Abstract

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).

Highlights

  • 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 a broad range of applications

  • 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

  • We show some preliminary results from data from tensile test experiments and outline the challenges to derive such predictor functions by using artificial neural networks and Long-short-term Memory cells (LSTM)

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Summary

Introduction

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 a broad range of applications. The predictor functions should be able to predict irreversible effects like inelastic (plastic) behaviour and material damage by data measured from simple tensile tests within the elastic range of the materials. Artificial neural networks (ANN) are suitable models for time-series predictor functions [4] by using networks with a feedback loop from neuron outputs to input edges of neurons of the same or a previous layer, i.e., recurrent neural networks (RNN) Data series prediction can be performed with feed forward networks, too [8] Both approaches are compared in this work, The prediction of the plastic material behaviour in advance should be possible by sensor data from load tests acquired within the elastic material range only. The following sections introduce the methods, the model networks, the training and test techniques, followed by a discussion of analysis results from tensile tests

Aims and Methods
Experimental Data
Predictor Functions and Models
Training and Test
Results
Conclusion
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