共查询到19条相似文献,搜索用时 296 毫秒
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一种电火花加工模糊间隙控制方法研究 总被引:1,自引:0,他引:1
针对电火花加工间隙状态难以控制的问题,分析电火花加工特性,研究电火花加工状态识别及检测方法,研制了基于常规模糊控制的优化模糊控制器.此优化模糊控制器以两种采集信息为输入量,进行相应的模糊化,然后建立相关的模糊控制规则,最后耦合两种情况,输出模糊控制量.该优化控制加工系统,解决电火花加工过程中的关键问题,实现加工过程的智能控制.实验证明,该系统运行稳定、安全可靠,智能化程度高,加工效果显著. 相似文献
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加工过程状态的监测与控制是提高机床智能化的重要研究内容,为实现加工过程的智能化监测与控制必须以多传感器及多传感器信息融合技术为基础.提出了一种基于粗糙集理论和神经网络的多传感器智能信息融合方法,该方法将粗糙集理论作为实现多传感器数据融合的方法,同时针对粗糙集理论只能处理离散数据的问题.提出了使用自组织特征映射网络对传感器采集数据进行离散化及聚类处理的方法,针对粗糙集理论在决策融合处理方面的不足,提出了使用BP神经网络来实现决策规则的有效融合,分析了该方法的原理、关键技术及实现方法,为后期的进一步的研究和应用打下基础. 相似文献
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电火花加工过程控制技术研究新进展 总被引:2,自引:0,他引:2
本文在综述自适应控制与智能控制在电火花加工过程自动控制中应用现状的基础上,着重阐述了:智能控制的两种主要技术——模糊控制和神经网络控制的集成,将为电火花加工过程的智能控制提供有效途径,并探讨了该研究方向近期应予以关注的几个重点问题. 相似文献
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设计了以DSP和CPLD为控制单元的微细电火花脉冲电源,满足微细电火花加工单个脉冲能量小而可控的要求。针对加工过程难以用数学模型描述的问题,利用智能控制不依赖数学模型的优势,设计了模糊神经网络控制器,根据间隙放电状态,对在线参数实时调整。通过微小孔加工实验表明,采用智能控制的加工方式可以提高加工速度,有很好的应用前景。 相似文献
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本文着重阐述了基于神经网络理论,设计出称重传感器神经网络动态补偿器。通过实际模型及其分析提出了快速称重方法和提高传感器性能的原理,将先进控制理论应用于实际传感器系统中,为提高传感器性能开辟了新途径。 相似文献
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针对传统控制理论在视觉跟踪焊接机器人随动系统中难以取很良好控制效果的问题,提出了基于免疫学的智能控制方法.并运用该方法将随动系统跟踪焊缝所需的转动角度作为免疫抗原,以随动系统的输入电压作为免疫抗体,对焊缝跟踪系统的跟踪性能进行了仿真实验,结果表明该控制方法具有良好的快速性和稳定性,满足随动系统快速旋转跟踪焊缝的要求. 相似文献
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基于粗糙-神经网络的非线性系统逆模型控制 总被引:2,自引:0,他引:2
粗糙控制是近年来兴起的一种新的智能控制方法,作为对粗糙控制理论的探索,提出了粗糙规则逆模型的概念,并分析了粗糙规则逆模型的一致性和完备性问题,引入了基于径向基函数网络的粗糙决策规则推理方法,构造了粗糙-神经网络逆模型.对粗糙-神经网络逆系统模型的辨识以及基于粗糙-神经网络逆模型的控制理论和方法进行了分析和讨论,并通过实例仿真计算与实验分析,验证了粗糙-神经网络逆模型控制方法的可行性. 相似文献
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Adaptive Control Constraint of Machining Processes 总被引:4,自引:1,他引:3
Y. Liu T. Cheng L. Zuo 《The International Journal of Advanced Manufacturing Technology》2001,17(10):720-726
Adaptive control constraint is one of the machining process control types. In this paper, the major adaptive control constraint
systems are discussed, based on the feedback control, parameter adaptive control/self-tuning control, model reference adaptive
control, variable structure control/sliding mode control, neural network control, and fuzzy control. Their typical applications
to constant cutting force control system are also described, and some recent experiment results are presented. 相似文献
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Gou-Jen Wang Bor-Shin Lin Kang J. Chang 《The International Journal of Advanced Manufacturing Technology》2007,32(1-2):42-54
Process control is one of the key methods to improve manufacturing quality. This research proposes a neural network based
run-to-run process control scheme that is adaptive to the time-varying environment. Two multilayer feedforward neural networks
are implemented to conduct the process control and system identification duties. The controller neural network equips the
control system with more capability in handling complicated nonlinear processes. With the system information provided by this
neural network, batch polishing time (T) an additional control variable, can be implemented along with the commonly used down force (p) and relative speed between the plashing pad and the plashed wafer (v).
Computer simulations and experiments on copper chemical mechanical polishing processes illustrate that in drafting suppression
and environmental changing adaptation that the proposed neural network based run-to-run controller (NNRTRC) performs better
than the double exponentially weighted moving average (d-EWMA) approach. It is also suggested that the proposed approach can
be further implemented as both an end-point detector and a pad-conditioning sensor. 相似文献
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Kyunghyun Choi 《Journal of Mechanical Science and Technology》1996,10(4):389-395
A neural networks based approach to determine the appropriate machining parameters such as speed, depth of cut and feed is proposed in this study. In this approach neural networks were used for building automatic process planning systems. Training of neural networks was performed with back propagation method by using data sets sampled in a standard handbook. These networks consist of simple processing, elements or nodes capable of processing information in response to external inputs. This approach saves computing time and storage space. In addition, it provides easy extendability as new data become available. Currently, the system provides three neural networks: for turning, for milling and for drilling operations. The performance of the trained neural network for drilling is evaluated to examine how well it predicts the machining parameters. Test results show that the neural network for the turning operation is able to predict the machining parameter values within an acceptable error rate. 相似文献
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Xifan Yao Yi Zhang Bin Li Zheng Zhang Xiaoqin Shen 《The International Journal of Advanced Manufacturing Technology》2013,69(5-8):1701-1715
Force control is an effective means of improving the quality and efficiency of machining operations, so various approaches for force control have been proposed. However, due to the nonlinear, time-varying and uncertain characteristics of machining processes, it is difficult to develop force control systems that are stable and robust over the full range of operating conditions. This study proposed two control schemes to address such difficulties in the field of nonlinear force control by using a linear feedback proportional-derivate (PD) controller respectively with two different nonlinear intelligent compensators: fuzzy logic compensator (FLC) and neural network compensator (NNC). The PD controller is used to improve the transient response while maintaining the stability of the process system, and the FLC or NNC is employed to eliminate the steady-state error and compensate for the system nonlinearity (or uncertainty). The applications of the proposed schemes in machining processes show that the controllers adapt well to nonlinearity under time-varying cutting conditions in comparison to PID, PD, and FLC. The online updating of the NNC parameters through the Feedback-Error Learning can further improve the system performance. 相似文献
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加工误差的控制是自动化加工技术中的难题,传统的控制理论不能解决一些问题。通过人工神经网络建立起切削加工自动控制系统,具体训练网络采用离线训练,将样本数据准备后用Matlab工具箱函数即可完成,并应用于实际的切削过程。结果表明,神经网络控制系统能有效的控制加工误差。 相似文献