Pre-loading on engineering materials or structures may produce pre-strain, especially plastic strain, which would change the fatigue failure mechanism during their service time. In this paper, an energy-based method for fatigue life prediction on high-strength-steel welded joints under different pre-strain levels was presented. Tensile pre-strain at three pre-strain levels of 0.2%, 0.35% and 0.5% was performed on the specimens of the material Q345, and the cyclic stress and strain responses with pre-loading were compared with those without pre-loading at the same strain level. The experimental work showed that the plastic strain energy density of pre-strained welded joints was enlarged, while the elastic strain energy density of pre-strained welded joints was reduced. Then, based on the strain energy density method, a fatigue life estimation model of the high-strength-steel welded joints in consideration of pre-straining was proposed. The predicted results agreed well with the test data. Finally, the validity of the developed model was verified by the experimental data from TWIP steel Fe-18 Mn and complex-phase steel CP800.

The reliability of a motion mechanism is affected by corrosion, wear, aging and other components’ performance degradations with the extension of service time. This paper tackles this problem by proposing a time-varying reliability analysis method for uncertain motion mechanisms. First, a model of motion mechanism error is constructed by assessing the difference between actual and expected motion. A time-varying reliability analysis method for a motion mechanism is proposed. The time-varying performance function is discretized into several static performance functions, which are further approximated with several normal variables. Then, the correlation coefficient matrix and probability density function of these normal variables are calculated, and the time-varying reliability of a motion mechanism is obtained via high-dimensional Gaussian integration. The study demonstrates that the proposed method successfully transforms the time-varying reliability problem into several time-invariant reliability problems for analysis, and handles the time-varying reliability problem of a nonlinear motion mechanism involving random variables and stochastic processes, and significantly increases the computational efficiency. Finally, the proposed method’s effectiveness is verified by two numerical examples and one practical engineering problem.