提出用两个二自由度并联机构相串联而成的混联机构作为主进给机构,外加一维运动的工作平台使之能实现五坐标数控加工的一种新型混联机床.其具有结构简单、易于控制的特点.对其机构进行了描述,应用螺旋理论分析了球形五杆机构的运动螺旋及其反螺旋,在此基础上求得其自由度;分析了该混联机床封闭的运动学正反解,用数值方法对其正确性进行了验证;并在此基础上以Visual C 为平台,利用OpenGL对其运动进行了仿真,对设计思想及设计细节进行了验证,为样机制造提供了可靠保证. 相似文献
computing devices such as Turing machines resolve the dilemma between the necessary finitude of effective procedures and the potential infinity of a function's domain by distinguishing between a finite-state processing part, defined over finitely many representation types, and a memory sufficiently large to contain representation tokens for any of the function's arguments and values. Connectionist networks have been shown to be (at least) Turing-equivalent if provided with infinitely many nodes or infinite-precision activation values and weights. Physical computation, however, is necessarily finite.
The notion of a processing-memory system is introduced to discuss physical computing systems. Constitutive for a processing-memory system is that its causal structure supports the functional distinction between processing part and memory necessary for employing a type-token distinction for representations, which in turn allows for representations to be the objects of computational manipulation. Moreover, the processing part realized by such systems provides a criterion of identity for the function computed as well as helps to define competence and performance of a processing-memory system.
Networks, on the other hand, collapse the functional distinction between processing part and memory. Since preservation of this distinction is necessary for employing a type-token distinction for representation, connectionist information processing does not consist in the computational manipulation of representations. Moreover, since we no longer have a criterion of identity for the function processed other than the behaviour of the network itself, we are left without a competence-performance distinction for connectionist networks, 相似文献