臺灣博碩士論文加值系統

目前科技正朝向輕薄、短小與多功能的趨勢發展，這使得粒徑大小介於1-100奈米的奈米粉體成為新崛起的先進材料。一般通常是使用濕式機械研磨來製備奈米粉體。而越小的粉體體積尺寸，會有越大的接觸面積與較大的使用效益。因此如何使研磨後粒徑尺寸小與維持粒徑一致性，研磨機的參數設計即成為重要的議題。關於參數設計問題，近年來工程師常運用田口直交表來求得參數設計。然而田口方法本身是為單一品質而設計，而本研究範疇包含粒徑大小與粒徑變異雙品質特性，因此如何找出滿足此雙目標的最佳的製程參數乃是本研究的目的。
本文中先應用多屬性決策中的簡單加權法、灰關聯分析與理想解類似度順序偏好法，將多重品質特性整合為單一品質，並排序得到幾組較佳的參數水準組合。為了更精確得到全域最佳解，運用適應性類神經模糊推論系統（ANFIS）來學習田口實驗的結果，模擬系統輸入參數與輸出品質之間的關係，作為啟發式演算法的目標式，再以快速菁英非凌駕排序基因演算法（NSGAII）來尋找隱藏在田口水準與水準之間的值。並進一步提出三個改良想法：1.以田口中貢獻率的概念來改良演算法中的交配與突變方法；2.應用模糊理論動態調整交配與突變率；3.應用多屬性所得到的解作為演算法搜尋之初始解。經由比較驗證後進而提出改良式NSGAII來搜尋獲得全域最佳化的加工參數組合。
本研究所提出的適應性NSGAII相較於NSGAII，在相同的母群體數目80與演化世代50下，由NSGAII所得到的群集覆蓋比率為0.375；適應性NSGAII所得之群集覆蓋比率為0.238，因此可知適應性NSGAII能找到較靠近柏拉圖前緣的解。

Due to the light, short and multi-function development trend, particles with 1~100 nanometer (nm) grain size become new advanced materials. We usually use wet-type mechanical milling process to make nano-particles. As the grain size of nano-particles decreases, the surface area is increased and the benefit of nano-particle powders increases. So keeping the particle size and size variations small becomes an important issue. Regarding to the optimal parameter design, most engineers use Taguchi methods to investigate the parameter design. It is known that Taguchi method only useful for single quality characteristic. But this research investigates the quality characteristics of particle size and particle variations. Therefore, the purpose of this study is to find out the optimal milling parameter and to optimize the two objectives simultaneously.
First of all, study was the multi-attribution decision making methods combine multi-quality characteristics into single quality characteristic to get several better parameter set. The methods include simple additive weight method (SAW), grey correlation analysis and technique for order performance by similarity to ideal solution (TOPSIS). In order to more precisely get the global optimal solution, we use Adaptive Network- Based Fuzzy Inference System (ANFIS) to learn the result of Taguchi experiment, and use it to simulate the input-output relationship of this system and then use it as the objective function in evolution algorithm. Then, we use non-dominated sorting genetic algorithm (NSGAII) to search the value between levels of Taguchi experiment. In addition, three improved ideas are proposed in this research. First, we apply the concept of contribution rate in Taguchi method to refine the crossover and mutation ways in NSGAII algorithm. Second, we adjust the crossover and mutation ratios in the evolution of each generation by using fuzzy control theory. Third, we use the results of the multi-attribution decision making as the initial solutions in the NSGAII algorithm. After the test and verification, we propose the adaptive NSGAII to search the global optimal solutions.
Under the same population size 80 and evolutional generations 50, the coverage of NSGAII is 0.375, and the coverage of the adaptive NSGAII which we proposed is 0.238. So the adaptive NSGAII compared with NSGAII can find the better optimal solutions that are much closer to the Pareto front.

摘要…………………………. ……………………... ……………………………. i
ABSTRACT …………………………. ………...... ……………………………. ii
誌謝………………………………………………..... ……………………………. iii
目錄………………………………………………..... ……………………………. iv
圖目錄…………………………. …………………... ……………………………. vii
表目錄…………………………. ………………...… ……………………………. ix
第一章 緒論…………………………. ……………. ……………………………. 1
1.1 研究背景與動機 ……………………………. ……………………………. 1
1.2 研究目的…………………………………...... ……………………………. 2
1.3 研究限制…………………………………...... ……………………………. 4
1.4 研究流程圖 …………………………………. ……………………………. 4
第二章 文獻回顧…………………………………... ……………………………. 5
2.1奈米研磨製程………………………………… ……………………………. 5
2.1.1 奈米微粒結構及特徵…………………... ……………………………. 5
2.1.2奈米微粒製備介紹……………………… ….………………………… 5
2.1.3 高能機械珠磨製程……………………... ……………………………. 6
2.2多品質特性…………………………………… ……………………………. 8
2.2.1多屬性決策方法…………………………. ……………………………. 8
2.2.2多品質特性最佳化問題………………… ……………………………. 11
2.2.3多目標演化演算法………………………. ……………………………. 14
2.2.4多品質特性相關研究…………………… ……………………………. 17
第三章 研究方法…………………………………... ……………………………. 19
3.1田口直交表實驗…………………………..… ……………………………. 19
3.2 研究架構…………………………………...... ……………………………. 20
3.3 多屬性決策方法…………………………...... ……………………………. 22
3.3.1 簡單加權法…………………………….. ……………………………. 22
3.3.2 理想解類似度順序偏好法…………...… ……………………………. 23
3.3.3灰關聯分析………………………………. ……………………………. 25
3.4 適應性模糊類神經網路…………………...... ……………………………. 28
3.5快速菁英非凌駕排序基因演算法…………… ….………………………… 31
3.5.1快速菁英非凌駕排序基因演算法………. ……………………………. 32
3.5.2 演算法評估與分析……………………... ……………………………. 36
3.6適應性菁英非凌駕排序基因演算法………… ……………………………. 39
3.6.1 交配與突變方法………………………... ……………………………. 40
3.6.2 交配率與突變率之參數調控…………... ……………………………. 43
第四章 實驗結果與分析…………………………... ……………………………. 47
4.1 田口直交表 …………………………………. ………….………………… 47
4.1.1簡單加權法………………………………. ……………………………. 50
4.1.2理想解類似度順序偏好法……………… ……………………………. 51
4.1.3灰關聯分析………………………………. ……………………………. 52
4.1.4 田口多目標決策方法比較………...…… ……………………………. 53
4.2 ANFIS模型建立…………………………...… ……………………………. 55
4.3 類神經基因演算法實驗與分析…………...... ……………………………. 59
4.4 適應性多目標演算法分析………………...... ……………………………. 62
4.4.1 有初始解……………………………...… ……………………………. 62
4.4.2適應性調控交配率與突變率……………. ……………………………. 63
4.4.3在主因子效應附近搜尋的影響………… ……………………………. 64
4.4.4比較適應性NAGA2與原始之NSGA2.. ……………………………. 65
第五章 結論與建議………………………………... ……………………………. 68
5.1 結論 ……………………………………..... ……………………………. 68
5.2 建議……………………………………...... ……………………………. 69
第六章 參考文獻…………………………………... ……………………………. 70

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