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About

Jihong Pang is a rising researcher whose work bridges reliability engineering, intelligent manufacturing, and decision science. His primary research areas include reliability assessment under limited data, supply-demand matching in cloud manufacturing, and risk evaluation for industrial robotics. Pang’s most notable contribution is a Bayesian reliability assessment framework for permanent magnet brakes (PMBs) using a bivariate Wiener model, addressing the critical challenge of evaluating complex systems with small sample sizes—a problem pervasive in automotive, robotics, and aerospace industries. This work has garnered 38 citations since 2024, reflecting its timely impact. He has also advanced cloud manufacturing by developing an intelligent evaluation method for supply-demand matching, integrating ELECTRE III and VIKOR to reduce transaction risks. Additionally, Pang optimized Failure Modes and Effects Analysis (FMEA) for industrial robots by combining TODIM with Best Worst Method and water filling theory in a Pythagorean fuzzy language environment, enhancing reliability assessment. His research demonstrates a commitment to solving real-world engineering challenges through innovative statistical and multi-criteria decision-making approaches.