Papers
4
Total Citations
34
H-Index
3
About
Tomi Wijaya is a researcher specializing in advanced manufacturing, process monitoring, and intelligent robotic finishing systems. His work focuses on leveraging sensor data and frequency domain analysis to enhance automation in industrial processes, particularly in robotic deburring and abrasive belt grinding for aerospace applications. Wijaya’s major contributions include developing methods for event classification from sensor data using spectral analysis, enabling real-time detection of process anomalies without relying on direct contact measurements. This approach addresses the challenge of big data management in high-sampling-rate environments, improving efficiency in robotic finishing tasks. His most cited paper, “Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring” (2017, 20 citations), demonstrates the impact of his work on process monitoring. Additionally, his research on contact condition analysis using dynamic pressure sensors (2018, 6 citations) provides insights into material removal rates for compliant abrasive tools. Wijaya also contributes to predictive maintenance strategies, as seen in his 2021 study on condition monitoring for the ARTC Model Factory. His work is essential for advancing smart manufacturing and automation in precision engineering.
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