Research On Intelligent Pressure Measurement System For Copper Pillar Based On Machine Vision

Chamber pressure is an important indicator for measuring the performance of artillery and firearms,it has significant implications for the research and design of barrel weapons.The plastic pressure measurement has always been the main means of measuring chamber pressure due to its advantages of simple operation,wide applicability,high reliability,and good economy.However,due to the reliance on manual use of micrometers for repeated measurement during high-precision readings,as well as the single measuring point,lack of uniform standards,and subsequent operations require manual table lookup,pressure conversion correction,and data recording,which results in a complex and cumbersome process,high labor costs,and inevitably produces human errors,directly affecting the accuracy of chamber pressure.In recent years,with the rapid development of machine vision,it has been widely used in more and more fields due to its advantages of good stability,high efficiency,high accuracy,non-contact,etc.This paper uses machine vision technology to achieve accurate measurement of copper columns and develops an intelligent pressure measurement software system using the Lab VIEW platform to automate the entire pressure measurement process and improve the efficiency of pressure measurement.The main contents of this paper are as follows:(1)Research on the working mechanism of plastic pressure measurement,the theory of copper column pressure measurement,discuss and analyze their errors.Research and introduce the static calibration system that has been used all along,and provide a theoretical basis t for the subsequent development of the system.(2)Develop a machine vision-based intelligent copper column pressure measurement system.First,analyze the overall indicators of the system and determine the system design scheme.Secondly,select appropriate hardware and build a suitable machine vision height measurement platform.Then,analyze the functional and non-functional requirements of the software system,divide the functional modules according to the requirements,and design the modules in detail to realize the functions of copper column height measurement,pressure conversion,data correction and recording,etc.Finally,analyze and test the system’s errors.The system is stable,comprehensive in function,excellent in performance,and easy to operate.(3)A regression model of support vector machine based on Genetic Algorithm(GASVR)was established for the judgment of copper column height.Firstly,preprocess the image to obtain sub-pixel level edge information of the copper column,use it as the feature input of the optimized model,and conduct model training and evaluation.Then,conduct experimental comparison.The results show that the GA-SVR model has better performance and higher accuracy,further improving the accuracy and stability of measurement.Finally,the system’s measurement accuracy of the system was found to be up to ±5 μm,and up to ± 2μm in some cases.