Abstract
Machine learning is useful for analyzing and monitoring complex manufacturing processes. However, it has several limitations including the curse-of-dimensionality and lack of training data. In this paper, we propose a quantum machine learning strategy to tackle these challenges. Quantum support vector machine is applied to identify the states of machines in fused filament fabrication process based on acoustic emission data. Quantum convolutional neural network is used to detect spatters in laser powder bed fusion process based on coaxial optical images. Our results show that quantum machine learning can achieve the similar accuracy levels of predictions by classical machine learning counterparts, but with exponentially fewer parameters.
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