A stress-strain prediction method based on machine learning

A stress-strain and machine learning technology, applied in the field of detection and prediction, can solve problems such as the indetermination of the strain function model, achieve the effects of convenient operation, easy promotion, and improved calibration accuracy

Inactive Publication Date: 2019-05-17
DALIAN UNIV OF TECH
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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 th

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  • A stress-strain prediction method based on machine learning
  • A stress-strain prediction method based on machine learning
  • A stress-strain prediction method based on machine learning

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Embodiment Construction

[0038] The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

[0039] attached figure 1Installation diagram for the strain sensor prediction system. The model of the optical fiber demodulator used in this example is si255-16-ST / 160-NO of MOI Company, its measuring range is -15000—15000με, the demodulation accuracy is 1pm, its measuring range is 0—2000με, the resolution is 0.5με. The data acquisition system selects the virtual instrument-based controller combination produced by American NI Company. Including data acquisition module NI PCI-4461, input and output module NI PCI-6528 / 5922, controller host PXIe-1082DC. Among them, the resolution of the data acquisition module PCI-4461 reaches 24 bits, and the sampling rate can reach 204.8kS / s. The force sensor is a model 41 load cell of Honeywell Company, which has a low error rate of up to 0.1%, and an optional output of 0 to 5V...

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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 ...

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Application Information

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IPC IPC(8): G06F17/50G06N20/10G01D21/02
Inventor 贾振元姜昕彤梁冰刘巍冯荻刘坤张洋
Owner DALIAN UNIV OF TECH
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