Exploiting Artificial Intelligence for Predicting Subcritical Failure of Microstructurally Disordered Materials

• Applicant: Professor Dr. Michael Zaiser
• Subject Area: Mechanical Properties of Metallic Materials and their Microstructural Origins
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 446245542

Subcritical (creep) failure occurs when materials are subjected over extended periods of time to loads below their short-time strength. Time dependent processes such as plastic creep or chemical reactions, which are often thermally activated, may then lead to a gradual accumulation of microstructural damage and ultimately to materials failure. Even for identically manufactured samples, failure times may exhibit a huge scatter. Furthermore, long failure times are difficult to access in experiment. It is therefore desirable to use monitoring data for sample- or component-specific prediction of residual lifetime. This can avoid costs associated with premature replacement of still functional parts as well as mitigate against in-service failure of damaged components. In the past, such predictions were often based on the characteristic U shape of the creep rate vs time curve, where creep rate first decelerates over time (Stage I) then passes a broad minimum with approximately constant creep rate (Stage II) and then accelerates in the run-up to failure 8Stage III). Sample specific lifetime predictions may then be based upon the location of the creep rate minimum, or upon the time dependency of creep rate in the approach to failure which may be described mathematically by a finite-time singularity at the failure time. Other monitoring approaches focus on characteristic increases in the rate of acoustic emissions, or on the localization of damage and deformation activity in the vicinity of the ultimate failure plane. The proposed project investigates whether methods of artificial intelligence / machine learning can be exploited to obtain improved predictions of residual sample lifetime from monitoring data which characterize the spatial and temporal evolution of deformation activity during creep. To this end we will use both computer generated data obtained from simulation of material models of different complexity, and on the other hand experimental data obtained from serial creep tests on paper samples accompanied by acoustic and optical monitoring. In each case we are dealing with large numbers of data sets (typically some 10000 in case of simulated samples and several 100 for experimental samples) which describe the creep history of individual samples. Part of these data sets is used to train so-called Neural Networks in ‘predicting’ failure by relating the spatial and temporal pattern of creep activity to a predicted failure time. The remaining data are then used to assess the quality of these predictions, and the method is benchmarked against other forecasting strategies described in the literature.

• DFG Programme: Research Grants