臺灣博碩士論文加值系統

隨著快速進步之電腦科技與逐漸成熟的電腦輔助工程技術，射出成形已儼然成為精密產品之主要生產途徑，如何改善產品生產效率及提高產品品質，勢必成為當前之首要研究課題。本論文先整理與分析過去數十年來有關射出成形之程序控制文獻與相關理論，再詳盡研究探討可改善產品品質之途徑與驗證方式，並考慮品質工程上所可能採用之方案，進行各種可行性探討與實驗設計分析，再參考類神經網路建模理論與步驟，嘗試改良程序控制器基本架構。故針對射出成形之程序控制，提出逆向類神經最佳化控制結構，串聯兩組輻射基類神經網路，令其一為控制器，另一為品質預測器，同時採用多重損失函數為一效能指標，藉以進行最佳化控制。此結構所構建射出成形參數與品質特性方式具下列之優點：一為可適當地的控制多種品質特性，另一為可調整權重以適應不同的品質組合要求。經由數值模擬及實驗的驗證，確可使射出成形過程完全自動化，並使操作參數逼近於最佳射出之參數條件；即便當次射出無法使成形品具最小的總品質損失，控制器仍將調整射出機之成形參數以減少總品質損失，此法不僅可應用於尋找射出機操作參數的起始設定，更可於生產時監看射出機操作參數之狀態，再於以調整至最佳操作點。經驗證實驗結果顯示，本文所提出之逆向類神經最化控制系統，可以依產品品質需求，在少數的幾模週期內，自動地穩定於近似最佳點上持續操作。

With the vast development on computer hardware as well as computer aided engineering (CAE) software, injection molding has become one of the primary methods for producing precision industrial products. Hence, the problems on improving both production efficiency and product quality need to be immediately studied and solved today. After reviewing the literatures and theoretical background concerning the process control for injection molding in the past decades, the study on quality improvement and experimental verification has been thoroughly conducted in this dissertation. With the considerations for quality engineering methodology, various viability studies and design of experiments have been accomplished. Also, improvements on the basic process control technology have been tested based upon the theoretical approach via neural networks modeling. In this study, a novel optimization scheme, namely inverse neural optimal control system (INOCS), is proposed for the process control with two serially connected radial basis functions networks (RBFN) that act as a quality predictor and an optimal controller relying on a multi-losses function based performance index. The proposed INOCS controller can appropriately handle multi-qualities or various combinations of qualities with prescribed weightings. After being verified by numerical simulations and experiments, the injection molding process could be fully automated with processing parameters set to the optimal conditions via the minimization of the total loss index. The INOCS controller works for not only the initialization of the parameters during startup but the optimization and adaptation of parameters during the process. In conclusion, the controller should meet the quality requirements and maintain steady operating conditions in less than a few cycles.

摘 要
ABSTRACT
ACKNOWLEDGEMENT
TABLE OF CONTENTS I
LIST OF TABLES AND FIGURES III
NOMENCLATURES AND NOTATIONS IX
CHAPTER 1 INTRODUCTION 1
1.1 Objective of the Study 2
1.2 Literatures Review 4
1.3 Scope of Work 18
CHAPTER 2 PREDICTIVE AND CONTROL MODEL 23
2.1 Problem Descriptions 23
2.2 Fundamental Assumptions 34
2.3 Design of Experiments 36
2.4 Radial Basis Function Networks 36
2.5 Inverse Neural Networks 38
CHAPTER 3 QUALITY CHARACTERISTICS AND PREDICTIONS42
3.1 Qualities and Losses 42
3.2 System Inputs and Outputs 51
3.3 Process Models 54
3.4 CAE Simulations 55
3.5 Screening Procedures 57
3.6 Convergence Criterion 58
CHAPTER 4 PROCESS CONTROL AND SIMULATIONS 61
4.1 Controller Structures 61
4.2 Characteristics of INOCS 67
4.3 Numerical Simulations 68
4.4 Concluding Remarks and Summary 74
CHAPTER 5 MODEL-BASED PROCESS CONTROL 101
5.1 Process Parameters Screening 102
5.2 On-Line Control Experiment 102
5.3 Concluding Remarks 112
CHAPTER 6 CONCLUSIONS AND FUTURE WORK 135
6.1 Conclusions 137
6.2 Future Works 140
BIBLIOGRAPHY 142

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