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Intelligent Digital-twin Prediction and Reverse Control System Architecture for Thermal Errors Enabled by Deep Learning and Cloud-edge Computing by Jialan Liu; Chi Ma; Hongquan Gui; Shilong Wang is a Computer Science article available to read on EtoBox.

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The heat generation is significant in the machining process, leading to thermal errors, and finally the geometric precision of machined parts is reduced. So the precision machine tool is a key factor in determining the geometric precision of complex parts. In recent years, the error control method is applied. But the method fails in reducing thermal errors because it cannot effectively process large-volume data, resulting from its low executing efficiency. To solve above issues, a new intelligent digital-twin prediction and reverse control system is designed for thermally induced errors based on the user-edge-cloud architecture to expedite the executing efficiency. The data-driven error modeling method is augmented by an error mechanism-based modeling to express the thermal error as a function with the temperature, armature current, rotational speed, and ambient temperature as independent variables, and then the long-term memorizing behavior of thermal errors is demonstrated. The error model is established based on an improved wavelet threshold denoising (IWTD) and a (long short-term memory) LSTM network to describe the memorizing behavior, and IWTD-LSTM network error prediction mo

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Author

Jialan Liu; Chi Ma; Hongquan Gui; Shilong Wang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)