Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing

Mai Li, Ying Lin (), Qianmei Feng, Wenjiang Fu, Shenglin Peng, Siwei Chen, Mahesh Paidpilli, Chirag Goel, Eduard Galstyan and Venkat Selvamanickam
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Mai Li: University of Houston
Ying Lin: University of Houston
Qianmei Feng: University of Houston
Wenjiang Fu: University of Houston
Shenglin Peng: University of Houston
Siwei Chen: Princeton Plasma Physics Laboratory
Mahesh Paidpilli: University of Houston
Chirag Goel: University of Houston
Eduard Galstyan: University of Houston
Venkat Selvamanickam: University of Houston

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 4, 3009-3030

Abstract: Abstract High-temperature superconductor (HTS) tapes have shown promising characteristics of high critical current, which are prerequisites for applications in high-field magnets. Due to the unstable growth conditions in the HTS manufacturing process, however, the frequent occurrences of dropouts in the critical current impede the consistent performance of HTS tapes. To manufacture HTS tapes with large scale, high yield, and uniform performance, it is essential to develop novel data analysis approaches for modeling the dropouts and identifying the related important process parameters. Conventional methods for modeling recurrent events, such as the point process, require the extraction of events from quality measurements. As the critical current is a continuous process, it may not comprehensively represent the drop patterns by transforming the time-series measurements into a set of events. To solve this issue, we develop a novel quantile regression-enriched event modeling (QREM) framework that integrates the non-homogeneous Poisson process for modeling the occurrence of dropouts and the quantile regression for capturing the drop patterns. By incorporating the feature selection and regularization, the proposed framework identifies a set of significant process parameters that can potentially cause the dropouts of HTS tapes. The proposed method is tested on real HTS tapes produced using an advanced manufacturing process, successfully identifying important parameters that influence dropout events including the substrate temperature and voltage. The results demonstrate that the proposed QREM method outperforms the standard point process in predicting the occurrence of dropouts.

Keywords: Superconductor manufacturing; Dropout events in critical current; Quantile regression; Non-homogenous point process; Feature selection and regularization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02358-7

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