Research On Mounting Optimization Of Surface Mount Technology Production Line

The placement of electronic components is a key process in SMT(Surface Mount Technology)production line,and shortening the placement path is an important way to improve the mounting efficiency.The placement head of the surface mounting machine generally needs to move back and forth between the feeder,PCB(Printed Circuit Board)and the nozzle station,so the placement path is closely related to the component placement sequence,feeder position,and nozzle replacement.Taking the arch-type placement machine as the object,this paper systematically studies the placement path optimization problem around component placement sequence optimization and feeder assignment optimization.The SMT surface mount production process and component placement process flow are analyzed.The arch-type placement machine placement process and the movement of the placement head are studied.The main factors affecting the placement efficiency are analyzed,and the research content and research ideas are defined.Then,the optimization of component placement sequence is studied.The difference in placement path between single placement head and multiple placement heads are analyzed,and a multi-head placement machine placement sequence optimization model is developed.And then,the coding problem of placing sequence coding and automatic optimization of chromosome part is studied,and genetic algorithm is designed and improved to solve the model.Simulation experiments were carried out to verify the effectiveness of the algorithm,and the placement paths before and after the optimization of the placement sequence were compared and analyzed.Finally,under the condition that the placement sequence of components is known,the feeder assignment optimization problem is further studied.In this paper,the feeder assignment problem is regarded as a multi-stage assignment problem,and the feeder assignment optimization model is constructed,and then the solution algorithm is designed based on the dynamic programming.Simulation experiments were also carried out to demonstrate the proposed algorithm and analyze the placement paths before and after the optimization of the feeder.