Multivariate interpolation and machine learning models for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by SLM

Jan Horňas, Aleš Materna, Jonathan Glinz, Miroslav Yosifov, Sascha Senck

Research output: Contribution to journalArticlepeer-review

Abstract

The paper proposes a novel methodology for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by selective laser melting (SLM) technique. The introduced framework is represented by a traditional ranking method based on the three highest values of maximum stress intensity factor (Kmax) with related defect parameters (size, distance from the free surface, compactness and sphericity), training set augmentation using variational autoencoder (VAE) and optimized data-driven models. The defects were observed using micro-computed tomography (µ-CT) prior to the fatigue tests. As data-driven methods a multivariate interpolation and machine learning (ML) models were employed and tuned using Bayesian optimization algorithm called tree-structured Parzen estimator (TPE). The proposed methodology was validated on the test set and the highest prediction accuracy was achieved by random forest (RF) model with value of coefficient of determination Rtest2=0.956. Additionally, the SHapley Additive exPlanations (SHAP) analysis was conducted to gain a deeper insights of applied data-driven models.

Original languageEnglish
Article number110756
JournalEngineering Fracture Mechanics
Volume314
DOIs
Publication statusPublished - 7 Feb 2025

Keywords

  • Data augmentation
  • Fatigue life prediction
  • Machine learning (ML)
  • Multivariate interpolation
  • Selective laser melting (SLM)

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