Welcome
I help build technologies that empower people and industries toward smarter, safer, and more sustainable futures.
Through robotics, intelligent manufacturing, digital twins, and industrial AI, my work focuses on developing systems that help people better understand, monitor, and navigate increasingly complex environments.
I believe engineering can help illuminate complexity in ways that enable people to engage with technology more clearly, responsibly, and meaningfully.
“Born human;
May die awakened;
While alive, be useful.”
Current Focus
Intelligent Robotics
Autonomous and semi-autonomous robotic systems for industrial environments, adaptive fabrication, and human–robot collaboration.
Manufacturing Intelligence
Digital twins, industrial AI, and intelligent monitoring systems for data-driven manufacturing and operational decision support.
Sustainable Industrial Systems
Research toward scalable prefabrication, resource-efficient production, and next-generation industrial infrastructure.
Selected Publications
A Reliable Real-Time Tool Wear Monitoring Framework Based on Temporal Segmentation Using Domain-Informed Stacked AI Models with Physics-Constrained Predictions
International Journal of Advanced Manufacturing Technology · 2026
Real-time tool wear monitoring is critical for maintaining machining quality and preventing unplanned industrial downtime, yet existing AI-based approaches often lack robustness, interpretability, and adaptability across evolving wear conditions. This work introduces the D-STL-MPK framework, integrating temporal segmentation, domain-informed stacked AI models, monotonicity enforcement, and physics-constrained Kalman smoothing to achieve interpretable and deployment-ready wear prediction with sub-2 ms inference latency, ultra-light computational cost, and strong generalization across public manufacturing datasets.
Patel, D., Muthuswamy, S. A Reliable Real-Time Tool Wear Monitoring Framework Based on Temporal Segmentation Using Domain-Informed Stacked AI Models with Physics-Constrained Predictions. International Journal of Advanced Manufacturing Technology (2026).
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