Advanced Materials Research Vol. 544

Abstract: Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exchanging and inserting the key operations which belong to the critical path. The optimization objective is to minimize makespan. The experimental results obtained from proposed algorithm show that the proposed algorithm is very efficient and effective for all tested problems.

Abstract: The unavailability equations of several aging scenarios of standby safety component are derived and the risk of standby safety system is quantified. The different maintenance strategies are adopted for no aging scenario and several aging scenarios respectively. The two kind of test and maintenance (T&M) policies are adopted for no aging scenario and several aging scenarios. A numerical example is introduced for the illustration. The system unavailabilities under different configurations of parallel safety system and T&M policies are computed and compared. It can be derived that the combination of different T&M policies and configurations is significant effect on the risk of standby safety system and optimal STI and T&M interval.

Abstract: In this paper, a multi-objective parameter optimization model based on experimental design and NN-GA is established. In this method, utilizing experimental design principle to deal with test project and applying NN to map and using Pareto genetic algorithm to optimize, multi-objective parameter optimization is accomplished, in which the high nonlinear mapping ability of neural network model, the global research ability of genetic algorithms and multiform choice about the test points according to experimental demand are utilized synthetically. A Pareto-optimal set can be found in specify region. The method can be applied broadly and it needn’t the concrete mathematic model for different optimizing demand. For virtual devices and products, the virtual experiments can be realized by parameter-driven characteristic.

Abstract: Hydraulic support is an important equipment of synthesis mechanization caving coal and safe production in modernization coal mine. According to the research object of ZF5000/16/28’s mould hydraulic support, stress distribution of dangerous parts is obtained by stress testing and ANSYS software. The relevant total displacement distribution rule and equivalent stress distribution rule of the type hydraulic support prototype were found. At the same time, reliability analysis of a single stent and stent system reliability prediction were carried out, which guides design and use of hydraulic support in theory better.

Abstract: Performance degradation; Time series; Reliability prediction; Non-stationary data. Abstract. Performance degradation model has become a critical issue for lifetime reliability research. A novel n-ARIMA-based equipment performance degradation model method was proposed in this paper. First of all, a general performance degradation process was analyzed in detail. Then a non-stationary Auto-Regression Integrated Moving Average model was adopted to set up the equipment performance degradation model, and an equation to calculate the performance degradation level was also built. Finally, a validation experiment was implemented on OTM650 milling machine. It has been indicated from the experiment that the performance degradation model built in this paper may accurately describe the characteristics of the equipment performance degradation against time.

Abstract: NC machines are more and more widely used in manufacturing industry. As the key equipments in the factory, NC machines should be closely monitored to avoid faults. Therefore, the research on multi-function diagnosis based on BP-SOM is discussed for NC machine in the paper. Taking the precision data as the basic data for diagnosis, the research used BP and SOM algorithms to build up diagnosis models. As a result, the research has accomplished the diagnosing process and acquires good simulation output, which resembles with the real fact.

Abstract: This paper analyzes the machining process of CNC machine tools, and builds an optimization model of the machining process parameters based on the mechanical vibration and the operational research. The model mixed genetic algorithm and particle swarm optimization (PSO) is built. It proposes an optimization algorithm that has higher convergence precision and execute ability to solve engineering problem with nonlinear and multi-extremum. According to case study, it proves the correctness of the model and the efficiency and high-performance nature of the designed optimization algorithm. It also appears the efficiency to solve the common engineering problems by the intelligent optimization algorithms.

Abstract: Condition-based Maintenance (CBM) can not only efficiently improve the performance of deteriorating system but also guarantee the system operation safety. This paper assumes that the system state is periodically inspected, and a preventive maintenance is performed if the degradation level exceeds a threshold. The effect of maintenance is imperfect, which means that maintenance can restore the system state to somewhere between as good as new and as bad as old. The algorithm is presented to get the solution of long run cost based on Monte-Carlo simulation, and the joint optimization of inspection rate, the threshold value and the number of preventive maintenance activities is investigated for the minimization of long run cost rate. A case study is given to show the procedure of the maintenance model and simulation. Therefore, the correctness and rationality of the model are proved.

Abstract: ATC provides a systematic approach in solving decomposed large scale systems that has solvable subsystems. However， complex engineering system usually has a high computational cost , which result in limiting real-life applications of ATC based on high-fidelity simulation models. To address these problems, this paper aims to develop an efficient approximation model building techniques under the analytical target cascading (ATC) framework, to reduce computational cost associated with multidisciplinary design optimization problems based on high-fidelity simulations. An approximation model building techniques is proposed: approximations in the subsystem level are based on variable-fidelity modeling (interaction of low- and high-fidelity models). The variable-fidelity modeling consists of computationally efficient simplified models (low-fidelity) and expensive detailed (high-fidelity) models. The effectiveness of the method for modeling under the ATC framework using variable-fidelity models is studied. Overall results show the methods introduced in this paper provide an effective way of improving computational efficiency of the ATC method based on variable-fidelity simulation models.