The discreteness is a common deficiency which impedes damage indices' practical application. It is caused by the arbitrary linear combination and some missing necessary variables. In order to deal with this proble...The discreteness is a common deficiency which impedes damage indices' practical application. It is caused by the arbitrary linear combination and some missing necessary variables. In order to deal with this problem,the correction term was added to the widely cited Park-Ang damage index. The unknown parameters in the correction term were obtained by the maximum likelihood estimation of these parameters' posterior distributions updated through Bayesian methodology. And the specimens for Bayesian updating were selected from PEER column database and Kawashima Earthquake Engineering Laboratory. Through model simplification process,a simplified modification was adopted,which has improved its goodness of fit to the experimental data. It has been found that the concrete core area and cross sectional area ratio,the longitudinal reinforcement ratio and the damage concentration factor are the critical variables affecting the error of the original damage index.展开更多
Several process parameters affect product reliability.Traditional reliability improvement methods primarily focus on maximizing product lifetime,often overlooking the variation in product lifetime.Manufacturers,howeve...Several process parameters affect product reliability.Traditional reliability improvement methods primarily focus on maximizing product lifetime,often overlooking the variation in product lifetime.Manufacturers,however,aim to produce products with minimal variations in their performance.Robust parameter design offers an effective strategy to help manufacturers enhance product reliability while reducing process variations.In practice,reliability experiments frequently involve constrained randomization due to the selection of specific experimental protocols.This study proposes a framework to achieve robust product reliability under a constrained randomization experiment.To consider random effects,we develop a Bayesian method-based Weibull non-linear mixed model for the lifetime response.Important factors are identified according to Bayesian posterior credible intervals.Subsequently,we propose an integrated multi-objective optimization model to determine the optimal factor levels.This model simultaneously considers minimizing the total costs of the manufacturer,maximizing the product lifetime,and minimizing the lifetime variance.An industrial thermostat experiment is conducted to validate the proposed method.Compared to existing approaches,the proposed method demonstrates superior performance in reducing variance and total cost,particularly for long warranty periods.Finally,we discuss the practical implications of the optimal solutions for manufacturers,finding that variations remain tolerable within a certain range.展开更多
基金Sponsored by the National Science and Technology Support Project of China(Grant No.2012BAK15B02)
文摘The discreteness is a common deficiency which impedes damage indices' practical application. It is caused by the arbitrary linear combination and some missing necessary variables. In order to deal with this problem,the correction term was added to the widely cited Park-Ang damage index. The unknown parameters in the correction term were obtained by the maximum likelihood estimation of these parameters' posterior distributions updated through Bayesian methodology. And the specimens for Bayesian updating were selected from PEER column database and Kawashima Earthquake Engineering Laboratory. Through model simplification process,a simplified modification was adopted,which has improved its goodness of fit to the experimental data. It has been found that the concrete core area and cross sectional area ratio,the longitudinal reinforcement ratio and the damage concentration factor are the critical variables affecting the error of the original damage index.
基金supported by the National Natural Science Foundation of China[grants numbers:72002066,71871204,72471172,and 71902138]。
文摘Several process parameters affect product reliability.Traditional reliability improvement methods primarily focus on maximizing product lifetime,often overlooking the variation in product lifetime.Manufacturers,however,aim to produce products with minimal variations in their performance.Robust parameter design offers an effective strategy to help manufacturers enhance product reliability while reducing process variations.In practice,reliability experiments frequently involve constrained randomization due to the selection of specific experimental protocols.This study proposes a framework to achieve robust product reliability under a constrained randomization experiment.To consider random effects,we develop a Bayesian method-based Weibull non-linear mixed model for the lifetime response.Important factors are identified according to Bayesian posterior credible intervals.Subsequently,we propose an integrated multi-objective optimization model to determine the optimal factor levels.This model simultaneously considers minimizing the total costs of the manufacturer,maximizing the product lifetime,and minimizing the lifetime variance.An industrial thermostat experiment is conducted to validate the proposed method.Compared to existing approaches,the proposed method demonstrates superior performance in reducing variance and total cost,particularly for long warranty periods.Finally,we discuss the practical implications of the optimal solutions for manufacturers,finding that variations remain tolerable within a certain range.
