Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network

Yang Zhang; Ke Wang; Hao Peng; Xuegang Liu; Yanfen Huang; Hai An; Yang Lei

doi:10.1021/acsomega.3c06140

Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network

ACS Omega. 2023 Dec 8;8(50):47812-47820. doi: 10.1021/acsomega.3c06140. eCollection 2023 Dec 19.

Authors

Yang Zhang^{1

2

3}, Ke Wang², Hao Peng⁴, Xuegang Liu³, Yanfen Huang², Hai An⁵, Yang Lei²

Affiliations

¹ School of History and Culture, Hubei University, Wuhan 430062, China.
² School of Chemistry and Chemical Engineering, Hubei Key Laboratory of Coal Conversion and New Carbon Materials, Wuhan University of Science and Technology, Wuhan 430081, China.
³ Jingzhou Conservation Center, 108 Jingbei Road, Jingzhou 434020, China.
⁴ Jingzhou Museum, 166 Jingzhong Road, Jingzhou 434020, China.
⁵ Shanxi Academy of Ancient Building and Painted Sculpture & Fresesco Preservation, Taiyuan 030012, China.

Abstract

Poly(methyl methacrylate) (PMMA) is widely used in the preservation and exhibition of cultural relics in museums. Accurately predicting its service life can help avoid many negative effects caused by PMMA aging. To study the change in the yellowing index of PMMA after aging in a UV light environment, an aging experiment was conducted. A prediction model for the service life of PMMA was established using nonlinear curve fitting and a back propagation (BP) neural network. By comparing the goodness of fit, simulation and modeling capabilities of the initial data, and the predictive ability for new data, it was found that the BP neural network prediction model outperformed the nonlinear curve fitting prediction model. In this study, the service life of newly produced PMMA samples was calculated as 7.83, 8.47, and 8.42 years, based on the yellowing index of retired PMMA as a benchmark and using the output data from the BP neural network prediction model. At this time, the performance and exhibition effect of the PMMA are poor, and the batch of PMMA needs to be updated.