Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

IF 4.2 2区工程技术 Q2 ENGINEERING, MANUFACTURING

Advances in Manufacturing Pub Date : 2023-10-18 DOI:10.1007/s40436-023-00455-z

Qiang-Qiang Liu, Shu-Ting Liu, Ying-Guang Li, Xu Liu, Xiao-Zhong Hao

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Abstract

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 monitoring the full temperature field or have to introduce heterogeneous 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 surface temperature measurements of auxiliary materials is proposed. Using the proposed method, an average temperature monitoring accuracy of 97% is achieved in various heating patterns. In addition, this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part. This method was experimentally validated during the self-resistance electric heating process, in which the monitoring accuracy reached 93.1%. This method can potentially be applied to automated manufacturing and process control in the composites industry.

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基于深度学习的原位加热过程中固化温度的非接触式全场在线监测

对固化温度场进行在线监测对于提高复合材料部件制造过程的质量和效率至关重要。传统的基于嵌入式传感器的技术难以监测全温度场，或者必须引入可能对零件产生不良影响的异构项目。本文提出了一种基于深度学习的非接触式全场监测方法，可利用辅助材料的表面温度测量值实时预测复合材料零件的内部温度场。使用所提出的方法，在各种加热模式下的平均温度监测精度达到了 97%。此外，该方法还证明了在部件上覆盖较强隔热层时的可行性。这种方法在自电阻电加热过程中得到了实验验证，监测精度达到了 93.1%。这种方法有望应用于复合材料行业的自动化制造和过程控制。

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来源期刊

Advances in Manufacturing Materials Science-Polymers and Plastics

CiteScore

9.10

自引率

3.80%

发文量

274

期刊介绍： As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.