Estimation of Damaged Surface Images in Drilled Unidirectional Carbon Fiber Reinforced Polymer Sheets Using Convolutional Autoencoder and Multi-Layer Perceptron Decoder 


Vol. 26,  No. 12, pp. 5647-5660, Dec.  2025
10.1007/s12221-025-01181-y


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  Abstract

Drilling of unidirectional carbon fiber reinforced polymer sheets often leads to damage around the hole, compromising the mechanical integrity of composite structures. To address this challenge, a deep learning framework was developed to predict drilling-induced damage images using process parameters as inputs. A convolutional autoencoder (CAE) was first employed to augment the limited experimental dataset by generating synthetic grayscale damage images. Subsequently, a multi-layer perceptron (MLP) decoder model was trained to predict damage images based on spindle speed and feed rate. Three CAE architectures were evaluated, with CAE Type I achieving the lowest reconstruction error in the damage area, with an error of 10.14% and an R2 value of 0.9862. Four MLP-Decoder models were tested using different combinations of original and CAE-generated images. The model trained with both original and CAE Type I images (MLP-Decoder Type B) showed the highest prediction accuracy, with an MSE of 1.13 and a predicted damage area of 36.92 mm2, which is closer to the validation data. Comparative analysis against experimentally validated images demonstrated that the proposed framework can effectively estimate drilling damage patterns.

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  Cite this article

[IEEE Style]

E. Kurniawan, Y. C. Hur, J. H. Kim, "Estimation of Damaged Surface Images in Drilled Unidirectional Carbon Fiber Reinforced Polymer Sheets Using Convolutional Autoencoder and Multi-Layer Perceptron Decoder," Fibers and Polymers, vol. 26, no. 12, pp. 5647-5660, 2025. DOI: 10.1007/s12221-025-01181-y.

[ACM Style]

Eddy Kurniawan, Yong Chan Hur, and Ji Hoon Kim. 2025. Estimation of Damaged Surface Images in Drilled Unidirectional Carbon Fiber Reinforced Polymer Sheets Using Convolutional Autoencoder and Multi-Layer Perceptron Decoder. Fibers and Polymers, 26, 12, (2025), 5647-5660. DOI: 10.1007/s12221-025-01181-y.

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