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- 저자 : Xianzhen Huang (검색결과 2 건)
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Reliability optimization of cutting parameters considering the diameter error of slender shaft
Pengfei Ding,Xianzhen Huang,Yuxiong Li,Guodong Yang 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.10
In slender shaft turning, any diameter error in the workpieces can cause cutting tool wear and poor machining accuracy. Published research ignores the integrated analysis of diameter error, the randomness of parameters, and optimization models. This paper sets material removal rate (MRR) as the optimization objective function and considers the randomness of cutting parameters in a reliability parameter optimization model design, under the constraint of diameter error. The cutting force is calculated based on the unequal shear zone model and is used in finite element analysis of the slender shaft, deriving the diameter error model. The derived complex error model is replaced by the Kriging fitting method, reducing the calculation time to less than 1 %. Single loop sequence optimization and reliability assessment (SORA) is used to optimize reliability. The results show significant improvement of the MRR, while the reliability of each constraint condition is close to 1.
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Structural reliability updating using experimental data
Lisha Zhu,Xianzhen Huang,Cong Yuan,Zunling Du 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.1
Conventional reliability analysis requires the information from an existing structure, such as a mechanical model and random distributed inputs. In many engineering problems, a state monitoring system commonly provides experimental or monitoring data, which can be used to update the initial estimation for structural reliability to reduce prediction uncertainty. A critical issue in this process is the manner in which the existing information and new data can be reasonably integrated into a reliability estimation. In this paper, Bayesian updating approach is applied to incorporate the additional data. Firstly, a theoretical model is established to predict the prior distribution of the limit state function (LSF) with the first-order reliability method. Then, the Bayesian inference theory is applied to update the probability distribution parameters of LSF using the acquired experimental or monitoring data. The analytical form of the LSF’s posterior distribution is derived under the assumption that the experimental test error follows a normal distribution. To improve accuracy, a second-order reliability method is proposed based on the theory of saddlepoint approximation. Markov chain Monte Carlo simulation is used to derive a general method for updating the LSF’s distribution using the experimental or monitoring data. Finally, three numerical examples are provided to illustrate the proposed framework’s validity.
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