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About

Dapeng Tan is a researcher whose work bridges intelligent manufacturing, robotics education, and advanced assembly systems. His key research areas include computer-aided instruction for robotics, precision machining, and fault-tolerant assembly for complex products. Tan’s most influential contribution is his pioneering work on Intelligent Computer-Aided Instruction (ICAI) modeling for parallel robot instruction, which addresses the inherent difficulty of teaching parallel robot design and kinematics—a field known for its high precision but steep learning curve. This paper has garnered 41 citations, reflecting its foundational role in robotics pedagogy. He further advanced manufacturing with his investigation into pneumatic wheel machining using softness consolidation abrasives (30 citations), contributing to surface finishing techniques. More recently, Tan has focused on fault-tolerant assembly methods for small, complex products, proposing the AAPF method to enhance stability and efficiency in delicate assembly processes. His collaborative assembly research for low-voltage electrical apparatuses tackles the rigidity and labor-intensity of traditional assembly lines. Through these contributions, Tan has demonstrated a sustained commitment to making complex engineering systems more teachable, reliable, and adaptable.