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It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted. [ABSTRACT FROM AUTHOR]

Additively manufactured materials are gaining wide attention owing to the manufacturing benefits as it results in near net shape components. It is well known that the manufacturing processes affects the performance of the components via microstructural features and the mechanical properties. There is an urgent need to understand the processing-structure-property-performance relationship for the materials manufactures via such innovative techniques. Strategies are needed to quantify and modify the mechanical properties. This study assists to design and tailor the process parameters based on the final properties required. Current work predicts the yield strength of additively manufactured Ti-6Al-4V with different post heat treatments. A thermal model predicted by ABAQUS is fed into an implementation of Langmuir equation that predicts the chemistry which is then used in a phenomenological equation predicting the yield strength. The model is confirmed via experiments showing less than 2% deviation from the predicated properties. A statistical model gives design allowables that have an uncertainty of less than 1 ksi.

Multiple-site (fatigue) damage, (MSD) and its impact on the structural integrity (or safety of flight) of aging aircraft have been well recognized. Research to date has focused on fracture-mechanics-based analysis and experimentation for the consequences of MSD. The impact of corrosion on the early onset of MSD and the need for quantitative methodologies to predict the evolution and distribution of damage and MSD, on the other hand, are not fully appreciated. The mechanism for pitting corrosion in airframe aluminum alloys and the influence of pitting on the onset of fatigue cracking are briefly reviewed, and the influence of localized corrosion on the evolution of MSD is discussed. A mechanistically based probability model for corrosion and corrosion-enhanced fatigue crack growth and its application in predicting the probability of occurrence (PoO) of damage are summarized. The use of the PoO in a methodology to assess the onset and severity of MSD is demonstrated using teardown data from a Boeing 707 and two AT-38B aircraft.

A simple probability model helps demonstrate that the new probability approach is effective in determining corrosion fatigue crack growth and corrosion in aluminium alloys in aqueous medium. The probability approach combines the cumulative distribution function of each random variable into a mechanistic model that predicts the lifetime and durability of the aluminium alloys. The model simulations agree well with experimental measurements.

Probability analyses are increasingly being used for reliability and durability assessments for life prediction of engineered components and systems. Nevertheless, many of the current analyses are predominately statistical rather than probabilistic. Fatigue life prediction has historically been based on the safe-life or the crack growth approaches, both of which are empirically based. Consequently, they do not adequately reflect long-term operating conditions, or identify the sources and extent of their contributions to variability. A comparison between probability and statistical approaches for fatigue life prediction is developed herein. Using simple crack growth models, the variability inherent in S-N response can be related to key random variables that are readily identified in the models. The identification and quantification of these variables are paramount for predicting fatigue lives. The effectiveness of probability modeling compared to statistical methodologies is shown through the analysis of an extensive set of S-N data for 2024-T4 aluminum alloy. Subsequently, the probability approach is demonstrated with S-N data for SUJ2 steel, in which two distinct failure modes are operative. Variability associated with manufacturing and material variables are considered. The adoption of this technique to put life prediction on a sound scientific and probabilistic basis is recommended. [ABSTRACT FROM AUTHOR]