Max Robotics — Global Service Robot Directory + US Certification

About

Dequan Zhang is a prominent researcher specializing in reliability analysis, uncertainty quantification, and kinematic accuracy assessment for industrial robots and mechanical systems. His work addresses one of modern manufacturing's most pressing challenges: ensuring that complex robotic systems perform within precise tolerances despite inherent uncertainties in component dimensions, joint clearances, and manufacturing errors. Zhang's most influential contributions include the development of advanced surrogate modeling techniques — particularly radial basis function networks and Kriging-based methods — to efficiently evaluate the reliability of industrial robot positioning accuracy, circumventing the prohibitive computational costs of traditional approaches. His highly cited 2020 paper on hybrid learning algorithms for reliability analysis (162 citations) and his moment-based positioning accuracy framework (128 citations) have become foundational references in the field. Notably, he has pioneered applications of evidence theory alongside conventional probability methods, enabling more robust uncertainty characterization when data is sparse or imprecise. With over 800 cumulative citations across his top works, Zhang's research has meaningfully advanced reliability-based design optimization for industrial mechanisms, including RV reducers used in precision robotics. His computational frameworks for kinematic reliability have provided engineers with practical, scalable tools that bridge theoretical mathematics and real-world manufacturing challenges, making him a significant voice in modern mechanical reliability engineering.