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Prediction method of service life of polymer materials based on environmental big data and machine learning

A polymer material and machine learning technology, applied in the field of service life prediction of polymer materials based on environmental big data and machine learning, can solve the problems of low accuracy of model prediction results, shorten product development cycle and improve weather resistance quality , the effect of strong universality

Active Publication Date: 2021-11-12
CHINA NAT ELECTRIC APP RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The limitation of this method is that the parameterized fitting of the influence factors of humidity and radiation on materials is based on the average value of many kinds of materials, and there are large differences in the influence of different materials by light radiation and humidity
This also leads to less accurate model predictions

Method used

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  • Prediction method of service life of polymer materials based on environmental big data and machine learning
  • Prediction method of service life of polymer materials based on environmental big data and machine learning
  • Prediction method of service life of polymer materials based on environmental big data and machine learning

Examples

Experimental program
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Effect test

Embodiment 1

[0064] Such as figure 1 As shown, the method for predicting the service life of polymer materials based on environmental big data and machine learning algorithms provided in this embodiment includes the following steps:

[0065] Carry out natural aging tests on polystyrene swatch samples in Qionghai, Sanya, Guangzhou and Jeddah, Saudi Arabia. The size of the swatch is 50×80×4mm. The test method refers to GB / T 3681. The surface of the sample is free from defects and has high transparency. The yellowness index is set as the life evaluation index, and the difference between the yellowness index and the initial value is 50 as the life end point, and the initial yellowness index of the sample is tested before the test.

[0066] During the natural aging process of polystyrene swatch samples, sampling tests were carried out every other month to analyze their natural environment in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 months The corresponding yellow index ( figure 2 shown in ...

Embodiment 2

[0076] Such as figure 1 As shown, the method for predicting the service life of polymer materials based on environmental big data and machine learning algorithms provided in this embodiment includes the following steps:

[0077] Carry out natural aging tests on polycarbonate swatch samples in Qionghai, Sanya, Guangzhou and Sanare, France abroad. The swatch size is 50×80×4mm. The test method refers to GB / T 3681. The surface of the sample is free from defects and has high transparency. The color difference is set as the life evaluation index, and the difference between the color difference and the initial value is 35 as the life end point, and the initial color difference of the sample is tested before the test.

[0078] During the natural aging process of polycarbonate swatch samples, sampling tests were carried out every other month to analyze their natural environment in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 months The corresponding color difference ( image 3 ).

[0...

Embodiment 3

[0088] Such as figure 1 As shown, the method for predicting the service life of polymer materials based on environmental big data and machine learning algorithms provided in this embodiment includes the following steps:

[0089] The natural aging test of high-density polyethylene dumbbell-shaped samples was carried out in Qionghai, Sanya, Guangzhou and Chennai, India. The sample size complied with GB / T 1040, and the test method referred to GB / T 3681. The sample is complete with no defects and scratches on the surface. The tensile strength is set as the life evaluation index, and the initial tensile strength is 30% as the end of life, and the initial tensile strength of the sample is tested before the test.

[0090] During the natural aging process of high-density polyethylene dumbbell-shaped samples, sampling tests were carried out every other month, and the samples were analyzed after aging for 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, and 36 months. corresponding tensile st...

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Abstract

The invention discloses a method for predicting the service life of polymer materials based on environmental big data and machine learning algorithms. The method extracts the characteristic data of the natural environment as the characteristic parameters, and uses the principal component analysis algorithm to reduce the dimension and noise of the characteristic parameter data For processing, the processed results are used as input parameters, and the aging degree of corresponding materials is used as output parameters, and the test data in some areas are used as training sets, and Python software is used for machine learning of the relationship between environment and performance changes, and a life prediction model is constructed. In the next step, the service life prediction of polymer materials in different regions. This method has the advantages of convenience, speed, and high accuracy, can effectively reduce the workload of the test, and can be used to guide the improvement of weather resistance of materials and the design of product weather resistance.

Description

technical field [0001] The invention belongs to the technical field of polymer material service life prediction, and in particular relates to a polymer material service life prediction method based on environmental big data and machine learning. Background technique [0002] Due to a series of excellent properties and high cost performance, polymer materials are widely used in equipment products in the fields of automobiles, electronics, electrical appliances, construction, packaging, chemical engineering, and biological engineering. During the process of processing, storage and service, the polymer materials of equipment products will gradually lose their luster and color under the joint action of various internal and external factors, such as plastics, rubber, paints, inks, etc. , yellowing, cracking, peeling, embrittlement, and the decline of various physical and chemical properties, and finally lead to the loss of its performance. Compared with metal materials, polymer ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16C60/00G06F30/27G06N20/00G06F113/26G06F119/04G06F119/08
CPCG16C60/00G06F30/27G06N20/00G06F2113/26G06F2119/08G06F2119/04
Inventor 覃家祥李淮陶友季时宇张晓东
Owner CHINA NAT ELECTRIC APP RES INST