Hock Hao Tan

About

Hock Hao Tan is a leading researcher in advanced manufacturing and intelligent process monitoring, with a primary focus on abrasive belt grinding and precision machining. His most influential work, "In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm" (2017), has garnered over 209 citations, establishing him as a key contributor to the field of smart manufacturing. Tan’s major contribution lies in developing machine learning frameworks—specifically combining support vector machines with genetic algorithms—to enable real-time, non-invasive detection of tool wear and process anomalies. This innovation significantly enhances process stability, reduces downtime, and improves surface quality in compliant grinding operations, which are critical for aerospace and automotive components. Beyond this seminal paper, Tan’s research spans sensor fusion, signal processing, and adaptive control for manufacturing automation. His work is widely recognized for bridging theoretical machine learning with practical industrial applications, making him a sought-after collaborator in both academia and industry. For students and researchers, Tan’s contributions offer a compelling model of how data-driven techniques can transform traditional manufacturing processes into intelligent, self-optimizing systems.

Research Focus

Key Achievements

Top Papers

1. 1
   In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm

   209 citations · 2017

Key Collaborators

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