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20232026
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cs.CE2026

Machine Learning-Based Battery State-of-health Prediction for Unmanned Aerial Vehicles Predictive Maintenance

Jiarui Xie, Lingchen Kong, Mohamed Rami Latreche +3

Battery state-of-health (SoH) prediction aims to estimate the remaining capacity by modeling battery degradation through its life cycle. Machine learning (ML)-based SoH models can…

cs.CE2025

Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing

Jiarui Xie, Yaoyao Fiona Zhao

The deployment of machine learning (ML)-based process monitoring systems has significantly advanced additive manufacturing (AM) by enabling real-time defect detection, quality asse…

cs.CE2024

Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability

Jiarui Xie, Zhuo Yang, Chun-Chun Hu +3

Powder bed fusion (PBF) is an emerging metal additive manufacturing (AM) technology that enables rapid fabrication of complex geometries. However, defects such as pores and balling…

cs.CE202413 cited

Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing

Jiarui Xie, Mutahar Safdar, Lequn Chen +2

Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal…

cs.CE2024

Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing

Jiarui Xie, Mutahar Safdar, Andrei Mircea +5

Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility…

cs.CE2023

Fairness- and uncertainty-aware data generation for data-driven design

Jiarui Xie, Chonghui Zhang, Lijun Sun +1

The design dataset is the backbone of data-driven design. Ideally, the dataset should be fairly distributed in both shape and property spaces to efficiently explore the underlying…