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1.
This paper aims to contribute to process and production planning integration through the development of a new cutting parameters optimisation model. The developed model considers simultaneously the technology-related constraints and a shop floor constraint determined by the available time at each workstation. The latter, being a constraint related to the part machining time, is associated with the set of all elementary machining operations and implies the development of a new multi-operation optimisation model. In this approach, part machining time is a new variable for shop floor scheduling. Since the limiting factor of workstation available time at every scheduling date depends on the shop floor status, optimum part machining time can range from the time for minimum cost to the time for maximum production rate. The introduction of the available time in the optimisation process allows for the generation of improved schedules according to several performance measures. The proposed optimisation model is non-linear, uni-criterion and multi-variable. The search of the optimal solution is carried out using sequential quadratic programming.  相似文献   
2.
An experimental study is presented that investigates the effects of various controllable polishing parameters on the resulting surface, when using a flexible abrasive disk on die steel. The objective is to achieve a robust process that results in a consistent surface finish, the roughness of which can be specified and controlled. Experimental results indicate that the inclination angle of the disk with respect to the workpiece, and the feedrate have optimal values that minimise variability of the surface finish, while the normal force applied to the disk should be used to control nominal surface roughness.  相似文献   
3.
Metal cutting plays an important role in manufacturing industries. Optimisation of cutting parameters represents a key component in machining process planning. In this paper, a neural network based approach to multiple-objective optimization of cutting parameters is presented. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers' preference structures. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.Nomenclature v cutting speed (m/min) - f feed rate per revolution (mm/rev) - d depth of cut per pass (mm) - T p total operation time per part (min) - T i set-up time per part (min) - T c tool change time (min) - T i idle time per part (min) - C p cost per part ($) - C t cost of tool per piece ($) - C l labor cost per unit time ($/min) - C o overhead per unit time ($/min) - V volume to be removed per part (mm3) - MRR metal removal rate (mm3/min) - TL tool life (min) - SR surface roughness (m) - H p arithmetic centre-line average (m) - P cutting power (kW) - F cutting force (kg) - interface temperature (°C)  相似文献   
4.
Product manufacturing on CNC milling machine tools involves a number of machining parameters and tool geometries. In the case of sculptured or free-form surfaces the number of these parameters can be significantly large and vary according to surface complexity. Minimising the number of parameters is carried out through statistical elimination. Design of experiments (DoE) along with the respective statistical analysis of variance (ANOVA) constitutes a low-cost useful tool in determining sub-optimum values for all parameters involved in each milling strategy as well as the most significant of those parameters. DoE was implemented for a particular sculptured surface assessing a variety of roughing and finishing strategies of a CAM simulation software.  相似文献   
5.
In this study, a new molten carbonate fuel cell-gas turbine hybrid system, which consists of a fuel cell, three heat exchangers, a compressor, and a turbine, is established. The multiple irreversible losses existing in real hybrid systems are taken into account by the models of a molten carbonate fuel cell and an open Brayton cycle with a regenerative process. Expressions for the power outputs and efficiencies of the subsystems and hybrid system are derived. The maximum power output and efficiency of the hybrid system are numerically calculated. It is found that compared with a single molten carbonate fuel cell, both the power output and efficiency of the hybrid system are greatly enhanced. The general performance characteristics of the hybrid system are evaluated and the optimal criteria of the main performance parameters are determined. The effects of key irreversibilities on the performance of the hybrid system are investigated in detail. It is found that the use of a regenerator in the gas turbine can availably improve the power output and efficiency of the system. The results obtained here are significant and may be directly used to discuss the optimal performance of the hybrid system in special cases.  相似文献   
6.
为了提高网络入侵检测的正确率,提出一种混合入侵杂草HIWO(hybrid invasive weed optimization)算法优化SVM的网络入侵检测模型(HIWO-SVM)。该模型将SVM参数编码为入侵杂草,并以网络入侵检测率作为杂草种子适应度函数,然后通过模拟杂草入侵种子的空间扩散、生长、繁殖和竞争等过程找到SVM的最优参数。在寻优过程中引入遗传算法交叉操作以增强HIWO算法跳出局部极值的能力,最后根据最优参数建立网络入侵检测模型。在Matlab 2012平台采用KDD CUP 99数据集仿真测试,结果表明HIWO-SVM可以获得满意的网络入侵检测效果。  相似文献   
7.
提出将基因表达式编程应用于图像自适应阈值去噪,根据多尺度分辨率特性和基因表达式编程的全局搜索能力,构建搜索最小均方差的自适应阈值优化模型。实验结果表明,基于基因表达式编程的自适应阈值参数优化策略在图像去噪方面是可行的,并达到了较高的峰值信噪比。  相似文献   
8.
针对带混沌特性的网络流量在线预测,提出一种融合自适应粒子群算法( APSO)和递推式最小二乘支持向量机回归的流量模型。对流量序列嵌入重构得到多维状态输入矢量,将其作为初始LSSVM的训练样本,其中采用自适应粒子群算法对模型的特征参数、嵌入维数寻优,避免早熟停滞。对于在线预报过程中的吸收样本、删减样本采用核矩阵迭代式求解,动态调整回归机,使得模型具有在线学习能力,由此得APSO-LSSVM在线流量预测模型,并考察网络负荷度与嵌入维数关系。仿真实验表明:该方法能有效预测网络流量,实现较高精度实时流量估计。  相似文献   
9.
网络异常检测技术是入侵检测系统中不可或缺的部分。然而目前的入侵检测系统普遍存在检测率不高,误报率过高等问题,从而难以在实际的企业中大规模采用。针对之前的检测技术检测效果不佳的问题,提出基于SVM回归和改进D-S证据理论的入侵检测方法。该方法是将支持向量机回归的分类融合应用到网络异常行为分析中,在SVM参数选择时采用交叉验证和深度优先搜索算法进行优化选择,并通过融合证据理论,建立网络异常检测模型。通过仿真实验表明,该模型能够有效地提高入侵检测性能,缩短检测时间。  相似文献   
10.
为了提高网络流量的预测精度,针对网络的时变性和混沌性,提出一种反向学习粒子群优化神经网络的网络流量预测模型(BPSO-RBFNN)。首先将网络流量样本输入到RBF神经网络进行学习,采用引入反向学习机制的粒子群算法优化参数,然后建立网络流量预测模型,最后采用仿真实验对模型性能进行分析。结果表明,BPSO-RBFNN可以描述网络流量的时变性、混沌性变化趋势,网络流量预测精度得以提高,具有较好的实际应用价值。  相似文献   
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