Papers
3
Total Citations
9
H-Index
2
About
Hua-Tzu Fan is a researcher at the forefront of smart manufacturing and automotive production systems, with a focus on quality assurance, flexible assembly, and intelligent scheduling. Their work addresses critical challenges in modern automotive manufacturing, from paint shop quality to reconfigurable assembly systems. Fan’s most cited paper, “A Case Study on First Time Quality Feature Investigation for an Automotive Paint Shop” (2022, 6 citations), provides actionable insights into defect reduction in highly automated painting lines, directly impacting production efficiency. Building on this, Fan explores dynamic scheduling with “Matrix Assembly System Scheduling Optimization in Automotive Manufacturing: A Deep Q-Network Approach” (2024, 2 citations), pioneering the use of reinforcement learning to coordinate autonomous mobile robots (AMRs) in flexible matrix systems. Their third paper, “Intelligent Layout Reconfiguration for Reconfigurable Assembly System: A Genetic Algorithm Approach” (2024, 1 citation), advances the design of modular, AMR-integrated factories that adapt to shifting customer demands. Together, these contributions demonstrate Fan’s commitment to bridging automation, AI, and operational excellence in automotive manufacturing, offering practical solutions for industry 4.0 transformation.
Research Focus
Key Achievements
Top Papers
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