Modeling, Diagnosis And Control Of Stream Of Dimensional Variation In Multi-Variation-Source Multi-Stage Machining Systems

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 variationsThis 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.