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首页> 外文期刊>Mechanical systems and signal processing >A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data
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A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data

机译：基于多传感器数据预测碳纤维增强塑料（CFRP）分层的新方法

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摘要

Carbon fiber reinforced plastic (CFRP) has been widely used in many fields such as in the aerospace and automotive industries. Drilling of CFRP is a key process in the manufacture of CFRP components. The existing quality control and tool change decision methods are mainly based on delamination damage. However, estimating delamination damage in situ is still a challenge in the process of continuous drilling. To solve this problem, a comprehensive delamination prediction method based on multi-sensor data is proposed in this paper. In process of the drilling, the force, torque, temperature, vibration and hole exit images were collected, and the delamination was quantified by a proposed statistical delamination factor F_s. Singular spectrum analysis (SSA) is used to smooth the F_s sequence to reduce randomness. Then, a XGBoost-ARIMA model is constructed for rolling prediction of F_s. Finally, drilling experiments were carried out to verify the effectiveness of the proposed method. The experimental results showed that compared with traditional delamination evaluation factors, F_s reduced the mean square error (MSE) of prediction by more than 50%. Compared with that of traditional machine learning models such as an SVM and ANN, the MSE of the model's regression part is decreased by more than 39%. The proposed method can provide a solution for real-time and in situ prediction of delamination damage in the continuous drilling process of CFRP components.
机译：碳纤维增强塑料（CFRP）已广泛应用于许多领域，例如航空航天和汽车工业。 CFRP的钻孔是CFRP组件制造的关键过程。现有的质量控制和工具改变决策方法主要基于分层损坏。然而，估计分层损坏原位仍然是连续钻井过程中的挑战。为了解决这个问题，本文提出了一种基于多传感器数据的综合分层预测方法。在钻孔的过程中，收集力，扭矩，温度，振动和空穴出口图像，并通过所提出的统计分层因子F_定量分层。奇异频谱分析（SSA）用于平滑F_S序列以降低随机性。然后，构造XGBoost-Arima模型用于F_S的滚动预测。最后，进行了钻探实验以验证所提出的方法的有效性。实验结果表明，与传统的分层评估因子相比，F_S将预测的平均方误差（MSE）降低了50％以上。与传统机器学习模型（如SVM和ANN）相比，模型的回归部分的MSE减少了39％以上。该方法可以在CFRP组分的连续钻井过程中提供实时和原位预测的实时和原位预测。

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  • 来源
    《Mechanical systems and signal processing》 |2021年第8期|107708.1-107708.23|共23页
  • 作者

    Jiacheng Cui; Wei Liu; Yang Zhang; Changyong Gao; Zhe Lu; Ming Li; Fuji Wang;

  • 作者单位

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

    Key Laboratory for Precision and Non-traditional Machining Technology of the Ministry of Education Dalian University of Technology No. 2 Linggong Road Dalian 116023 China;

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  • 正文语种 eng
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  • 关键词

    Multisensor measurement; Delamination evaluation; Machine learning; In situ prediction; Drilling of CFRP;

    机译：多传感器测量;分层评估;机器学习;原位预测;钻探CFRP.;

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