應用蟻群演算法求解3D列印之適應式切層

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姓名

洪瑋成(Wei-Cheng Hung) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

應用蟻群演算法求解3D列印之適應式切層
(Applying ant colony optimization to adaptive slicing for rapid prototyping)

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摘要(中)

近年來自造者的概念不斷水漲船高，3D列印也跟著聲勢看漲。在業界，因為列印的效率低落，使得3D列印仍然停留在初期模型驗證與產品測試的階段，無法達到大量製造的規模，所以若能在不影響3D列印硬體技術下的情況下，提升軟體上的前處理，則可以以最小成本提升列印效率；在學術界，提出了許多優化模型切層的演算法，求解方式仍有需多困難，許多相關研究領域的學者認為此問題仍有相當大的發展空間。
適應式切層（Adaptive Slicing）的問題為一混合整數規劃問題，針對混合整數規劃問題已經有很多的學者利用啟發式演算法對這類問題進行求解，像是蟻群演算法等。本研究採用Wang於2014年提出的適應式切層模型為基礎，發展蟻群演算法來求解此模型。本研究為了提升軟體應用上的效率，提出適應式切層求解之方法，目的在於快速求解切層問題，提升3D列印使用者的體驗與效率。

摘要(英)

In recent years, the concept of maker constantly rising, 3D printing also followed the momentum bullish. In the industry, because the efficiency of 3D printing is low, it remains in the initial model validation and product testing stage. Achieving mass production still has a long way to go. Therefore, if we can improve the software on the pre-treatment under the circumstance of not affecting the 3D print hardware, you can maximize the printing efficiency with very low cost; in academics, a lot of thesis of slicing optimization have be proposed. However, solving the problem in model slicing still faces challenges. Many scholars in this field think that the problem is still considerable.
Adaptive slicing is a mixed-integer programming problem. For mixed-integer programming problems, many scholars have utilized heuristic algorithms to solve such problems, such as ant colony optimization. Based on the mathematical model of adaptive slicing problem, which proposed by Wang in 2014. We develop an ant colony algorithm to solve the problem. In order to decrease the degree of uncomfortable experience for 3D printing user, a fast algorithm for adaptive slicing problem is necessary.

關鍵字(中)

★ 快速成型
★ 蟻群演算法
★ 適應式切層

關鍵字(英)

★ Rapid Prototyping
★ Ant Colony Optimization
★ Adaptive Slicing

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究背景 1
1-2 研究動機與目的 2
第二章文獻探討 3
2-1 快速成型技術 3
2-2 模型切層 7
2-2-1 均勻切層 7
2-2-2 適應式切層問題 9
2-3 啟發式演算法 12
2-3-1 蟻群演算法 12
第三章問題描述與研究方法 17
3-1 技術介紹 17
3-1-1 模型切層輪廓 17
3-1-2 網格顯著性（Mesh Saliency） 18
3-2 問題描述 19
3-3 蟻群演算法之設計 22
3-3-1 取得視覺品質資訊 23
3-3-2 路徑轉換規則 23
3-3-3 費洛蒙釋放機制 24
3-3-4 費洛蒙更新機制 24
3-3-5 蟻群演算法架構 25
第四章實驗設計與結果分析 29
4-1 參數設計 29
4-1-1 螞蟻數量（ANT） 29
4-1-2 費洛蒙濃度初始值（τ0） 30
4-1-3 費洛蒙濃度與視覺品質能見度（α,β） 31
4-1-4 費洛蒙揮發係數（ρ） 32
4-1-5 迭代數（I） 33
4-1-6 小結 34
4-2 ACO_ASP之求解效能測試 34
4-3 總結 35
第五章結論與後續研究 36
5-1 結論 36
5-2 後續研究 36
5-2-1 切層系統架構 36
5-2-2 ACO_ASP程式優化 38
參考文獻 39

參考文獻

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指導教授

王啟泰(Chi-Tai Wang)

審核日期

2017-7-11

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