Kinematic Synthesis And Application Of Planar Linkages Based On Task Positions

Planar single degree of freedom series mechanism is simple in structure, and has a variety of structural forms and characteristics, which could cope with a variety of task requirements. The main purpose of this paper is to improve the design method of single-degree-freedom planar series mechanism.Firstly, a comprehensive method of planar single-degree-freedom multi-bar mechanism is studied in this paper. It is needed to design a multi-degree-of-freedom series bar with all the positions of the task in the method,and then to add the restraint degree series. The problem is that it is necessary to empirically select the length of the rod of the initial rod at the beginning.In this paper, the method of selecting the length of the pole is improved. Based on the principle of machine learning and the analysis and comparison of the actual situation, the machine learning theory is discussed,which can select the appropriate model and function, and use the data in the database to train the model till the final length of the rod can be optimized by the coordinates of a given task position. It can be avoided to choose unsuitable length of the rod, and no response of the latter calculation in order to save the time of designing.Then, Java and matlab are used to build the MVC architecture, which make the calculation interface and automation approached, and all the data are stored in the local MySQL database. Also, the software to synthesize the planar single-degree-freedom four-bar is written by using Java and matlab to provide more choices for practical project designing.Finally, combining the theory and practice, the designing and calculation of the flip-flop mechanism and the piping paint mechanism are accomplished by using the method of planar four-bar mechanism synthesis and multi-bar mechanism synthesis. The solid modeling is carried out in Solidworks, the kinetic analysis of each designed institution is been calculated in Adams combining with practical requirements to obtain the optimal mechanism. In the design of the organization, there are many initial values are needed to be preset in many situations, and to be modified according to the feedback. The idea of using machine learning to optimize the selection of initial values can be used in many cases.