An Investigation of the Relationship Between Encoder Difference and Thermo-Elastic Machine Tool Deformation
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Chair of Machine Tools, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Germany
Submission date: 2023-03-31
Final revision date: 2023-06-21
Acceptance date: 2023-06-22
Online publication date: 2023-06-26
Publication date: 2023-09-30
Corresponding author
Mathias Dehn   

Chair of Machine Tools, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Germany
Journal of Machine Engineering 2023;23(3):26-37
New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermos-elastic error. Afterward, the artificial neural network is presented, which uses the encoder difference to compute the thermo-elastic error. To conclude, the potential of the encoder difference as an input of the model is evaluated.
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