真空輔助樹脂轉注成型（Vacuum Assisted Resin Transfer Molding, VARTM）是以真空吸引將樹脂注入開放模具，同時浸潤纖維補強材之複合材料製造方法，近年來已被廣泛應用於各大領域。而碳纖維補強高分子複合材料（Carbon Fiber Reinforced Polymers, CFRP)，因質地輕、強度強而備受重視，但此材料最大缺失即是成本相對高，倘若產品中含有不可挽救之缺陷，其損失必然極大。故在製程中，往往結合非破壞性檢測（Non-Destructive Testing, NDT），來確保成品良率。
現今NDT種類繁多，而紅外線檢測領域的脈衝式熱成像法（Pulsed Thermography, PT）因檢測快速且架設方便，使應用非常廣泛。不過此法迅速獲取資訊同時也造就其影響過多導致難以辨識瑕疵，故現今發展方向多以除去圖像干擾因子，如不均勻熱源背景、圖像雜訊，來提高缺陷辨識能力，其中，傳統PT訊號處理方式以脈衝相位熱成像法（Pulsed Phase thermography, PPT）與熱訊號重構法（Thermal signal Reconstruction, TSR）為主，但兩法並不能完好除去干擾因子，針對此本文提出多維度系綜經驗模態分解法（Multi-dimensional Ensemble Empirical Mode Decomposition, MEEMD）與懲罰最小平方法（Penalized Least square methods）兩方法，以克服此問題。
為實現上述，本研究優先建構一套小型VARTM製程，以此製作含有缺陷之CFRP平板，同時架設PT檢測裝置配合LabVIEW軟體擷取來獲取原始熱像圖，最後結合包含PPT、TSR、MEEMD與AIRPLS方式作訊號處理獲取各自結果，並依據訊噪比（Signal-to-Noise Ratio, SNR）比較之。結果顯示TSR與PPT仍會受到雜訊干擾而模糊缺陷訊號，相較於MEEMD與AIRPLS，前者可將原始訊號從眾多干擾中單獨抽離，以保留瑕疵資訊，後者則利用最佳化運算，達到濾除雜訊與除去背景，以增進缺陷辨識度，兩方法皆有效凸顯瑕疵，尤以懲罰最小平方法最為突出，使SNR值大幅增加。

Vacuum Assisted Resin Transfer Molding (VARTM) is a popular manufacturing method of composite materials, where the porous preform placed in a single sided mold is impregnated with thermoset resin through the use of vacuum. It has been applied in many fields, including manufacturing carbon fiber reinforced polymers (CFRP). In the recent years, CFRP has been utilized due to its high strength and light weight. Nevertheless, the price of CFRP is relatively high. As a result, if the CFRP products contain irremediable defects, the economic loss is inevitably large. Therefore, efficient Non-Destructive Testing (NDT) is desired to ensure the quality of products.
Pulsed thermograph (PT) is a popular NDT technique for the convenient deployment and rapid detection. However, as PT data is usually acquired rapidly, the information of flaws is often contaminated by a variety of disturbances. For this reason, a number of data processing methods have been developed to enhance the detectability of PT through eliminating the effects of non-uniform heating and measurement noise. Conventional thermographic image analysis techniques include Pulsed Phase thermography (PPT), Thermal signal Reconstruction (TSR), et al. However, these methods cannot completely remove the influence of the interference in thermal images. Hence, two statistical algorithms are proposed to counter this problem, including Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and Penalized Least squares methods.
In this thesis, first, a VARTM experiment system was constructed to manufacture the CFRP specimens, while a PT system was established to acquire thermographic data. Then, PPT, TSR, MEEMD, and AIRPLS were utilized for thermal image processing and compared through Signal-to-Noise Ratio (SNR). The results show that the proposed MEEMD and AIRPLS methods significantly outperform the conventional methods by better eliminating non-uniform backgrounds and noise contained in thermal images.

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