To accurately predict quality loss of high reliability and long‐life product in service, a dynamic quality characteristics model is established based on linear degeneration and random error. According to this model, the expectation and variance of dynamic quality characteristics can be figured out. Moreover, assuming that the quality characteristics follow the normal distribution at the initial stage, the expectation of quality loss and the life distribution of dynamic quality characteristics follow three types are derived, such as L‐type (Larger is better), S‐type (Smaller is better), and N‐type (Nominal is better). According to the discount theory of quality loss, the present value model of dynamic quality loss based on the life is derived by combining life and the present value of dynamic quality loss. Then, this model is used to evaluate the quality loss of a GaAs laser and the rationality of the model is analyzed.
The improvement of mechanical parts inherent reliability has an impact on the reputation and performance of the company. To estimate the inherent reliability of products more conveniently and economically, a hidden quality cost‐production cost (HQC‐PC) reliability prediction model is put forward. To estimate the hidden quality cost (HQC) of products more accurately, a quadratic exponential quality loss function model is established, which is different from Taguchi's quadratic quality loss function (QLF) and the modified QLFs. In the new quality loss model, the growth rate of quality loss on both sides of the target value is considered. Under the condition that the quality characteristic value obeys normal distribution, the general estimation formulas of HQC in the tolerance range is obtained considering sampling error and the numerical model of inherent reliability is established. The effect of different parameters on the inherent reliability of products is discussed with practical case, such as design and production parameters. Then, the appropriate process capability index (PCI) is selected according to different production processes. The relationship between the HQC‐PC reliability prediction model and PCI is derived by a numerical model of inherent reliability. A new analysis method of inherent reliability is proposed.
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