Yiguang Wang

Guilin University of Electronic Technology

Papers

Total Citations

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About

Yiguang Wang is a researcher whose work centers on the calibration and precision enhancement of industrial robotics, with a particular focus on six-degree-of-freedom (6-DOF) systems. Their key research areas include neural network-based modeling, finite element analysis (FEA), and the optimization of robotic positioning accuracy in real-world industrial settings. Wang’s most notable contribution is the development of a calibration method that integrates a Particle Swarm Optimization-Back Propagation (PSO-BP) neural network with FEA to correct the position and posture errors of industrial robots. This approach leverages the neural network’s ability to identify complex, nonlinear relationships between robot inputs and outputs, offering a more adaptive and accurate solution than traditional calibration techniques. While their most-cited paper, published in 2020, has garnered 2 citations, it represents a foundational step in applying intelligent algorithms to industrial automation challenges. Wang’s work is particularly valuable for engineers and researchers seeking to improve robot performance in manufacturing environments, where even minor positional errors can impact product quality and operational efficiency.