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Many structures are subjected to variable amplitude loading in engineering practice. The foundation of fatigue life prediction under variable amplitude loading is how to deal with the fatigue damage accumulation. A nonlinear fatigue damage accumulation model to consider the effects of load sequences was proposed in earlier literature, but the model cannot consider the load interaction effects, and sometimes it makes a major error. A modified nonlinear damage accumulation model is proposed in this paper to account for the load interaction effects. Experimental data of two metallic materials are used to validate the proposed model. The agreement between the model prediction and experimental data is observed, and the predictions by proposed model are more possibly in accordance with experimental data than that by primary model and Miner's rule. Comparison between the predicted cumulative damage by the proposed model and an existing model shows that the proposed model predictions can meet the accuracy requirement of the engineering project and it can be used to predict the fatigue life of welded aluminum alloy joint of Electric Multiple Units (EMU); meanwhile, the accuracy of approximation can be obtained from the proposed model though more simple computing process and less material parameters calling for extensive testing than the existing model.

Fatigue is a damage accumulation process in which the material property deteriorates and degenerates continuously under cyclic loading. The analysis of damage accumulation plays a key role in preventing the occurrence of fatigue failures, and the damage evolution mechanism is one of the important focuses of fatigue behavior. In this paper, a residual strength degradation model according to the stress-strength interference (SSI) model is introduced firstly; then a modified nonlinear fatigue damage accumulation model based on the Manson-Halford theory is proposed. Combining the proposed nonlinear damage accumulation model with the residual strength degradation model, a new method is developed for fatigue life prediction under constant and variable amplitude loading, which considers not only the effects of load interactions, but also the phenomenon of strength degradation of materials induced by loading history, and it can be used to predict the reliability and fatigue life of mechanical components. Moreover, the material parameter for the residual strength degradation can be obtained directly from S-N curve without running extra experiments. Finally, experimental data are used to compare with the predicted value in order to demonstrate the proposed residual strength degradation model. In addition, two sets of experimental data are also used to verify the proposed nonlinear fatigue damage accumulation model which is applied to predict fatigue life under two-stress level loading and reliability prediction under multi-stress level loading. The results show that the proposed method has a good agreement between the experimental data and predicted values.

Understanding the genetic influence on ECG time intervals and heart rate (HR) is important for identifying the genes underlying susceptibility to cardiac arrhythmias. The objective of this study was to determine the genetic influence on ECG parameters and their age-related changes in mice. ECGs were recorded in lead I on 8 males and 8 females from each of 28 inbred strains at the ages of 6, 12, and 18 mo. Significant interstrain differences in the P-R interval, QRS complex duration, and HR were found. Age-related changes in the P-R interval, QRS complex duration, and HR differed among strains. The P-R interval increased with age in 129S1/SvlmJ females. The QRS complex duration decreased with age in C57BR/J males and DBA2/J females but increased in NON/ShiLtJ females. HR decreased in C57L/J females and SM/J and P/J males but increased in BALB/cByJ males. Differences between males and females were found for HR in SJL/J mice and in the P-R interval in 129S1/SvlmJ mice. Broad-sense heritability estimates of ECG time intervals and HR ranged from 0.31 for the QRS complex duration to 0.52 for the P-R interval. Heritability estimates decreased with age for the P-R interval. Our study revealed that genetic factors play a significant role on cardiac conduction activity and age-related changes in ECG time intervals and HR.

Low cycle fatigue-creep is the main reason for the failures of many engineering components under high temperature and cyclic loading. Based on the exhaustion of the static toughness and dissipation of the plastic strain energy during fatigue failure, a new low cycle fatigue-creep life prediction model that is consistent with the fatigue-creep damage mechanism and sensitive to the fatigue damage process is presented in an attempt to develop viscosity-based approaches for general use in isothermal and thermo-mechanical loading. In this model, the theory of ductility exhaustion is used to describe the process of fatigue-creep interaction. It was assumed that the ductility exhaustion related only to the plastic strain and creep strain caused by tensile stress under stress-controlled conditions. In addition, the mechanisms of loading waveform, creep and mean stress effects were taken into account in a low cycle fatiguecreep regime. The predicted lives by the proposed model agree well with the reported experimental data from literature under different temperature loading conditions.

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O ur research focuses on the storage decision in a semi-automated storage system, where the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. These pods are transported by robotic drives to stationary stations on the boundary of the storage zone where associates conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. We characterize the maximum possible improvement from applying a velocity-based storage policy in comparison to the random storage policy. We show that class-based storage with two or three classes can achieve most of the potential benefits and that these benefits increase with greater variation in the pod velocities. To validate the findings, we build a discrete-time simulator with real industry data. We observe an 8% to 10% reduction in the travel distance with a 2-class or 3-class storage policy, depending on the parameter settings. From a sensitivity analysis, we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings, including different zoning strategies, resource utilization, and space utilization levels.Note. Small dots are storage locations, larger dots on boundary are stations for picking and stowing.