Se-Chan Park, Deok-Yeop Kim, Kang-Bok Seo, Woo-Jin Lee

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 12, pp. 489-498, Dec. 2022
https://doi.org/10.3745/KTSDE.2022.11.12.489,   PDF Download:
Keywords: Data Imbalance, Outlier Handling, data augmentation, Tensile Tenacity and Tensile Elongation, Biodegradable Fiber(PLA)
Abstract

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.

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