Very-high-cycle fatigue (VHCF) failure behavior of GH4169 superalloy fabricated via powder bed fusion using laser beam (PBF-LB/GH4169) at elevated temperatures remains incompletely understood, thus delaying its practical application in industrial fields. To address this critical gap, we apply a multiscale characterization framework integrating ultrasonic fatigue testing at 650 °C, fracture analysis, and high-resolution transmission electron microscopy to directly uncover the elevated temperature fatigue failure mechanism. Notably, we demonstrate a fundamental shift from an internal-defect dominant mode to a highly competitive crack initiation mechanism involving both surface indentations and internal pores. Furthermore, a critical oxidation-fatigue interaction is clarified: high-temperature oxidation alters the early damage evolution process of surface crack initiation, enabling surface-governed failures to reach the VHCF regime. Furthermore, this study provides direct evidence for a consistent local microstructural evolution process in the internal pore-induced VHCF crack-initiation region of PBF-LB/GH4169 at both room temperature and 650 °C. Driven by highly constrained cyclic plasticity associated with internal pore defects, dislocation proliferation occurs; subsequently, the shearing of precipitate phases by mobile dislocations leads to their dissolution, triggering continuous dynamic recrystallization (CDRX) and the formation of fine grains, within whose boundaries microcracks ultimately nucleate. Crucially, this localized behavior accelerates nanoscale (Ti,Nb)C precipitation, as well as the detrimental transformation of the strengthening γ′′ phase into the brittle δ phase. Collectively, these findings establish a unified mechanistic understanding of elevated temperature VHCF failure in PBF-LB/GH4169, directly informing synergistic processing strategies, specifically eliminating internal pores and enhancing surface oxidation resistance to mitigate fatigue in extreme environments.

Lattice structures realized through additive manufacturing have garnered increasing interest within both academia and industry in recent years. Various factors, including unit cell topology, base material, heat treatments, and relative density, significantly influence the overall behaviour of these architectured structures. This study specifically examines the compressive mechanical behaviour of solid-based gyroid lattices made of Ti6Al4V alloy through Laser Powder Bed Fusion (PBF-LB) technique. Specimens with four different relative densities were produced to investigate the impact of this parameter on the compressive behaviour (quasi-static and fatigue); furthermore, each relative density category included two sets of specimens to evaluate the effect of annealing and Hot Isostatic Pressing (HIP) as post-processing techniques. Micro-CT scans, microstructural, postmortem and finite element analyses were included to further evaluate the failure mechanisms and explain the observed experimental results. Furthermore, the behaviour documented in the present analysis has been correlated with a wide fatigue dataset retrieved from literature in an effort to dig deeper into the behaviour of these structures. The results, together with the retrieved dataset, allowed for a more comprehensive understanding also considering aspects such as yielding effect, surface roughness and notch mechanics. It has been proved that the use of optimized process parameters and cheaper heat treatments is able to match the beneficial effects expected by HIP. Furthermore, easy-to-use methodologies to account for the reduction in strength due to the change in relative density presented in the literature, such as effective and normalized stress, have been considered to evaluate their accuracy, but also their limitations.

When designing mechanical components, their functional requirements often lead to geometrical discontinuities with severe stress concentrations and gradients. These discontinuities, known as notches, can markedly reduce the structural reliability and fatigue strength of components. Depending on the notch severity, conventional point-based approaches may significantly overestimate their detrimental effects on fatigue behaviour. Notches are generally classified as blunt or sharp with the fatigue behaviour of the sharp ones not accurately captured by point-based approaches. Numerous studies have attempted to define the transition between these two behaviour and to develop design methodologies capable of consistently addressing both. Among these, the averaged Strain Energy Density (SED) method has demonstrated high accuracy and robustness for both blunt and sharp notches. In this work, the SED method is employed to identify a limiting condition, expressed through a limit notch radius, rho limit, that distinguishes between blunt and sharp notches. This condition is investigated through numerical simulations and validated against an extensive fatigue database from the literature. Defining the limit condition as a notch radius simplifies components design and may also serve as guideline for determining the required notches tolerances. Finally, a methodology is proposed for fatigue-oriented material selection, coupling bulk material properties, component geometry and notch sensitivity. Indeed, in fatigue design, the highest-performing component is not necessarily obtained using the material with the highest intrinsic fatigue strength. For sharp notches, materials with lower intrinsic fatigue strength, but reduced notch sensitivity, can indeed yield superior fatigue performance. The methodology can be readily extended to lightweight design applications.

Compact-tension (CT) specimens of the equiatomic Co–Cr–Fe–Mn–Ni high-entropy (Cantor) alloy were cycled to failure and the fracture surfaces examined by scanning electron microscopy. The surface separates into a fatigue-propagation region and a final overload zone. The fatigue region is predominantly transgranular and exhibits a tortuous, step-rich morphology with short secondary cracks—features consistent with heterogeneous slip in a low-stacking-fault-energy FCC alloy and with roughness-induced deflection. Localized parallel markings compatible with striation-like features appear in select high-magnification areas, although continuous periodic striations are not uniformly resolved. Final fracture proceeds by ductile microvoid coalescence, producing a dimpled morphology with occasional particle imprints at dimple bases. No pervasive intergranular decohesion is observed. Taken together, these observations indicate that fatigue-crack advance reflects a synergy of intrinsic crack-tip plasticity and extrinsic shielding that promotes crack-path tortuosity. Crack-closure was not quantified; interpretations are strictly fractography-based.

This work investigates the role of manufacturing-induced crystallinity on the fatigue behavior of Polyamide 12 (PA12), with particular emphasis on the role of temperature in fatigue performance. Specimens produced by Multi Jet Fusion (MJF) are compared with hot-pressed (HP) counterparts manufactured from the same powder batch under controlled cooling conditions. An extended cooling (EC) protocol is introduced to tailor crystallinity by prolonging the residence time within the crystallization window. Comprehensive thermal, microstructural, and mechanical characterization is combined with fatigue testing and in-situ infrared thermography. Results show that higher crystallinity significantly reduces viscoelastic dissipation and self-heating, leading to improved fatigue resistance. Despite lower defect content, standard HP specimens exhibit inferior fatigue performance compared to MJF due to reduced crystallinity. The EC protocol effectively bridges this gap, enhancing fatigue life and matching the MJF fatigue performance. Based on these findings, optimized cooling parameters are proposed and experimentally validated, demonstrating that crystallinity control is an effective process-level strategy to improve the fatigue performance of PA12.

Fused Deposition Modeling (FDM) is currently the most accessible additive manufacturing technology due to its affordability and the simplicity of its machinery, coupled with the low cost and widespread availability of thermoplastic polymers. In this study, the mechanical properties of virgin thermoplastic materials (PLA and PETG) under bending and dynamic tests, as well as the impact of their carbon fiber reinforcement are investigated. All samples were manufactured under the same conditions using the same FDM machine. A study is also made on the tensile properties of the material wires, studying their morphology in detail in order to highlight possible signs that would reveal the nature of the influence of fiber reinforcement on the overall mechanical behavior of the printed components. The experimental results revealed contrasting effects of carbon fiber reinforcement on PLA and PETG. PLA-CF exhibited microcrack formation (crazing), which led to increased ductility but also to a decrease in tensile strength, flexural strength, and fracture toughness, indicating a general embrittlement of the composite. In contrast, PETG-CF showed improved mechanical and fracture properties compared to plain PETG, most likely due to better interfacial adhesion between the fibers and the polymer matrix, although with a more brittle behavior.

The industrial sector continues to explore innovative strategies to exploit the full potential of Additive Manufacturing (AM). Among its many advantages, AM enables the fabrication of lattice structures; these are lightweight metamaterials with tunable mechanical properties and excellent energy absorption capabilities. Despite their promise, the widespread industrial use of such structures is limited by the difficulty in accurately assessing their fatigue behavior. This study presents a methodology aimed at predicting the fatigue life of polymer-based lattice components, with a specific focus on PA12 manufactured using the Multi Jet Fusion (MJF) process. This is an industrially relevant technology offering large production volumes, high printing quality and low production costs. The approach begins with fatigue testing of bulk PA12 specimens to establish baseline material behavior. Based on these results, a predictive algorithm is developed to estimate the fatigue performance of lattice structures. The model adopts an energy-based framework inspired by the Average Strain Energy Density (ASED) method, previously used for metallic materials, and adapts it to the characteristics of polymer lattices. The proposed methodology contributes to the development of efficient fatigue assessment tools, supporting the broader adoption of lattice structures in cost-sensitive industrial applications where polymer-based materials are effective.

Additive Manufacturing allows the fabrication of complex shape parts, but the manufacturing processes may introduce defects that undermine the mechanical performance. In the presented study, the influence of selected process parameters and testing conditions on the fatigue life of Ti6Al4V alloys produced by Electron Beam-Powder Bed Fusion (EB-PBF) has been studied. To study the effects of process parameters, the EB-PBF machine was operated in manual mode, bypassing proprietary manufacturing algorithms. Moreover, various load ratios were considered for fatigue tests conducted under constant-amplitude loads. The results established a direct relationship between energy density, microstructure, and fatigue resistance. It was found that the beam current and the scan speed significantly determined the energy density of the process and, consequently, the material porosity and density, so affecting the mechanical performance. These findings indicate that careful selection and control of manufacturing parameters can enhance fatigue properties in EB-PBF components. This information can be used to support the development of AI-based optimisation strategies to predict stress-related behaviour and minimise defects, such as cracking and residual stress.

In this study, the micromechanical response of a representative volume element (RVE) under cyclic loading was simulated using the crystal plasticity finite element method (CPFEM) to obtain the local stress-strain response and accumulated plastic strain. Based on the high-fidelity data generated by CPFEM, an incremental neural network (INN) model was constructed. The INN model takes the load ratio and the current accumulated plastic strain as inputs to predict the corresponding accumulated plastic strain increment for a given number of cycles. Compared with traditional fatigue prediction models, this model does not require presetting empirical equations. The results demonstrate that this incremental learning approach can effectively capture the nonlinear evolution of plastic strain with the number of cycles. The developed single-hidden-layer INN model accurately predicts the plastic strain accumulation process in laser powder bed fusion (LPBF) GH4169 (Inconel 718) under cyclic loading and achieves the highest prediction accuracy.

Fatigue is the dominant failure mechanism in engineering components subjected to cyclic loading, particularly in heavy-duty automotive systems where structural reliability is critical. Understanding the fatigue behavior in components is essential for improving design and service performance. In this study, failure analysis was conducted on two drivetrain components, a drive shaft (Case I) and a gearwheel (Case II), extracted from a heavy-duty truck. Each case was investigated using macroscopic examination and fractographic analysis. The results showed that fatigue was the primary cause of failure. In Case I, fatigue behavior was expected because the rotating shaft experienced cyclic bending stresses during service loading. Crack initiation occurred at the shaft periphery under rotating-bending conditions and propagated progressively, accounting for most of the fracture surface. The final overload region was small ( 9

The ideal sintering behaviours as a semi-crystalline thermoplastic polymer makes polyamide 12 (PA12) the most widely used material for Selective Laser Sintering (SLS) printing technique. However, the presence of defects, such as partially melted particles, compromise the structural integrity of the printed components influencing their mechanical behaviours especially in terms of fatigue life. Estimating fatigue properties is a resource-intensive process, both in terms of material and time, particularly within the rapidly evolving AM industry. If the desired properties are not achieved, the manufacturing process must be restarted with adjustments to one or more printing parameters. In this context, the need to rapidly verify the mechanical properties of components has become increasingly critical. Over the years, numerous energy-based methods have been developed to expedite the study of fatigue properties in materials, thanks to the dissipative nature of the fatigue process. Among these, the Thermographic Methods (TMs) have shown simplicity of application and rapidity to obtain results. In this work the mechanical properties of PA12 specimens obtained with SLS technique have been investigated using the Risitano’s Thermographic Method (RTM) and the Static Thermographic Method (STM). The influence of the printing direction has been analysed testing two sets of specimen’s configurations. The difference in terms of energetic release during quasi-static and fatigue tests for both configurations have been highlighted and discussed. The study demonstrates the potential of thermography as a technique for evaluating the fatigue life of polymeric materials produced by SLS, opening new perspectives for quality control and optimization of AM production processes.

This study examines the fatigue failure behavior of Unequal Thickness Butt Welded Joint (UT-BWJ) in structural steel applications for construction machinery. The research combines experimental fatigue testing with Finite Element Analysis (FEA) to evaluate UT-BWJs using both structural and local assessment methodologies. Three distinct configurations of longitudinal load-carrying BWJs were analyzed to quantify their fatigue strength under cyclic tensile loading conditions. The investigation systematically evaluates the fatigue characteristics of UT-BWJs through comprehensive local and structural assessment approaches. The obtained fatigue strength was benchmarked against international standard codes and existing reference data. Additionally, multiple three-dimensional Finite Element (FE) models were established to simulate UT-BWJs with backing bars. The relationship between critical geometric parameters and fatigue performance was identified, and the fatigue loading capacity of these welded joints was evaluated by means of nominal stress, structural, and local approaches. Results indicate that fatigue life can be predicted using Strain Energy Density (SED) theory, and analytical solutions for predicting fatigue characteristics in UT-BWJs were proposed, demonstrating the accuracy of the estimation model. The results provide valuable insights for fatigue strength and design optimization of unequal thickness welded connections in structural applications.

The corrosion-fatigue behaviour of laser powder bed fusion (L-PBF) Ti-6Al-4V lattice struts was investigated with particular emphasis on the role of manufacturing-induced geometrical imperfections. Thin strut specimens representing strut-based lattice sub-unit elements were built at a 60 degrees orientation and tested under tension-tension loading (R = 0.1) in laboratory air and in phosphate-buffered saline (PBS) at 37 degrees C. Micro-computed tomography (Micro-CT) was employed to quantify surface roughness, geometrical deviations, and the interaction between surface valleys and near-surface porosity. Quasi-static tensile tests showed limited scatter in strength and ductility, indicating that monotonic behaviour is governed by global geometry. In contrast, fatigue performance was strongly defect-sensitive. While comparable fatigue strength was observed in air and PBS in the lowcycle regime, exposure to PBS led to a marked reduction in high-cycle fatigue strength, reaching approximately 25% at 106 cycles. Fractographic and EDXS analyses revealed that fatigue cracks initiated at surface-connected valleys in both environments, whereas the physiological environment primarily accelerated crack propagation through corrosion-assisted mechanisms and suppressed crack branching. A micro-CT-based deepest-valley analysis showed good agreement between predicted critical defects and experimental failure locations. The results highlight the dominant role of extreme surface geometrical imperfections and environment-assisted crack growth in the fatigue behaviour of L-PBF Ti-6Al-4V lattice structures.

The fatigue life prediction of additively manufactured AlSi10Mg alloys under complex loading, particularly under conditions involving both low-cycle fatigue (LCF) and progressive strain accumulation (ratcheting), remains a significant challenge. This study presents a comprehensive experimental and theoretical investigation to address this issue. The experimental campaign included LCF and ratcheting tests performed on AlSi10Mg specimens fabricated via Selective Laser Melting using two different laser powers (300 W and 175 W). Postmortem fractographic and tomographic analyses were conducted to identify the underlying failure mechanisms. A novel, path-dependent fatigue damage model is proposed to capture the observed behaviour. The core of the model is a dynamic memory surface in plastic strain space that governs damage accumulation. This surface evolves through both expansions, to record overloads, and contraction, to model the "fading memory" of prior load history. The framework integrates the Smith-Watson-Topper parameter for LCF damage with a new ratcheting damage formulation directly linked to the evolution of the memory surface size. A modified summation rule is introduced to account for the effect of compressive ratcheting. A robust, non-iterative calibration procedure, which decouples the initial damage from standard LCF tests, is also presented. The proposed model demonstrates significantly improved predictive accuracy for complex; non-stationary loading histories compared to both a simple linear summation rule and the original competitive model. The memory contraction mechanism is shown to be decisive for accurately predicting fatigue life under variableamplitude loading. Furthermore, the 175 W material variant exhibits superior resistance to ratcheting due to its pronounced cyclic hardening behaviour, which represents a key finding for process optimization.

Energy-absorbing architected metamaterials, featuring dissimilar sub-elements arranged in deliberate patterns, can achieve a notably wider array of mechanical properties compared to their uniform counterparts. The traditional design of these heterogeneous structures typically depends on expert knowledge and requires considerable trial-and-error effort. Here, we introduce a data-efficient approach for the inverse multi-objective design of high-energy absorbing, three-dimensional, heterogeneous mechanical metamaterials comprised of the combination of two distinct plate-based unit cell topologies. This approach proposes a framework that pairs a Deep Neural Network (DNN) with a Genetic Algorithm (GA), supported by finite element (FE) simulations, to inverse design heterogeneous metamaterials with tailored Young's modulus (E), while maximizing energy absorption capacity and minimizing relative density (rho). We applied this method to orthopaedic implants, as a case study, to design structures with a desirable biocompatible elastic modulus, enhanced energy absorption efficiency and minimized rho. To the best of our knowledge, this is the first inverse design framework that integrates clustering-aware deep neural networks with evolutionary optimization, enabling accurate and efficient design of heterogeneous plate-based lattices with tailored mechanical performance.

Additive manufacturing (AM) has emerged as a promising alternative to conventional manufacturing techniques. Among the various AM processes, laser powder bed fusion (L-PBF) has gained significant attention for fabricating metallic materials, particularly Ti-based alloys. Among these, Ti-6Al-4V stands out as one of the most widely used alloys in high-tech industries. Despite its advantages, L-PBF of Ti-6Al-4V faces several challenges commonly associated with this technique, including internal defects, poor surface quality, metastable microstructures, and high residual stresses. These factors, along with some others e.g. build orientation, complex geometries, irregular and reused powder feedstock, can significantly impact the fatigue performance of this material under complex dynamic loading conditions. This review examines how L-PBF process parameters and post-processing strategies can be tailored to control these factors and systematically elucidates the critical role of each of them on fatigue behavior. Furthermore, this work addresses key fatigue mechanisms and reviews fatigue life prediction approaches ranging from conventional methods to emerging data-driven techniques such as machine learning, alongside modeling strategies for realistic L-PBF Ti-6Al-4V components. The insights presented here offer valuable guidance for future research and technological advancements in this field.

The very-high-cycle fatigue (VHCF) failure behavior of laser powder bed fusion (LPBF) GH4169 superalloy at elevated temperatures remains incompletely understood, thus delaying its practical application in industrial fields. To address this critical gap, we apply a multiscale characterization framework integrating ultrasonic fatigue testing at 650 ℃, fracture analysis, and high-resolution transmission electron microscopy to directly uncover the elevated temperature fatigue failure mechanism. Notably, we demonstrate a fundamental shift from an internal-defect dominant mode to a highly competitive dual-channel crack initiation mechanism involving both surface indentations and internal pores. Furthermore, a critical oxidation-fatigue interaction is clarified: high-temperature oxidation alters the early damage evolution process of surface crack initiation, enabling surface-governed failures to reach the VHCF regime. Furthermore, this study presents a unified mechanistic model describing the microstructural evolution governing VHCF crack initiation in LPBF GH4169 at both room and elevated temperatures for the first time. Driven by highly constrained cyclic plasticity associated with internal pore defects, dislocation proliferation occurs; subsequently, the shearing of precipitate phases by mobile dislocations leads to their dissolution, triggering continuous dynamic recrystallization (CDRX) and the formation of fine grains, within whose boundaries microcracks ultimately nucleate. Crucially, this localized behavior accelerates nanoscale (Ti, Nb)C precipitation, as well as the detrimental transformation of the strengthening γ′′ phase into the brittle δ phase. Collectively, these findings establish a unified mechanistic understanding of elevated temperature VHCF failure in LPBF GH4169, directly informing synergistic processing strategies, specifically eliminating internal pores and enhancing surface oxidation resistance to mitigate fatigue in extreme environments.

The present paper presents two novel data-driven topology optimization (TO) procedures to design lighter additively manufactured (AM) fatigue resistant components. The first TO method is driven by a probabilistic machine learning (ML) algorithm based on a Bayesian Neural Network (BNN), trained on fatigue data from the literature to assess probabilistic stress-life (PSN) curves. These curves are used to predict the allowable design stress for TO and are predicted directly from AM process parameters, the risk volume, and thermal and surface treatments. The second TO design procedure is instead driven by another BNN, trained to predict the maximum critical defect size from the process parameters. The TO limit stress is computed from the predicted critical defect and the threshold stress intensity factor Kth. After the TO, the critical stress intensity factor KI in the component is computed and compared against Kth, to assess the effectiveness of this design procedure. These two frameworks are applied to the design of an SS316L automotive suspension lower control arm and a Ti6Al4V aerospace bracket, respectively. With the following framework, the limit stress calculation does not require specifically designed experimental campaigns and prototyping, as previously sparse experimental knowledge can be embedded in a powerful design tool, which allows for preventing fatigue failures, while accounting directly for the influence of the AM process parameters.

The limited and scattered fatigue performances and their difficult predictability remain critical barriers for the widespread adoption of Laser-based Powder Bed Fusion (L-PBF) metamaterials in engineering applications, as fatigue damage initiation is highly sensitive to manufacturing-induced geometric imperfections. While X-ray computed tomography (CT) provides high-fidelity as-built reconstructions fundamental for metamaterials’ structural health monitoring, its cost and complexity hinder routine integration into fatigue assessment workflows at the design stage. In this work, we propose a computationally efficient framework for the development of synthetic as-built CAD models, serving as digital twins for fatigue life and failure location prediction. The proposed model is herein reported for L-PBF Ti-6Al-4V struts, the elemental building blocks of metamaterial architectures, manufactured at different building orientations. Leveraging stereomicroscopy input images, a modular reconstruction pipeline capturing orientation-dependent surface morphology and partially fused particles allows the generation of as-built CAD models that retain the geometric variability governing fatigue behaviour, without reliance on volumetric imaging. Synthetic models are coupled with finite element analyses and a statistical strain energy density criterion to identify failure-critical locations. Validation against CT-derived counterparts demonstrates close morphological agreement and, since the design stage, the ability to estimate fatigue life and predict experimental failure locations within established scatter bands.