Instytut Podstawowych Problemów Techniki

Streszczenie:

Material creep, defined as time-dependent strain accumulation under constant loading, can result in severe deformation and eventual component failure, posing a significant engineering challenge. Therefore, the possibility of early prediction of creep behavior is highly desirable. The objective of this study is to propose a robust method for predicting creep failure. To this end, we investigate the creep behavior of paper samples (quasi-brittle fiber composites) used as a model material, subjected to constant uniaxial tensile loads. Local strain fields are obtained through Digital Image Correlation and analyzed using dimensionality reduction techniques, a form of unsupervised machine learning, to identify universal indicators of deformation. This approach enables the detection of the onset of tertiary creep phase (deformation acceleration towards final failure), prediction of failure time, and accurate prediction of the failure location on the material surface just before the tertiary creep phase begins. Among the techniques used—Principal Component Analysis (PCA), Independent Component Analysis (ICA), Factor Analysis (FA), Non-negative Matrix Factorization (NMF), and Dictionary Learning (DL)—PCA and FA perform better in both detecting the onset of tertiary creep and predicting failure locations. The comparative analysis reveals the presence of universal characteristics in the evolution of local strain fields, offering a novel framework for studying material mechanics and providing key insights into failure prediction. In particular, the prediction of failure location as well as the comparison of the efficacy of various dimensionality reduction techniques are clearly novel aspects introduced in this work

Słowa kluczowe:

Creep, Digital Image Correlation, Local strain fields, Unsupervised machine learning, Dimensionality reduction techniques, Universal characteristics, Failure prediction

Afiliacje autorów: