A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design.  However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system.  This study develops a mathematical regression model to summarize the FE modeled results.  The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc.  The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. 
FE
A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design.  However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system.  This study develops a mathematical regression model to summarize the FE modeled results.  The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc.  The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. 
FE
A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design.  However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system.  This study develops a mathematical regression model to summarize the FE modeled results.  The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc.  The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. 
FE