Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

Introduction

Motivation

The objective of this work is the prediction of material behaviour changes (behaviour state transitions) and the occurence of material damages by data series prediction.

This work addresses data processing of measuring data from tensile tests.

Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

Testing Methods

• There is an emerging field of new materials, e.g., fibre-metal laminates, foam materials, and materials processed by additive manufacturing, highly related to a broad range of applications.

• Typically, material properties such as yield strength, inelastic behaviour, and damage are determined from tensile tests.

Destructive Tests (DT)

Tensile tests commonly destroy the specimen (non-reversible tests)!

Non-destructive Tests (NDT)

Monitoring and prediction without modifying the specimen (reversible tests)!

Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

Transition DT → NDT

• Materials basically pose two "behaviour ranges":

  • reversible, i.e., the elastic range;
  • non-reversible, i.e, the non-elastic/plastic range.

• Our aim is to enable a methodology shift from DT to NDT methods by using

  • Measuring data from tests without non-reversible material and specimen altering;
  • Machine Learning to predict material behaviour changes still in the non-reversible range

Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

Methods

Three different methods were used to predict the material behaviour of a device under test (DUT) from tensile test data ⟨F,x⟩ (F:load force, x: strain length):

1. Feed-forward Artficial Neural Networks (FFNN) prediciting the damage fracture point (break x_dam ∈ x, strain length) of DUT from the first data points F₀ of a tensile test, i.e., with data series F of the load forces

Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

2. State-based Recurrent ANN (RNN) perfoming the same prediction of the damage straing length point by early tensile data.

3. State-based RNN performing data series prediction, i.e., the force-strain curves from tensile tests to predict the start of the inelastic range of the material:

$$P(F(\delta,\vec{F_{i0}})): F_i \rightarrow F_{i+\delta}, \\ \vec{F_{i0}}=[F_j|j<i]$$

P is the predictor function hypothesis derived from ML and training

Stefan Bosse: Damage and Material-state Diagnostics with Predictor Functions

Experiments