Experimental and numerical studies on the repeated low-velocity impact response and damage accumulation in woven S2-glass fibre/epoxy composites

Fulginiti, F.; Rezasefat, Mohammad; Silva, A. Xavier da; Amico, Sandro Campos; Giglio, Marco; Manes, Andrea

doi:10.1080/09243046.2023.2178272

Cited by 1 publication

(1 citation statement)

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“…Several researchers have applied physics-based simulations, machine learning (ML), and data-driven techniques to investigate the mechanical behaviour of defect-and porecontaining parts [44][45][46][47][48][49]. Owing to the outstanding capabilities of ML models, handling large and complex datasets obtained from XCT or simulations of parts with pores is now possible.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components

Rezasefat,

Hogan

2023

Modelling

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Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes.

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