mechanical properties of the regolith are expected to be simultaneously investigated during the penetration process using the drilling tool on the IBR. This paper provides a preliminary study of an in situ method for measuring planetary regolith mechanical parameters using a drilling tool on a test bed. A conical-screw drilling tool was designed, and its drilling load characteristics were experimentally analyzed. Based on the drilling tool-regolith interaction model, two identification methods for determining the planetary regolith bearing and shearing parameters are proposed. The bearing and shearing parameters of lunar regolith simulant were successfully determined according to the pressure-sinkage tests and shear tests conducted on the test bed. The effects of the operating parameters on the identification results were also analyzed. The results indicate a feasible scheme for future planetary subsurface exploration.     

固体火箭发动机专用ICT机械扫描系统

以成熟的组合精密镗床为本体, 研制了固体发动机专用ＩＣＴ机械扫描系统。采用４５０ ｋＶ Ｘ射线源, 设计了准直器系统、探测器系统、机械扫描系统（传动控制系统、夹具及同步台）, 实测证明扫描系统符合精度要求     

人工神经网络在CAPP系统开发工具中的应用

探讨了基于神经网络理论在ＣＡＰＰ系统开发工具的应用;建立了工艺过程设计系统的ＡＮＮ模型;详细讨论了工艺过程设计系统的推理机制,并给出了用集成ＢＰ网络选择加工方法及通过改进Ｈｏｐｆｉｅｌｄ网络确定工艺路线的过程。     

超精密CNC平面磨床在线测量、补偿加工及对刀功能的自动化实现

介绍了大型超精密ＣＮＣ平面磨床测量控制系统设计中多传感器在线检测、导轨误差补偿加工以及超精密磨削对刀等特殊功能的实现方法,并将其纳入ＣＮＣ的Ｍ功能指令系统,实现了超精密平面磨削自动化