Investigation of Curing Process Heterogeneity from Raman Spectrum via CP Decomposition
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Monitoring the Raman Spectrum Fluence in the Ultraviolet 3D Curing process is essential in the optical science. Traditional method based on SEM scanning suffers from complexity and time-consuming. Wavelet decomposition based method encounters difficulty in frequency selection and interpretation. This research proposes a novel unsupervised learning algorithm based on CP decomposition, which clusters cured samples via heterogeneous features extracted from Raman spectrum fluence data. The effectiveness of the proposed method is demonstrated by a real case study.