Exploiting Artificial Intelligence for PredictingSubcritical Failure of Microstructurally Disordered Materials

当材料长时间承受低于其短时强度的载荷时，就会发生亚临界（蠕变）破坏。与时间相关的过程，如塑性蠕变或化学反应，通常是热激活的，可能导致微观结构损伤的逐渐积累，最终导致材料失效。即使是相同制造的样品，失效时间也可能表现出巨大的分散。此外，在实验中难以获得较长的失效时间。因此，需要使用监测数据对样品或部件特定的剩余寿命进行预测。这可以避免过早更换仍然有效的部件所带来的成本，并减轻损坏部件在使用中发生故障的风险。在过去，这种预测通常是基于蠕变率与时间曲线的特征U形，蠕变率首先随着时间的推移而减速（阶段I），然后以近似恒定的蠕变率通过一个广泛的最小值（阶段II），然后在即将发生故障时加速（阶段III）。样本特定的寿命预测可以基于蠕变速率最小值的位置，或者基于蠕变速率在失效过程中的时间依赖性，这可以用失效时间的有限时间奇点来数学描述。其他监测方法侧重于声发射速率的特征增加，或关注最终破坏面附近的损伤和变形活动的局部化。拟议的项目研究是否可以利用人工智能/机器学习方法从监测数据中获得残余样本寿命的改进预测，这些数据表征了蠕变过程中变形活动的时空演变。为此，我们将使用计算机生成的数据，通过模拟不同复杂程度的材料模型获得的数据，另一方面，通过对纸质样品进行连续蠕变测试获得的实验数据，并伴有声学和光学监测。在每种情况下，我们都要处理大量的数据集（通常是模拟样本的10000个，实验样本的100个），这些数据集描述了单个样本的蠕变历史。这些数据集的一部分用于训练所谓的神经网络，通过将蠕变活动的时空模式与预测的故障时间联系起来，来“预测”故障。然后使用剩余的数据来评估这些预测的质量，并将该方法与文献中描述的其他预测策略进行基准测试。

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.