應用粒子群最佳化演算法於真圓度量測

Applying Particle Swarm Optimization Algorithm to Roundness Measurement

蔡宗翰(Tsung-Han Tsai)

指導教授 ： 田方治

國立臺北科技大學/管理學院/工業工程與管理系所/碩士(2006年)

摘要

有鑑於產業各界對產品品質要求不斷的日益提高，且在相關自動化檢測技術不斷提昇地當下，為加快產品檢驗的速度以及準確性，發展一簡便且具高效率之檢測系統成為本研究深感興趣的課題。因此，本研究之目的在應用粒子群最佳化演算法(Particle Swarm Optimization, PSO)於機器視覺(Machine Vision)之真圓度量測(Roundness Measurement)。粒子群最佳化演算法為一具有模仿生物群體依賴相似特性之群體智慧(Swarm Intelligence)的概念方法，且具有粒子經驗交換及傳承次代之演算模式，其利用粒子族群具有探測(Exploitation)與開發(Exploration)的特色，於問題空間中搜尋全域的最佳解。除利用PSO法外，本研究在於真圓度量測上共使用三種不同真圓度量測之數學模型法則：最大內切圓法(Maximum Inscribing Circle)、最小外接圓法(Minimum Circumscribing Circle)以及最小環帶圓法(Minimum Zone Circle)進行量測，量測物件選定為三吋矽裸晶圓；在研究中非但將探討粒子群最佳化演算法的參數設計，對於粒子數目、粒子之最大速度及慣性權重等影響搜尋結果之參數作一分析與討論，並提出參數設定之建議值。實驗之末也並將比較粒子群最佳化演算法與基因演算法(Genetic Algorithm)對求解之良窳。經過反覆的實驗結果顯示，本研究所提出以粒子群最佳化演算法於機器視覺之真圓度量測較基因演算法為基礎之量測，速度較快且所得之量測結果更為精準。

關鍵字

機器視覺 ； 真圓度量測 ； 粒子群最佳化演算法 ； 基因演算法

並列摘要

Roundness measurement has been a crucial issue of quality control in industry. This study proposes a machine vision-based roundness measuring method that applies Particle Swarm Optimization Algorithm (PSO) for computing the roundness measurement of maximum inscribing circle (MIC), minimum circumscribing circle (MCC) and minimum zone circle (MZC). This study first conducted an extensive study for five PSO models, in which the impact of inertia weight, maximum velocity and the number of particles on the performance of the particle swarm optimizer was analyzed. Furthermore, the proposed PSO-based method was benchmarked with the GA-based (Genetic Algorithm) method using the derived images of the roundness measurement. The experimental results reveal that the PSO-based method outperforms GA-based method in both accuracy and the efficiency.

並列關鍵字

Machine Vision ； Particle Swarm Optimization Algorithm ； Roundness Measurement ； Genetic Algorithm

參考文獻

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