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基于复杂系统理论的高速数控加工装备动静态特性监控技术研究

Dynamic and Static Characteristics Monitoring Technology for High-Speed CNC Machining Equipment Based on Complex System Theory

【作者】 杨泽青

【导师】 刘丽冰;

【作者基本信息】 河北工业大学 , 机械制造及其自动化, 2010, 博士

【摘要】 随着企业信息化程度和高速加工技术的不断发展,客户需求也日益个性化、多样化、精密化,高速数控制造装备不仅配置有实现加工任务所需的刀具,而且须配备检测设备和监控设备,导致制造系统的信息环境变得越来越复杂,产品的制造过程也与常规的切削加工过程不同,是集装夹过程、加工过程、检测过程、监控过程等多流程为一体,具有多变性、复杂性、动态性和不确定性等特点。高速数控制造装备和集成制造过程动静态特性监控具有复杂系统的动态、复杂、多变、不确定性以及协同工作等特点,属于复杂性问题,所以需要用复杂系统的理论和分析方法对其进行研究,具有重要理论意义和研究价值。本文基于复杂系统理论对高速数控加工装备动静态特性监控方法及应用技术展开研究,取得了如下创新性研究成果与结论:1、针对产品制造过程复杂、动态、多变、不确定等特点,借鉴诸多学者在制造系统组织结构复杂性、产品工艺规划复杂性、产品设计过程复杂性方面的研究思想和研究成果,提出了制造过程复杂性的概念,目的是找出一种能减少制造过程复杂性的系统科学方法,保证复杂制造装备优质高效地完成加工任务,具有一定的科学意义。2、在对复杂系统理论基本方法研究的基础上,针对目前制造过程组态监控存在的监控对象固定不变、监控参数不能根据需要任意调整等局限性,再结合元胞自动机模型解决复杂性问题的优势,提出了元胞组态协同的监控方法。由于其具有复杂系统理论基本方法的动态、演化、协同特点,所以是基于复杂系统理论的研究方法,可解决高速数控制造装备和集成制造过程动静态特性监控问题。3、给出了规范化的元胞设计方法,即每个元胞设计为一个四元组,表示为C=(元胞态空间,元胞状态集合,影响元胞状态变化的因素集合,作业规则),并研究了元胞内部的组态原理和元胞之间的协同机制,为元胞组态协同监控方法的实现提供理论基础。4、在理论方法和关键技术研究基础上,将元胞组态协同监控方法应用于高速数控制造装备和集成制造过程动静态特性监控中,用有限元分析方法对高速数控车削加工工艺系统动静态特性进行分析,明确了动静态特性指标,找出工艺系统的薄弱环节,为确定监测对象提供依据。应用元胞组态协同方法将高速集成制造复杂过程多对象、多空间、多领域监控转换为基于“工序节点处质量控制元胞”一维监控,把高维问题转换为一维问题,大大的降低了制造过程监控的复杂性。且基于组态软件及VC平台,研发了基于元胞组态协同方法的高速数控制造装备和集成制造过程动静态特性监控系统,实现了高速数控制造装备动静态性能指标的监控和高速车削加工质量的监控,为高速车削加工全过程质量动态监控的实现提供依据,便于对产品制造质量特性波动追本溯源,进而挖掘产品质量波动的分布规律和制造过程中的误差传播问题,为动态误差补偿提供依据。

【Abstract】 With the development of enterprise information and high-speed CNC machining technology, the requirements of individuation, diversification and precision are increasing. High-speed CNC manufacturing equipment should be equipped with inspection devices and monitoring equipments besides the necessary tools to achieve processing tasks. That increases the complexity of the information environment of the manufacturing system. And the product manufacturing process is different from conventional machining process. It integrates the clamping process, machining process, inspection process and monitoring process as a whole. And it has the characteristics such as complexity, dynamic, polytropy, uncertainty, etc.. Static and dynamic characteristics monitoring of High speed CNC manufacturing equipments and integrated manufacturing process belong to complex problem, because it has the characteristics of complex system, including complexity, dynamic, polytropy, uncertainty, collaborative and etc.. Therefore, complex system theory and analysis methods are employed to research on the static and dynamic characteristics monitoring of High speed CNC manufacturing equipments and integrated manufacturing process, which has significant theoretical and research value.This thesis investigates dynamic and static characteristics monitoring method of high-speed CNC machining equipment and application technology based on complex system theory. The creative contributions and conclusions may be summarized as follows.(1) In view of the characteristics of the manufacture process such as complexity, dynamic, polytropy, uncertainty, etc., Manufacturing Process Complexity(MPC) concept is proposed after learning many research scholars’thoughts and achievements on manufacturing system structure complexity, product process planning complexity and product design process complexity, in order to find a systematic scientific method can reduce the manufacturing process complexity and ensure that the machining task can be achieved with high quality and efficiency, which has certain scientific significance.(2) Cellular Configuration Collaborative Monitoring(CCCM) method is proposed based on the research on basic methods of complex system theory, taking account many limitations such as monitoring object can not be changed and monitoring parameters can not be discretionary adjusted in manufacturing process configuration monitoring at present,and combining with the advantages of cellular automata model solves complex problem. It has the same characteristics with complex system theory such as dynamic, evolution, collaborative, so It’s a research method based on complex theory, and can resolve complexity problem such as static and dynamic characteristics monitoring of high speed CNC manufacturing equipment and integrated manufacturing process. (3) Normalized cellular design method is given that is each cellular is designed as a quaternion, expressed as "C= (cellular state space, cellular state set, factors set affecting the cellular state, operation rules) ". Then the configuration principle of cellular and the collaborative mechanism between cellulars are researched, which provide theoretical foundation for the realization of the CCCM method.(4) The CCCM method is applied to static and dynamic characteristics monitoring of high speed CNC manufacturing equipment and integrated manufacturing process, based on research of theory method and key technologies. Static and dynamic characteristics of High-speed CNC turning process system are analyzed with Finite Element Analysis(FEA) method, in order to determine parameters of static and dynamic characteristics, and find the weaknesses of the process system. These informations provide references to decide which objects need to be monitored. The CCCM method converts the monitoring with multi-object, multi-space and multi-field in the high speed CNC integrated manufacturing complex process into one-dimensional monitoring based on "Quality Control in Process Nodes" Cellular. The complexity of manufacturing process monitoring is greatly reduced. Moreover, the static and dynamic characteristics monitoring system of high speed CNC manufacturing equipment and integrated manufacturing process is developed based on the CCCM method, using configuration software and VC platform. The static and dynamic characteristics parameters of high speed CNC manufacturing equipment and machining quality of high speed turning can be monitored by the system. It contributes to the achievement of dynamic quality monitoring of whole process in high speed turning, and facilities to look for the reasons of manufacturing quality fluctuation, and further to summarize distribution rules of product’s quality fluctuation and error transition in manufacturing process, which can provide evidences for dynamic error compensation.

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