Fatigue strength evaluation of PPGF35 by energy approach during mechanical tests

Risitano, Giacomo

doi:10.3221/igf-esis.59.35

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…If the limit stress, assessed by the STM, is applied in a cyclical way to the specimens it would reach fatigue failure. The assessment of the limit stress has been performed by making the linear regression of the linear part (Phase I) and plateau region (Phase II) of the temperature trend; based on the ability of the operator to recognize the different phases of the temperature signal [12,13] However, Machine Learning (ML) algorithms can be a useful aid to automatically assess the value of the limit stress by analysing the time vs temperature vs applied stress signal obtained during a static tensile test. Within the ML algorithms, Time Series Forecasting (TSF) is an entry point and can be applied to many sectors, ranging from weather forecasts to trends in economic indicators [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning algorithm for the assessment of the first damage initiation monitoring the energy release of materials

Milone

Santonocito

2022

Frattura Ed Integrità Strutturale

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Monitoring the energy release during fatigue tests of common engineering materials has been shown to give relevant information on fatigue properties, reducing the testing time and material consumption. During a static tensile test, it is possible to assess two distinct phases: In the first phase (Phase I), where all the crystals are elastically stressed, the temperature trend follows the linear thermoelastic law; while, in the second phase (Phase II), some crystals begin to deform, and the temperature assumes a non-linear trend. The macroscopic transition stress between Phase I and Phase II could be related to the “limit stress” that, if cyclically applied, would lead to material failure. Nowadays, it is impossible to distinguish the transition between Phase I and Phase II in an objective way. Indeed, it is up to the operator's experiences. This work aims to create a universal methodology that predicts the limit stress by assessing the change in temperature trend by adopting Neural Networks. A Deep Learning algorithm has been created and trained on experimental data coming from static tensile tests performed on several classes of materials (steels, plastics, composite materials). Once trained, the network can predict the transition temperature at which the first plastic deformation occurs within the material.

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Section: Introductionmentioning

confidence: 99%