基于多粒度共进化功能推理的机械运动方案设计新方法

NEW APPROACH TO MECHANICAL KINEMATIC SCHEME GENERATION BASED ON MULTI-GRANULARITY CO-EVOLUTIONARY FUNCTION REASONING

提出了基于多粒度共进化功能推理的机械运动方案设计方法。首先分析了共进化功能推理模型和多粒度设 计模型的特点,并结合二者的优点构造了一种多粒度的共进化功能推理模型:然后将该推理模型应用于机械运动 方案设计,提出了一种面向机械运动方案设计的共进化功能推理方法,该方法采用分类功能来描述机构单元及机 械系统的运动特征信息,将机构单元和机械系统都采用运动功能变换函数进行抽象表达,通过功能推理来生成机 械运动变换单元的串联组合方案:随即给出了相应的功能推理算法流程,通过与已有算法的比较详细分析了该算 法的特性,并讨论说明了该算法所具有的效率高、可精确描述运动功能变换特性等优点;最后通过电线进给机构 运动方案设计实例验证了该方法的有效性。

A new approach to mechanical kinematic scheme generation based on multi-granularity co-evolutionary function reasoning is proposed. Firstly, features of co-evolutionary function reasoning model and multi-granularity design model are investigated, then a multi-granularity co-evolutionary function reasoning model is constructed by integrating their strong-point. Such a model is applied into the kinematic scheme generation and a co-evolutionary algorithm for kinematic scheme generation is presented, in which classified functions are used to describe the kinematic characters of mechanical system and mechanisms, each mechanism and mechanical system is expressed by a motion-transforming function abstractly and series-connected mechanical kinematic schemes are generated based on function reasoning. Next, detailed flow of function reasoning algorithm is provided and the characteristics of the algorithm are analyzed by comparing with those of existed algorithms. Further, the advantages of the algorithm, such as high computing efficiency, high precision in describing motion transformation, are discussed. Finally, kinematic scheme generation of a wire-feeding mechanism is used to verify the effectiveness of the new approach.