Jianguang Fang

ORCID: 0000-0003-0119-6108
Publications
Citations
Views
---
Saved
---
About
Contact & Profiles
Research Areas
  • Cellular and Composite Structures
  • Topology Optimization in Engineering
  • Advanced Multi-Objective Optimization Algorithms
  • High-Velocity Impact and Material Behavior
  • Probabilistic and Robust Engineering Design
  • Composite Structure Analysis and Optimization
  • Structural Response to Dynamic Loads
  • Fluid Dynamics Simulations and Interactions
  • Transportation Safety and Impact Analysis
  • Mechanical Engineering and Vibrations Research
  • Additive Manufacturing and 3D Printing Technologies
  • Mechanical Behavior of Composites
  • Advanced Materials and Mechanics
  • Bone Tissue Engineering Materials
  • Numerical methods in engineering
  • Vehicle Noise and Vibration Control
  • Metal Forming Simulation Techniques
  • Polymer composites and self-healing
  • Acoustic Wave Phenomena Research
  • Automotive and Human Injury Biomechanics
  • Engineering Applied Research
  • Fatigue and fracture mechanics
  • Structural Health Monitoring Techniques
  • Electric Motor Design and Analysis
  • Magnetic Bearings and Levitation Dynamics

University of Technology Sydney
2017-2024

Taiyuan University of Technology
2022

Shandong University of Science and Technology
2021

Hunan University
2019

The University of Sydney
2014-2019

Bauhaus-Universität Weimar
2019

Qingdao National Laboratory for Marine Science and Technology
2017

Chinese Academy of Fishery Sciences
2006-2017

Tongji University
2012-2016

Ocean University of China
2015

10.1016/0168-5597(93)90115-6 article EN Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 1993-12-01

10.1016/j.ijsolstr.2017.08.013 article EN International Journal of Solids and Structures 2017-08-17

10.1016/j.ijmecsci.2023.108657 article EN International Journal of Mechanical Sciences 2023-07-30

With the wide application of industrial robots in field precision machining, reliability analysis positioning accuracy becomes increasingly important for robots. Since robot is a complex nonlinear system, traditional approximate methods often produce unreliable results analyzing its accuracy. In order to study more efficiently and accurately, radial basis function network used construct mapping relationship between uncertain parameters position coordinates end-effector. Combining with Monte...

10.1109/tr.2020.3001232 article EN IEEE Transactions on Reliability 2020-07-24
Coming Soon ...