A stress-strain prediction method based on machine learning

Abstract

The invention discloses a stress-strain prediction method based on machine learning, and belongs to the technical field of detection and prediction. The prediction method comprises the steps of takingthe machine learning as a medium, processing the stress-strain experiment data as input and output of a learning model, selecting a proper algorithm and the corresponding training parameters, training, so that a prediction network is obtained. During the prediction process, the computer is operated to control the loading force of the loading device each time, a demodulator is used for collectingthe measurement data, a data analysis software is used for processing the experimental data, an appropriate learning model is established, the model is trained, and therefore accurate prediction of the strain field of the tested system is achieved. The method is suitable for the stress-strain field prediction of any optical fiber strain sensor detection system, avoids the errors caused by consideration of determination of loading force and pre-tightening force, simplification of the elastic modulus range of a test piece and a complex model and the like, greatly improves the calibration precision, and is convenient and rapid to operate and easy to popularize.

Description

technical field [0001] The invention belongs to the technical field of detection and prediction, and relates to a stress and strain prediction method based on machine learning. Background technique [0002] The stress and strain detection of aircraft tooling system plays a very important role in the online monitoring process of aircraft assembly, and is the key to ensuring the quality of aircraft assembly. Aircraft tooling detection requires high-precision measurement and prediction of the stress-strain field of tooling parts. The structure, position, and detection features of aircraft tooling parts are complex and diverse, which makes the strain characteristic detection conditions of tooling parts more stringent, and in terms of prediction efficiency and accuracy. There are also strict requirements. In recent years, with the continuous progress of industrial production, the demand for detection and monitoring of stress and strain has also increased, and it is necessary to ...

AI Technical Summary

Problems solved by technology

It avoids the problem that the initial conditions of the finite element analysis and the strain function model cannot be determined, and can effectively use the data-driven method to solve the difficulties caused by the uncertain factors in the mechanism analysis of the mathematical physical model, and realize the stress-strain field of the system under test. accurate prediction

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