Machine Learning for Additive Manufacturing
PhD Research
Research focused on in-situ monitoring and quality assurance for laser powder-bed fusion using optical tomography and machine learning.
Scholarship
Research
Research gives me the technical depth to reason carefully about model behavior, evaluation, uncertainty, and real-world engineering constraints.
Industrial Process Monitoring
Computer Vision
Developed deep learning and image-processing approaches for detecting process anomalies and extracting useful information from industrial imaging systems.
Intelligent Manufacturing Systems
Industrial AI
Applied machine learning to engineering and manufacturing environments where reliability, interpretability, and deployment constraints matter.
Peer-Reviewed Research
Publications
Published research spanning additive manufacturing, optical monitoring, machine learning, and intelligent quality assurance.
Doctoral Work
PhD
In-Situ Monitoring and Quality Assurance Algorithms for Laser Powder-Bed Fusion using Optical Tomography.
My doctoral research focused on building machine learning and computer vision methods for monitoring additive manufacturing processes and identifying quality-related process behavior.
The work combined optical imaging, image processing, machine learning, anomaly detection, and engineering knowledge to develop practical approaches for industrial quality assurance.
Machine LearningComputer VisionAnomaly DetectionOptical TomographyAdditive Manufacturing
Research → Engineering
Why it matters
Research taught me to ask more than whether a model works.
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How should the system be evaluated?
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What happens when the data distribution changes?
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Where can the model fail?
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What assumptions are being made?
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Can the result be reproduced and trusted?
I bring that same thinking into production AI systems, where reliability and evaluation matter just as much as model capability.
Research outputs
Selected publications
Additive Manufacturing / 2023
Optical tomography and machine learning for in-situ defects detection in laser powder bed fusion: A self-organizing map and U-Net based approach
International Journal of Extreme Manufacturing / 2024