多源多工序加工系统偏差流建模、诊断和控制系统研究
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摘要
产品尺寸偏差是直接影响产品质量、生产率和市场响应时间等的最重要因素之一。复杂产品的加工系统往往是一个串行与并行相结合的多源多工序制造系统,最终产品质量受到制造过程中所有工序上多个偏差源的影响。除了单个工序上的各种偏差外,不同工序间存在复杂的耦合关系,这些尺寸偏差不断产生、增长、消减、累积和传递,形成了最终产品的尺寸偏差。目前复杂零件的加工过程向着高精度、高效率、高可靠性的方向发展,其重要特征体现为制造过程的可预测性和可控性,制造系统的可维护性和可诊断性。目前国内外学者在单工序尺寸偏差分析、诊断和控制方面已有大量研究成果,但很少从系统层面上对历经多个工序尺寸偏差的累积传递过程进行全面描述,同时缺乏有效的偏差源诊断和控制体系,未能实现尺寸偏差传递分析、预测、诊断、控制一体化。本文以复杂多源多工序加工过程为研究对象,通过对尺寸偏差流建模、诊断和控制,为加工过程的质量偏差预示与消减提供共性技术与方法,确保系统在高质量控制模式下运行。首先研究了多工序加工系统尺寸偏差流分析与建模方法。通过将关键特征偏差映射为随工序延展而不断变化的状态变量,研究如何将各工序之间偏差的影响关系向数学模型进行映射,并描述偏差形成与传递关系、动态变化及分析加工过程中偏差流流动线路。然后对偏差源诊断体系进行了研究。推导了多工序制造系统具有完全可诊断性的充分和必要条件;提出了局部可诊断系统可诊断能力的量化评价体系,并针对局部可诊断系统给出具体的提高偏差源诊断能力的措施;研究全面有效的多源多工序加工系统的偏差源诊断方法。最后设计了尺寸偏差控制系统,基于产品测量数据,设计多工序加工系统尺寸偏差控制系统结构,并使用开发的关键产品特征数学模型,针对实际生产情况推导出相应的控制法则和控制策略。
基于前人的研究成果和大量实际的工程经验,本文在多工序加工系统尺寸偏差流建模、诊断和控制的研究中做出了以下具有创造性的研究工作:
(1)建立了线性显式表示的多源多工序加工系统尺寸偏差传递状态空间模型,并将该模型扩展到了串并联多个偏差流加工系统中
在建立关键特征分析的系统流程和算法基础上,分析了多工序加工过程中的尺寸偏差传递规律。使用齐次变换方法,建立了关键产品特征状态空间模型,并对状态空间的工程含义、状态向量、控制向量、观测向量以及系统矩阵、控制矩阵和观测矩阵进行定义;通过对关键产品特征偏差、刀具路径、定位基准、测量基准和夹具几何关系的研究,线性显式表达了零件的关键产品特征与各种偏差源之间的影响关系和动态变化,解决了传统方法难以给出偏差转换与累积关系的线性和显式表达的难题。将串联制造系统单个偏差流状态空间模型扩展到了串并联混合式多个偏差流状态空间模型。在已知零件的初始偏差、各种输入偏差以及各工序所对应的离散时间点上的系统矩阵、控制矩阵的基础上,推导计算各个工序上产品偏差的递推算法,并给出了模型降维方法。推导出了每一道工序和每一条加工路线上产品偏差的均值和方差的计算公式。这些模型和方法弥补了传统方法不能对多源多工序加工系统多个偏差流进行有效建模的缺陷,增强了偏差流建模理论和制造系统建模理论。
(2)提出了多工序加工系统可诊断性分析方法和基于状态空间模型、估计理论与假设检验的偏差源诊断方法
基于建立的状态空间模型,解析出多工序制造系统具有完全可诊断性的充分必要条件,为优化制造系统设计和快速诊断出偏差源提供了理论基础;并建立了局部可诊断系统的量化评价体系,解决了对局部可诊断的多源多工序加工系统进行量化描述的问题。然后提出了基于状态空间模型、估计理论和先进统计学相结合的偏差源诊断方法,该方法可以有效地诊断出多源多工序加工系统的偏差源,并为偏差源诊断中大量引入线性控制理论和先进统计学提供了坚实的基础。
(3)建立了基于状态空间模型的尺寸偏差控制系统,推导出了控制法则,建立了具体的控制策略
在引入奇异值分解、值空间、零空间等概念的基础上,基于线性显式状态空间模型,针对“不同批次零件之间的控制”和“同一批次内零件的控制”两种生产情况提出了闭环控制系统结构。利用奇异值分解理论推导出了多工序加工系统相应的控制法则和控制策略,为产品尺寸偏差控制提供了新的理论依据和实践参考。
本文通过提出偏差流离散事件动态系统的多学科建模方法,将偏差源对产品质量作用的定性认识上升为对其行为的定量分析,实现对零件质量的预测和控制。本文的研究可以很好地解决多源多工序加工系统产品质量控制与改进中存在的核心问题,实现建模、分析、预测、诊断和控制一体化,为多源多工序加工系统过程控制、系统设计、偏差源诊断等提供有效的科学指导。
Product dimensional variation is one of most important factors that affect directly product quality, productivity and response time to market. A machining system of complicated product is usually a serial-parallel multi-variation-source multi-stage manufacturing system, in which the final product variation is an accumulation or stack-up of variation from all machining stages. There are coupling relationships between variations from stages while different variations exist in a single stage. Those dimensional variations will be introduced, increased, decreased, stack-up, propagated, and become the final product variation when one workpiece goes through the whole machining system. Currently, the complicated products have been machining with high precision, high efficiency and high reliability. In addition, some important characteristics of machining processes and machining systems are reflected in predictability and controllability, maintainability and diagnosability respectively. A lot of research work about dimensional variation analysis, diagnosis, control in a single stage has been done by domestic and international researchers. However, these work does not fully characterize dimensional variation accumulation process when a workpiece goes through multiple stages in a systemic level, and does not provide the integration of propagate analysis of dimensional variation, predict, diagnosis, control.
The multi-variation-source multi-stage machining process is studied in this research, which provides some general technologies and methodologies for predicting and reduction of product quality variation in a machining process and ensuring that the machining system can be controlled very well.
This research work can be summarized as follows:
1) Development of methodology for dimensional variation analysis and modeling in a multi-stage machining system. Through mapping key Characteristics variations into changeable state variables when a workpiece goes through the whole machining process, this work studies how to map the relationship of variations from all stages into a mathematical model, and describe form and propagation of variations, dynamical changes, as well as analyze propagation process of stream of variations in a machining process.
2) Development of a diagnosis system for variation sources. This work studies the sufficient and necessary conditions of diagnosability, in which a multi-stage manufacturing system is fully diagnosable, and also presents a study of quantized evaluation system of diagnosable capability for partial diagnosable manufacturing system, as well as provides some measures that can improve the diagnosable capability of variation sources for partial diagnosable manufacturing system. This work also studies the effective diagnosis methods for variations sources detection in a multi-variation-source multi-stage machining system.
3) Design of dimensional variations control system. This work designs dimensional variations control system for a multi-stage machining system, and derives corresponding control laws and control strategies in terms of practical production using product measurement based on developed key Product Characteristics mathematical model.
Based on related research work and lots of practical engineering experience, this work makes three main contributions about stream of dimensional variations modeling, diagnosis and control in multi-stage machining system as follows:
(1) Development of linear explicit state space model of dimensional variation in multi-variation-source multi-stage machining system, and this model is extended to state space model of multiple streams of variations
This work analyzes the propagation process of stream of dimensional variations based on explored procedure and algorithm of key Characteristics analysis. Using homogeneous transformation approach, key Product Characteristics State Space Model is developed, and engineering meanings, state vectors, control vectors, observe vector, system matrix, control matrix and observe matrix are defined. Through analyzing the geometry relationship between key product characteristics variations, tooling path, locating datum, measure datum and fixture, the procedures are introduced for expressing explicitly the influence and dynamical changes of errors in fixtures, locating datum features and measurement datum features on dimensional variations. This explored model can solve how to build the linear explicit relationship between KPCs and variation-source, which can’t be solved by current traditional methods. The state space model of single stream of variation in a serial multi-stage machining is extended to the state space model of multiple streams of variations in a serial-parallel hybrid multi-stage machining system. On the basis of given initial workpiece variations, all kinds of inputs errors, and system matrix, control matrix at discrete time points corresponding the stage, regression algorithm that calculate the product dimensional variations of all stages is explored and the method for system model dimensions reduction is developed.
(2) Development of diagnosiability analysis and calculation and diagnosis method based on developed state space model, estimation theory and hypothesis test in a multi-stage machining system.
The sufficient and necessary conditions are derived based on developed state space model, in which a multi-stage machining system can be fully diagnosable. They lay the foundation for optimization of system and diagnosis. The quantitive evaluation system for a partial diagnosable system is explored, and the measurements are given to improve the diagnosability. One diagnosis method of variations sources integrating state space model, estimation theory and advanced statistics is developed, which lay the foundation for introduction of lots of linear control theory and advanced statistics in diagnosis of variation sources.
(3) Development of dimensional variation control system based on state space model and derivation of control laws and control strategies.
On the basis of introducing singular value decomposition, range space, null space, one closed loop control system is developed in terms of“process-to-process workpiece variations control”and“within-one-process workpiece variation control”practical production based on state space model. Control laws and control strategies of multi-stage machining systems are derived using singular value decomposition theory, which can be taken as new theory and practical references for product dimensional variations control.
One multiple-disciplines modeling method for stream of variation in discrete event dynamical system is developed, which provides us not only a qualitative description but also a quantized description about influence of variation sources on product quality in order to predict and control workpiece quality. This research work can solve some main problems of product quality control and improvement in a multi-variation-source multi-stage machining system, and provides a integration of modeling, analysis, predict, diagnosis and control, and provides scientific guidance for process control, system design, variation sources diagnosis in multi-variation-source multi-stage machining system.
基于前人的研究成果和大量实际的工程经验,本文在多工序加工系统尺寸偏差流建模、诊断和控制的研究中做出了以下具有创造性的研究工作:
(1)建立了线性显式表示的多源多工序加工系统尺寸偏差传递状态空间模型,并将该模型扩展到了串并联多个偏差流加工系统中
在建立关键特征分析的系统流程和算法基础上,分析了多工序加工过程中的尺寸偏差传递规律。使用齐次变换方法,建立了关键产品特征状态空间模型,并对状态空间的工程含义、状态向量、控制向量、观测向量以及系统矩阵、控制矩阵和观测矩阵进行定义;通过对关键产品特征偏差、刀具路径、定位基准、测量基准和夹具几何关系的研究,线性显式表达了零件的关键产品特征与各种偏差源之间的影响关系和动态变化,解决了传统方法难以给出偏差转换与累积关系的线性和显式表达的难题。将串联制造系统单个偏差流状态空间模型扩展到了串并联混合式多个偏差流状态空间模型。在已知零件的初始偏差、各种输入偏差以及各工序所对应的离散时间点上的系统矩阵、控制矩阵的基础上,推导计算各个工序上产品偏差的递推算法,并给出了模型降维方法。推导出了每一道工序和每一条加工路线上产品偏差的均值和方差的计算公式。这些模型和方法弥补了传统方法不能对多源多工序加工系统多个偏差流进行有效建模的缺陷,增强了偏差流建模理论和制造系统建模理论。
(2)提出了多工序加工系统可诊断性分析方法和基于状态空间模型、估计理论与假设检验的偏差源诊断方法
基于建立的状态空间模型,解析出多工序制造系统具有完全可诊断性的充分必要条件,为优化制造系统设计和快速诊断出偏差源提供了理论基础;并建立了局部可诊断系统的量化评价体系,解决了对局部可诊断的多源多工序加工系统进行量化描述的问题。然后提出了基于状态空间模型、估计理论和先进统计学相结合的偏差源诊断方法,该方法可以有效地诊断出多源多工序加工系统的偏差源,并为偏差源诊断中大量引入线性控制理论和先进统计学提供了坚实的基础。
(3)建立了基于状态空间模型的尺寸偏差控制系统,推导出了控制法则,建立了具体的控制策略
在引入奇异值分解、值空间、零空间等概念的基础上,基于线性显式状态空间模型,针对“不同批次零件之间的控制”和“同一批次内零件的控制”两种生产情况提出了闭环控制系统结构。利用奇异值分解理论推导出了多工序加工系统相应的控制法则和控制策略,为产品尺寸偏差控制提供了新的理论依据和实践参考。
本文通过提出偏差流离散事件动态系统的多学科建模方法,将偏差源对产品质量作用的定性认识上升为对其行为的定量分析,实现对零件质量的预测和控制。本文的研究可以很好地解决多源多工序加工系统产品质量控制与改进中存在的核心问题,实现建模、分析、预测、诊断和控制一体化,为多源多工序加工系统过程控制、系统设计、偏差源诊断等提供有效的科学指导。
Product dimensional variation is one of most important factors that affect directly product quality, productivity and response time to market. A machining system of complicated product is usually a serial-parallel multi-variation-source multi-stage manufacturing system, in which the final product variation is an accumulation or stack-up of variation from all machining stages. There are coupling relationships between variations from stages while different variations exist in a single stage. Those dimensional variations will be introduced, increased, decreased, stack-up, propagated, and become the final product variation when one workpiece goes through the whole machining system. Currently, the complicated products have been machining with high precision, high efficiency and high reliability. In addition, some important characteristics of machining processes and machining systems are reflected in predictability and controllability, maintainability and diagnosability respectively. A lot of research work about dimensional variation analysis, diagnosis, control in a single stage has been done by domestic and international researchers. However, these work does not fully characterize dimensional variation accumulation process when a workpiece goes through multiple stages in a systemic level, and does not provide the integration of propagate analysis of dimensional variation, predict, diagnosis, control.
The multi-variation-source multi-stage machining process is studied in this research, which provides some general technologies and methodologies for predicting and reduction of product quality variation in a machining process and ensuring that the machining system can be controlled very well.
This research work can be summarized as follows:
1) Development of methodology for dimensional variation analysis and modeling in a multi-stage machining system. Through mapping key Characteristics variations into changeable state variables when a workpiece goes through the whole machining process, this work studies how to map the relationship of variations from all stages into a mathematical model, and describe form and propagation of variations, dynamical changes, as well as analyze propagation process of stream of variations in a machining process.
2) Development of a diagnosis system for variation sources. This work studies the sufficient and necessary conditions of diagnosability, in which a multi-stage manufacturing system is fully diagnosable, and also presents a study of quantized evaluation system of diagnosable capability for partial diagnosable manufacturing system, as well as provides some measures that can improve the diagnosable capability of variation sources for partial diagnosable manufacturing system. This work also studies the effective diagnosis methods for variations sources detection in a multi-variation-source multi-stage machining system.
3) Design of dimensional variations control system. This work designs dimensional variations control system for a multi-stage machining system, and derives corresponding control laws and control strategies in terms of practical production using product measurement based on developed key Product Characteristics mathematical model.
Based on related research work and lots of practical engineering experience, this work makes three main contributions about stream of dimensional variations modeling, diagnosis and control in multi-stage machining system as follows:
(1) Development of linear explicit state space model of dimensional variation in multi-variation-source multi-stage machining system, and this model is extended to state space model of multiple streams of variations
This work analyzes the propagation process of stream of dimensional variations based on explored procedure and algorithm of key Characteristics analysis. Using homogeneous transformation approach, key Product Characteristics State Space Model is developed, and engineering meanings, state vectors, control vectors, observe vector, system matrix, control matrix and observe matrix are defined. Through analyzing the geometry relationship between key product characteristics variations, tooling path, locating datum, measure datum and fixture, the procedures are introduced for expressing explicitly the influence and dynamical changes of errors in fixtures, locating datum features and measurement datum features on dimensional variations. This explored model can solve how to build the linear explicit relationship between KPCs and variation-source, which can’t be solved by current traditional methods. The state space model of single stream of variation in a serial multi-stage machining is extended to the state space model of multiple streams of variations in a serial-parallel hybrid multi-stage machining system. On the basis of given initial workpiece variations, all kinds of inputs errors, and system matrix, control matrix at discrete time points corresponding the stage, regression algorithm that calculate the product dimensional variations of all stages is explored and the method for system model dimensions reduction is developed.
(2) Development of diagnosiability analysis and calculation and diagnosis method based on developed state space model, estimation theory and hypothesis test in a multi-stage machining system.
The sufficient and necessary conditions are derived based on developed state space model, in which a multi-stage machining system can be fully diagnosable. They lay the foundation for optimization of system and diagnosis. The quantitive evaluation system for a partial diagnosable system is explored, and the measurements are given to improve the diagnosability. One diagnosis method of variations sources integrating state space model, estimation theory and advanced statistics is developed, which lay the foundation for introduction of lots of linear control theory and advanced statistics in diagnosis of variation sources.
(3) Development of dimensional variation control system based on state space model and derivation of control laws and control strategies.
On the basis of introducing singular value decomposition, range space, null space, one closed loop control system is developed in terms of“process-to-process workpiece variations control”and“within-one-process workpiece variation control”practical production based on state space model. Control laws and control strategies of multi-stage machining systems are derived using singular value decomposition theory, which can be taken as new theory and practical references for product dimensional variations control.
One multiple-disciplines modeling method for stream of variation in discrete event dynamical system is developed, which provides us not only a qualitative description but also a quantized description about influence of variation sources on product quality in order to predict and control workpiece quality. This research work can solve some main problems of product quality control and improvement in a multi-variation-source multi-stage machining system, and provides a integration of modeling, analysis, predict, diagnosis and control, and provides scientific guidance for process control, system design, variation sources diagnosis in multi-variation-source multi-stage machining system.
引文
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