publications

2027

1. Coordinated laser power and beam shaping for controlled melt pool dynamics in laser powder bed fusion

   C. Taylor et al.

   CIRP , 2027

   In preparation. Available upon request.

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2026

1. 

   PI-TSAD: A physically informed time-series anomaly detection framework for real-time monitoring of keyhole collapse in laser powder bed fusion

   C. Taylor, C. Porter, G. Mathesen, K. Mumm, F. Carter, and J. Cao

   Journal of Manufacturing Processes, 2026

   Accepted for publication. A physically informed anomaly detection framework for real-time monitoring of keyhole collapse in laser powder bed fusion using in situ process signals.

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   Reliable in-situ detection of keyhole collapse in Laser Powder Bed Fusion (LPBF) remains challenging due to the transient nature of the melt pool and the inherent stochasticity of the process. This study presents a physically informed, machine learning-based framework that identifies collapse events directly from Thermal Energy Density (TED) obtained from coaxial photodiodes with a sampling rate of 200 kHz. Power Spectral Density (PSD) analysis of the TED signal revealed that low-frequency regions are dominated by pink noise and high-frequency regions by white noise, with bulk melt pool motion and collapse dynamics confined to the intermediate band. Accordingly, a 0.25-30 kHz band- pass filter was applied to the TED signal to isolate the melt pool dynamics. Statistical and frequency- domain features from the filtered TED are used to train a Random Forest anomaly detection model using ground truth data from the Advanced Photon Source, where high-speed operando X-ray imaging verified collapse events. Applied to full build TED data from a commercial DMG MORI LASERTEC 12 SLM (LPBF) machine, the framework generalizes within the same material system and sensing modality without retraining, preserving a strong correlation between predicted anomalies and part-level CT-measured porosity. By integrating physics-based filtering, feature-driven learning, and adaptive thresholding, this method provides a scalable and interpretable foundation for real-time LPBF defect detection and monitoring.

2. 

   MOSAIC: Mobile Object Segmentation under Adverse Imaging Conditions for Rapid L-PBF Keyhole Behavior Characterization

   G. Mathesen, K. Mumm, C. Taylor, and J. Cao

   Journal of Manufacturing Processes, 2026

   An image segmentation algorithm for rapid analysis of L-PBF keyhole dynamics in high-speed X-ray images, without manual labeling or model training.

   DOI

2024

1. 

   Evaluation of a Systems-Based Approach to Shingle Impact Performance

   C. Taylor and D. Buckwalter

   Owens Corning Science & Technology Center, Granville, Ohio , Aug 2024

   Peer-reviewed internal document (Proprietary/Trade Secret). An internal technical document evaluating a systems-based approach to shingle impact performance and damage quantification.

2. 

   Experimental Measurements and Numerical Finite Element Models of Radial Indentation

   C. Taylor

   Honors Undergraduate Research Thesis, The Ohio State University, 2024

   Undergraduate honors thesis on experimental measurements and finite element modeling of advanced metalforming processes.

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