Effects of Slit Edge Notches on Mechanical Properties of 3D-Printed PA12 Nylon Kirigami Specimens

Shu, Jing; Wang, Junming; Li, Zheng; Tong, Kai-Yu

doi:10.3390/polym15143082

Cited by 2 publications

(3 citation statements)

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“…There is a special case with additional segmentations (AE [20,40], AE [40,60]), resulting in a larger testing RMSE of 14.77 mm. It will be discussed in depth in the following paragraph, along with an illustration in temporal style.…”

Section: Resultsmentioning

confidence: 99%

“…As indicated by the pinkhighlighted area, the saturation introduces a discrepancy between the estimation results and the actual extended length, resulting in a deflection with a span of 50 mm in the R 2 graph. This phenomenon has a significant impact on the regression of sensor prediction when using a neural network with segmentation that includes this region (AE[0,20] þ AE [20,40] þ AE [40,60] þ AE[60,80] þ AE[80 110]). However, by intuitively excluding this portion in the segmentation [20,60], the performance of drift-aware feature learning remains consistent and improves with increased segmentation.…”

Section: Resultsmentioning

confidence: 99%

“…The scanning electron microscope with model JSM-7800 F (JEOL Ltd., Japan) was employed to examine distinct areas of the samples subjected to various stretching durations under 10 kV, involving unstretched sample, and sample stretched for 20 h. Prior studies on kirigami structures have indicated the presence of stress concentration at the edge of the hole. [5,40] Consequently, closer inspections were performed on the hole (corner) and hole (center) regions, as shown in Figure 12. As illustrated in Figure 12, upon closer examination, microcracks were observed at the edge of the holes in the sensor after being stretched for 20 h (hole [corner] and hole [center], as shown in Figure 12b).…”

Section: Discussionmentioning

confidence: 99%

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Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

Wang,

Shu,

Alam

et al. 2024

Advanced Intelligent Systems

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In this article, a novel approach is presented for drift‐aware feature learning aimed at calibrating drift biases in soft sensors for long‐term use. The proposed method leverages an autoencoder for data preprocessing to extract expressive signal drift traces features, and incorporates drift characteristics through the latent space representation in a long short‐term memory (LSTM) regression neural network. In the results, it is demonstrated that the proposed approach outperforms other typical recurrent neural networks, such as LSTM, gated recurrent unit, and bidirectional LSTM, with a reduced root mean square error of 60% for the training dataset (≈2.5 h) and 80% for the testing dataset (≈20 h). The proposed approach has the potential to optimize the performance of soft sensors with long‐term drift and reduce the need for frequent recalibration. By compensating for sensor drift using existing prior information and limited time data, the proposed neural network can effectively reduce the complexity and computational burden of the system, without the need for additional settings or hyperparameter fine‐tuning.

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Section: Resultsmentioning

confidence: 99%