Research On Pattern Preparation And Defect Detection Based On Aerosol Inkjet Printing

Aerosol jet print(AJP)is a 3D inkjet printing technology emerging in recent years.It has attracted wide attention in materials,new energy sources,flexible electronics and other fields because of its advantages of high printing accuracy,low maintenance cost and low ink viscosity requirements.It has high research prospects and application value.However,the forming process of aerosol inkjet printing technology is complex and the process parameters are numerous.At present,researchers at home and abroad pay more attention to the research and application of materials.It is a key problem to reveal the printing process rules and the detection and analysis of printing defects,which needs to be studied and solved urgently at present and in the future.This paper focuses on theoretical analysis,experimental study of process parameters and defect detection.The aerosol inkjet printing technology platform assembled by ourselves is used as the experimental device to carry out research work.The main work of this paper is as follows:(1)Principle elaboration and mechanical analysis.In this paper,the principle of ultrasonic atomization in aerosol inkjet printing technology is described,the force analysis of aerosol particles is analyzed,and the prediction model of printing accuracy at the nozzle is given.(2)Research on process parameters and analysis of printing patterns.Aiming at the four factors of conveying gas flow,circumferential gas flow,gas flow ratio and printing speed,the process experiment is carried out.The influence rules of each factor on printing accuracy are obtained.The conductive image was printed out by optimizing the process parameters,and the morphology was observed and the electrical performance was analyzed.(3)Defect detection based on image processing.The problem of noise and illumination in the process of direct image acquisition is solved by image preprocessing.The image contrast is realized by using Python,Opencv library and SSIM algorithm to realize defect detection on Pycharm.