本文關鍵詞： 3D打印 熔絲沉積成型 最優(yōu)放置角度 模型分割 裝箱問題　出處：《西北大學》2016年博士論文　論文類型：學位論文

【摘要】：3D打印是目前的熱點研究領域,熔絲沉積成型作為3D打印中最常用的技術之一,以其低成本、易于維護等特性受到人們的廣泛關注和重視。本文針對熔絲沉積成型中制造精度不高、制造需添加額外支撐等突出問題,研究其幾何計算關鍵技術,旨在提高打印精度及物品可用性、節(jié)約打印耗材和打印時間。主要研究進展包括：(1)提出了模型最優(yōu)放置角度計算方法,分析受放置角度影響的熔絲沉積成型中的六類制造指標,根據(jù)需求建立無約束和帶約束的最優(yōu)放置角度目標函數(shù),利用改進的powell方法求解目標函數(shù)。實驗結果表明,該算法可有效計算各種需求條件下的物體最優(yōu)放置角度,優(yōu)化了制造物體表面精確度,節(jié)約了打印耗材和打印時間,避免部分后處理對模型造成的損害。(2)提出一種基于“熔絲成型”的支撐結構生成算法,針對影響熔絲成型的因素,建立四項熔絲打印成型約束,構造最優(yōu)化目標函數(shù)在模型上計算各類支撐結構的最小支撐區(qū)域,建立代價最小生成樹將支撐結構連接生成完整外部支撐,優(yōu)化了現(xiàn)有支撐結構生成算法或耗材多或打印過程中不穩(wěn)固的問題。與傳統(tǒng)算法相比,充分考慮成型的最基本要素“熔絲成型”,不僅支撐區(qū)域更小,且支撐效果更好,可確保模型的每條打印熔絲均完好成型。實驗結果表明,算法生成的支撐結構在打印過程中能穩(wěn)固支撐模型,在耗材和耗時上均優(yōu)于傳統(tǒng)算法。(3)針對過大物體無法直接放入打印空間的問題,提出一種基于集束搜索的去外部支撐模型分割算法。對模型表面進行分區(qū),根據(jù)各區(qū)域法向分布計算一組切面劃分模型,采用集束搜索方式將所有劃分構造成樹結構,迭代劃分直到所有子模型均為錐體,通過搜索樹得到最優(yōu)劃分。針對家具等模型實驗驗證表明,算法將超出打印空間的模型劃分為符合打印空間的分塊,均可實現(xiàn)無外部支撐打印與分塊組裝使用。(4)提出一種去內(nèi)部支撐的模型分割算法,針對空心物體直接打印時內(nèi)部存在冗余填充物導致其不能使用的問題,算法通過區(qū)域生長在模型表面搜尋不需要內(nèi)部支撐的分區(qū)作為候選分區(qū),利用蒙特卡洛和深度剪枝生成樹兩種搜索方法,將候選分區(qū)不斷組合分化,獲得最優(yōu)的無內(nèi)部支撐分割方案。實驗結果表明,算法運用在容器、花瓶、陶俑等模型上,打印的空心物品無內(nèi)部填充,節(jié)省了打印材料與打印時間,可用性大大提高。(5)提出了緊湊低耗的裝箱智能優(yōu)化算法,針對大規(guī)模生產(chǎn)制造多模型同時打印需求,將裝箱問題解用一組旋轉(zhuǎn)向量和位移向量表示,利用動態(tài)鄰域的局部學習粒子群算法求解該問題,獲得包圍盒最小或外部支撐最小的最優(yōu)裝箱方案,用戶可一次向打印空間放入更多模型,減少人機交互和額外能耗。針對零件、文物碎片批量打印的實驗驗證表明,算法能將多模型緊湊裝箱置于打印空間內(nèi),可有效提高打印空間利用率。
[Abstract]:3D printing is a hot research field at present. As one of the most commonly used technology in 3D printing, fuse deposition molding has low cost. The characteristics such as easy maintenance have been paid more attention to. In this paper, the key technology of geometric calculation is studied in view of the outstanding problems such as the low manufacturing precision and the need to add extra support in the fabrication of fuse deposition molding. The aim of this paper is to improve the accuracy of printing and the availability of objects, and to save printing consumables and printing time. The main research progress includes: (1) A method for calculating the optimal placement angle of the model is proposed. This paper analyzes six kinds of manufacturing indexes in fusing deposition molding influenced by placement angle, and establishes the optimal placement angle objective function of unconstrained and constrained according to the requirement. The improved powell method is used to solve the objective function. The experimental results show that the algorithm can effectively calculate the optimal placement angle of the object under various requirements and optimize the surface accuracy of the manufacturing object. This paper saves printing consumables and printing time, and avoids damage caused by partial post processing to the model. (2) A support structure generation algorithm based on "fuse forming" is proposed, aiming at the factors that affect fuse forming. Four fuse printing constraints are established, and the optimal objective function is constructed to calculate the minimum support region of all kinds of support structures on the model. The minimum spanning tree is established to link the support structure to generate the complete external support. This paper optimizes the existing algorithms for the generation of support structures or the problem of more consumables or instability in the printing process. Compared with the traditional algorithms, the most basic element of forming is fully considered "fuse forming", which not only has a smaller support area. The experimental results show that the support structure generated by the algorithm can support the model stably in the process of printing. In terms of consumables and time consuming, it is better than the traditional algorithm. 3) aiming at the problem that too large objects can not be directly put into print space. A cluster search based model segmentation algorithm is proposed. The surface of the model is partitioned and a set of tangent partition models are calculated according to the normal distribution of each region. All partitions are constructed into tree structure by cluster search, and all submodels are divided into cones iteratively. The optimal partition is obtained by searching the tree. The experimental results for furniture and other models show that. The algorithm divides the model beyond the printing space into blocks that accord with the printing space, and can realize printing and assembling without external support. (4) A model segmentation algorithm without internal support is proposed. In order to solve the problem that the hollow object can not be used because of redundant fillers when printing directly, the algorithm uses regions to search for candidate partitions on the surface of the model that do not require internal support. Monte Carlo and deep pruning tree are used to divide candidate partitions and obtain the optimal segmentation scheme without internal support. The experimental results show that the algorithm is applied to containers and vases. On models such as terracotta warriors and other models, the printed hollow objects have no internal filling, which saves printing materials and printing time, and greatly improves the usability. 5) A compact and low cost intelligent optimization algorithm for packing is proposed. Aiming at the requirement of multi-model printing in mass production, the packing problem is represented by a set of rotation vector and displacement vector, and the local learning particle swarm optimization algorithm of dynamic neighborhood is used to solve the problem. The optimal packing scheme with minimum bounding box or minimum external support is obtained. The user can put more models into print space at a time to reduce man-machine interaction and extra energy consumption. The experimental results of batch printing of fragments show that the algorithm can effectively improve the efficiency of printing space by putting the multi-model compact packing in the printing space.
【學位授予單位】：西北大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.73

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