There is an emerging field of new materials, including, but not limited to, fibre-metal laminates, foam materials, and materials processed by additive manufacturing, highly related to space applications. Typically, material properties such as yield strength or inelastic behaviour are determined from tensile tests. The main disadvantage of tensile testing is the irreversible modification of the device under test (only one experiment possible!). We develop and investigate the training of approximating predictor functions by Machine Learning (ML) and simple Artificial Neural Networks (ANN) for inelastic and fatigue prediction by history recorded data. The predictor functions should be able to predict irreversible effects like inelastic behaviour and material damage by data measured from simple tensile tests within the elastic range of the materials. We show some preliminary results from a broad range of materials and outline the challenges to derive such predictor functions by using recurrent neural networks and Long-short-term Memory cells (LSTM). The neural network is activated by a linearized sequence of sensor samples measured either from laboratory tensile tests or by using strain-gauge and force sensors at run-time. The predictor functions outputs an extrapolation of the development of the measured variables (e.g., force, tension).
Damage and Material-state Diagnostics with Predictor Functions using Data Series Prediction and Artificial Neural Networks
Published: 14 November 2020 by MDPI in 7th International Electronic Conference on Sensors and Applications session Structural Health Monitoring Technologies and Sensor Networks
Keywords: Predictor Functions, Time-series prediction, Damage Diagnostics, Material behaviour prediction, LSTM, Recurrent Neural Networks