首页|Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning
Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning
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NETL
NSTL
万方数据
Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts.Traditional embedded sensor-based technologies have difficulty moni-toring the full temperature field or have to introduce het-erogeneous items that could have an undesired impact on the part.In this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using sur-face temperature measurements of auxiliary materials is pro-posed.Using the proposed method,an average temperature monitoring accuracy of 97％is achieved in various heating patterns.In addition,this method also demonstrates satisfy-ing feasibility when a stronger thermal barrier covers the part.This method was experimentally validated during the self-resistance electric heating process,in which the moni-toring accuracy reached 93.1％.This method can potentially be applied to automated manufacturing and process control in the composites industry.
Online monitoringCuring temperature fieldDeep learning(DL)In-situ heating
NETL
NSTL
万方数据
