Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel radial basis function (RBF) neural network-based geometric error compensation method with displacement measurements only is proposed in this paper. The individual geometric error components are formulated mathematically based on laser interferometer calibration with displacement measurements only and modeled using RBF neural network for error compensation in motion controller. Only 4 and 15 displacement measurements are required to identify the error components for XY and XYZ table, respectively. The experiment results on two XY tables illustrate the effectiveness of the proposed method. The overall errors can be reduced significantly after compensation, and different data intervals can be selected to reduce calibration time but maintain a high level of accuracy. The proposed methodology can be extended to other types of precision machine and is more suitable for precision machines requiring a relative low level of accuracy, but fast calibration like those used for acceptance testing and periodic checking.
['Rui Yang', 'Kok Kiong Tan', 'Arthur Tay', 'Sunan Huang', 'Jie Sun', 'J. Y. H. Fuh', 'Yoke San Wong', 'Chek Sing Teo', 'Zidong Wang']
I am writing a research paper on a idea An RBF neural network approach to geometric error compensation with displacement measurements only
[{"from": "human", "value": "I am writing a research paper on a idea An RBF neural network approach to geometric error compensation with displacement measurements only"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A novel radial basis function (RBF) neural network-based geometric error compensation method with displacement measurements only is proposed in this paper. The individual geometric error components are formulated mathematically based on laser interferometer calibration with displacement measurements only and modeled using RBF neural network for error compensation in motion controller. Only 4 and 15 displacement measurements are required to identify the error components for XY and XYZ table, respectively. The experiment results on two XY tables illustrate the effectiveness of the proposed method. The overall errors can be reduced significantly after compensation, and different data intervals can be selected to reduce calibration time but maintain a high level of accuracy. The proposed methodology can be extended to other types of precision machine and is more suitable for precision machines requiring a relative low level of accuracy, but fast calibration like those used for acceptance testing and periodic checking."}]