Research On Error Analysis And Terminal Posture Prediction Of Automatic Solder Paste Printing Equipment

Surface Mounted Technology(SMT)is one of the key and basic industries of the electronic information industry.With the development of portable,small and ultra-thin electronic products,SMT has been endowed with increasingly high requirements.Solder paste printing process,as one of the key processes of SMT,exerts a decisive impact on the quality of SMT products.It is mainly completed by automatic solder paste printing equipment.Since the 21 st century,China's SMT technology has developed rapidly,but there is still a certain gap in the field of automatic solder paste printing equipment compared with foreign brands.China's solder paste printing equipment gradually become high-end,so the development of high-precision equipment is an urgent problem in the SMT industry,and it is also an important direction to improve the core competitiveness of China's information industry.In this paper,the automatic solder paste printing equipment is taken as the research object.In order to improve the performance of the equipment,a multi-objective prediction method based on integrated regression prediction chain is proposed for the automatic solder paste printing equipment to predict the terminal posture and posture of the platform.The main research contents of this paper are as follows:Firstly,the motion model of the automatic solder paste printing equipment is established;the kinematics of the 3-dof parallel alignment platform is analyzed;the positive solution model and the inverse solution model are obtained,and the singular configuration of the platform are studied.Based on the definition of the motion posture,the algorithm of the counterpoint platform is solved,and the validity of the algorithm is verified by the simulation experiment.This paper analyzes the influence of the input parameters of the alignment platform and the manufacturing and assembly errors on the precision of the equipment,and summarizes the other errors of the automatic solder paste printing equipment.Then,the machine learning algorithms are used to forecast the alignment platform terminal pose of the automatic solder paste printing equipment.Based on the prediction algorithm of single-objective Extreme Gradient Boosting,integrated multi-objective prediction algorithm of multi-objective return chain XGB-ERC(XGBoost Ensemble of Regressor Chains)is proposed,and this method is verified by comparison experiments has a better prediction effect on the related terminal poses.Finally,the automatic solder paste printing equipment is adjusted,and the data are collected under the normal working condition of the solder paste printing equipment.On the basis of real-time working data of the equipment,the XGB-ERC method is compared with the traditional machine learning method to further verify the effectiveness of this method.A simulation experiment is conducted to compensate the printing process of solder paste based on the alignment platform terminal pose predicted by XGB-ERC method.It has been verified that the method proposed in this paper can reduce the error of automatic solder paste printing equipment,thus improving the printing performance of the equipment,which has an important reference value for the development of SMT industry.