Predicting rolling contact fatigue crack hot spots or regions with increased local driving forces in rails is challenging due to the wide range of factors that influence crack initiation. Rail sections experience fluctuating creepage conditions, contact positions, and loads throughout their lifespan, influencing the development and location of fatigue cracks. A new computational method is proposed that predicts the orientation and regions prone to rolling contact fatigue cracks under realistic service loading. It combines multi-body simulations, finite element analysis, and critical plane approaches. A novel multi-variable sampling technique simplifies loading spectra into representative traction profiles, which are then analyzed using finite element analysis and the Smith–Watson–Topper damage indicator parameter (DIP<inf>SWT</inf>). The maximum DIP<inf>SWT</inf> value identifies the critical plane and potential crack orientation. A case study on the Swedish heavy haul train line (Malmbanan) considers measured traffic and loading conditions, analyzing the wheel load spectrum for a 384 m long section of a R = 450 m curve. Results show that the DIP<inf>SWT</inf> is highest for the locomotive with a loaded payload configuration, with a maximum value of 3.84 × 10⁻⁸ located at 38.59 mm from the lower gauge face corner. The DIP<inf>SWT</inf> critical plane aligns with experimental measurements of RCF cracks orientations near the gauge corner. This computational method, when combined with other predictive tools, can efficiently identify conditions that lead to RCF cracks and determine their possible locations and orientations in railway tracks.

This work investigates the influence of a single tensile preload, applied prior to fatigue testing, on the fatigue strength of 316L stainless steel parts manufactured using laser-based powder bed fusion (PBF-LB) with a porosity of up to 4 %. The specimens were produced in both the horizontal and vertical build directions and were optionally preloaded to 85 % and 110 % of the yield strength before conducting the fatigue tests. The results indicate a clear tendency of improved fatigue life and fatigue limit with increasing overload in both cases. The fatigue limits increased by 25.8 % and 24.6 % for the horizontally and vertically built specimens, respectively. Extensive modelling and experiments confirmed that there was no significant alteration in the shape and size of the porosity before and after preloading. Therefore, the observed enhancement in fatigue performance was primarily attributed to the imposed local compressive residual stresses around the defects.

A material model for predicting fatigue related changes due to shot peening is developed. The model includes plastic behaviour at large strains and transformation of retained austenite. The model is simplified to limit the amount of material parameters needed and an iterative approach is used to fit the parameters to experimental data in the form of uniaxial tests on through hardened specimens, single impacts of carbide balls on polished case hardened test plates and on shot peened, ground test plates. With good accuracy, reasonable computer resources and basic experimental techniques to fit the model parameters, the model can predict; residual stresses, retained austenite transformation and work hardening in form of Vickers hardness increase. The model is also adequate for parameter studies as exemplified by changing media hardness, coverage or media shape variations. For surface roughness predictions, the initial surface roughness of the target must be included and future work is needed there.

A new multiscale computational method predicts RCF crack formation in rails by integrating multi-body simulations, finite-element methods, and critical plane approaches. Multi-variable modal value sampling is introduced to reduce the computational expense of large-scale FE studies by capturing multiple wheel passes in a single loading profile. The Smith-Watson-Topper (SWT) critical plane fatigue damage parameter is selected to capture the non-proportional multiaxial load history drivers for RCF formation. The critical plane fatigue damage parameter matches the experimentally observed location and orientation of RCF cracks, highlighting the applicability of this new method for future RCF assessments.

An analytical model for the fatigue probability of abrasive waterjet cut high strength steel as a function of surface roughness, surface residual stress, tensile strength and number of cycles to failure is presented. Based on the model, which is valid in the finite and infinite-life high cycle fatigue regime, the influence of the aforementioned parameters on the fatigue strength at different probability levels is studied. For validation, fatigue tests are performed on abrasive waterjet-cut dog-bone specimens manufactured from high-strength steel with a yield strength of 700 MPa. Residual stresses are measured parallel to the loading direction at the inlet, middle and outlet of the cut surface. Surface roughnesses are measured with laser line triangulation as well as a traditional contact stylus method, showing good agreement between both measurement techniques. The proposed probabilistic model shows good agreement with the experimental results with less than 4% error in the predicted mean fatigue limit. Furthermore, the applicability of the presented analytical expression in a probabilistic design framework is demonstrated. An engineering example is introduced demonstrating the implementation of the model in a finite-element simulation, accounting for both multiaxial loading and the statistical size effect. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Second-order reliability methods are commonly used for the computation of reliability, defined as the probability of satisfying an intended function in the presence of uncertainties. These methods can achieve highly accurate reliability predictions owing to a second-order approximation of the limit-state function around the Most Probable Point of failure. Although numerous formulations have been developed, the lack of full-scale comparative studies has led to a dubiety regarding the selection of a suitable method for a specific reliability analysis problem. In this study, the performance of commonly used second-order reliability methods is assessed based on the problem scale, curvatures at the Most Probable Point of failure, first-order reliability index, and limit-state contour. The assessment is based on three performance metrics: capability, accuracy, and robustness. The capability is a measure of the ability of a method to compute feasible probabilities, i.e., probabilities between 0 and 1. The accuracy and robustness are quantified based on the mean and standard deviation of relative errors with respect to exact reliabilities, respectively. This study not only provides a review of classical and novel second-order reliability methods, but also gives an insight on the selection of an appropriate reliability method for a given engineering application.

Design and verification of gasket elements between engine mounted components requires computation and physical tests with respect to wear. Wear is a common problem in engines today and mainly comes from engine vibrations and thermal loading. The vibrations are due to inertial loads as well as reaction forces to gas pressure in the cylinders. Here, a new method to correctly simulate the measured wear rate of an oil pan rubber gasket is described. The engine motion is derived directly from measurements and the resulting simulation process has a high efficacy. Much work exists on investigations of gasket sealings between cylinder head and block, where the thermal loading becomes very important. The method described herein, focuses instead on other types of gaskets on the engine, where the main failure mode is due to sliding wear caused by the engine block and component vibrations.

Reliability-based design optimization (RBDO) aims at minimizing a function of probabilistic design variables, given a maximum allowed probability of failure. The most efficient methods available for solving moderately nonlinear problems are single loop single vector (SLSV) algorithms that use a first-order approximation of the probability of failure in order to rewrite the inherently nested structure of the loop into a more efficient single loop algorithm. The research presented in this paper takes off from the fundamental idea of this algorithm. An augmented SLSV algorithm is proposed that increases the rate of convergence by making nonlinear approximations of the constraints. The nonlinear approximations are constructed in the following way: first, the SLSV experiments are performed. The gradient of the performance function is known, as well as an estimate of the most probable failure point (MPP). Then, one extra experiment, a probe point, per performance function is conducted at the first estimate of the MPP. The gradient of each performance function is not updated but the probe point facilitates the use of a natural cubic spline as an approximation of an augmented MPP estimate. The SLSV algorithm using probing (SLSVP) also incorporates a simple and effective move limit (ML) strategy that also minimizes the heuristics needed for initiating the optimization algorithm. The size of the forward finite difference design of experiment (DOE) is scaled proportionally with the change of the ML and so is the relative position of the MPP estimate at the current iteration. Benchmark comparisons against results taken from the literature show that the SLSVP algorithm is more efficient than other established RBDO algorithms and converge in situations where the SLSV algorithm fails.

Internal combustion engines (ICE) are sources of vibration in many applications. Engine mounted components need to be designed with respect to fatigue, to withstand these vibrations. In order to compute an estimate of the fatigue life of a component, the vibration load needs to be known. A methodology that is based on a seven degree of freedom model to represent the engine block is proposed. By fitting measured accelerometer data to this model, acceleration levels in arbitrary positions and directions, related to the engine block, may be computed. Results presented for an engine vibration measurement show good agreement between measured and computed vibration levels. A measure to find erroneous accelerometer definitions is also proposed and exemplified through measurement results. The method also enables pointing out local dynamic phenomena at measurement points.

In the Second-Order Reliability Method, the limit-state function is approximated by a hyper-parabola in standard normal and uncorrelated space. However, there is no exact closed form expression for the probability of failure based on a hyper-parabolic limit-state function and the existing approximate formulas in the literature have been shown to have major drawbacks. Furthermore, in applications such as Reliability-based Design Optimization, analytical expressions, not only for the probability of failure but also for probabilistic sensitivities, are highly desirable for efficiency reasons. In this paper, a novel Second-Order Reliability Method is presented. The proposed expression is a function of three statistical measures: the Cornell Reliability Index, the skewness and the Kurtosis of the hyper-parabola. These statistical measures are functions of the First-Order Reliability Index and the curvatures at the Most Probable Point. Furthermore, analytical sensitivities with respect to mean values of random variables and deterministic variables are presented. The sensitivities can be seen as the product of the sensitivities computed using the First-Order Reliability Method and a correction factor. The proposed expressions are studied and their applicability to Reliability-based Design Optimization is demonstrated.