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Defects Detection at Additive Manufacturing by Convolutional Deep Learning
(Düzce Üniversitesi, 2024) Lotfinejad, Reza; Zajkani, Asghar
Additive Manufacturing technologies present a wide array of benefits, including the capacity to manufacture components with complex geometric forms, reduced production expenses, minimized material usage, and time efficiency. This research constitutes a significant effort to pinpoint geometric defects and dimensional irregularities as well as surface quality imperfections in the Fused Deposition Modeling process through the development of a deep learning model utilizing multi-scale convolutional neural networks. The proposed methodology encompasses three distinct scales, each capable of identifying defects of varying dimensions. The model underwent extensive hybridizing procedures for precisely training through diverse datasets, and the training process is repeated numerous times until the desired level of accuracy was attained. A sufficiently extensive image datasets are employed to train the models, leading to the precise calibration of the network. As a result, the necessity for prolonged time and intricate computations to identify large-scale defects is eliminated. The highest validation accuracy for defect detection in this study reached 94%.