| Carbon fiber resin-based composite materials have entered the stage of promotion and application since the 1980 s in China.After more than 30 years of development,most of the operations have been transformed and upgraded from manual operations to automated operations.However,after entering the 21st century,due to the large number of black boxes in the carbon fiber resin-based composite materials manufacturing process,and the manufacturing process has a low degree of dominance,the manufacturing process relies too much on the control of special processes to ensure the quality.It is difficult to analyze and solve the mechanism problems through the data of the manufacturing process,which makes it difficult to increase the capacity at this stage and the product quality fluctuates greatly.In response to the above problems,this thesis takes the most critical autoclave curing and molding process that involves the physical and chemical changes of the product in the carbon fiber resin-based composite materials manufacturing process as the research object.Through the real-time monitoring and early warning of the key data collection and analysis of the autoclave,the abnormality in the manufacturing process can be found in time,and timely adjustments can be carried out to avoid the occurrence of quality accidents.At the same time,the modeling application of data is carried out to carry out the forecasting work of the manufacturing process,and the unreasonable situation of the manufacturing process is found in advance,and the intervention and adjustment are made in the industrial design stage of the manufacturing front end.Finally,after completing the exploration of data storage and governance,the technology is applied to all aspects of carbon fiber resin-based composite materials manufacturing to improve the efficiency of the entire manufacturing process,effectively reduce quality costs,and stabilize product quality.This thesis specifically include:Firstly,the overall design plan of the intelligent monitoring system of the autoclave equipment for carbon fiber resin-based composite materials molding is proposed,which includes the autoclave data acquisition module,the online real-time detection and monitoring module,and the autoclave heat distribution prediction module.Secondly,through the equipment’s PLC and Modbus/TCP communication protocol,the temperature,pressure,vacuum and other key parameters and status data of the autoclave operation process are collected.While achieving a related product data and operating parameters by aligning two-dimensional code technology.Providing the data source for the subsequent data applications.Thirdly,the collected data are used to design and realize the on-line real-time online detection and monitoring function module of the autoclave,and the model of the process business was constructed to realize the rapid and accurate inspection of the curing process of the carbon fiber resin-based composite materials autoclave,greatly shorten the detection cycle.Through real-time monitoring of the operation process of the autoclave,problems in the operation process of the equipment is found in time,and early warning is given to the operator in advance to avoid quality accidents.Finally,based on the forming mechanism of fiber resin-based composite materials and the operating principle of autoclave equipment,the key temperature distribution is predicted.Aiming at the problem of high physical and chemical coupling during the curing process of carbon fiber resin matrix composites and key data factors that affect the data,data cleaning and normalization of the affected data are carried out.It is then introduced into the machine learning by constructing support vector regression model,to predict the carbon fiber resin matrix composite curing temperature distribution of the product,and the model and iterative optimization. |
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