|摘要: ||本文使用田口法進行3D列印之表面粗糙度最佳化分析，使工件接近設計尺寸並減少表面粗糙度。首先使用3D印表機製作立方殼工件，並利用共軛焦雷射顯微鏡量測工件的長度、寬度及表面粗糙度，其中粗糙度參數為粗糙度輪廓曲面的均方根高度，實驗選定的控制因子包含噴頭溫度、進給率、層厚、平台溫度、填充率、填充圖案、空載速度與殼數，最後依因子的總自由度選定 直交表，並將因子及其水準值填入直交表以執行實驗。|
變異數分析結果顯示，對工件長度有顯著影響的因子為進給率、空載速度與殼數，對工件表面粗糙度有顯著影響的因子為層厚及噴頭溫度，而工件寬度無顯著因子，依因子反應信號雜訊比大小分別決定三個品質特性的最佳設計，原始設計為3D印表機標準品質的製程參數組合，將三個品質特性的最佳設計及原始設計當作確認實驗，結果顯示工件長度及工件表面粗糙度的信號雜訊比皆在信心區間內，本文的實驗模式可有效地預測3D列印之最佳工件長度及最佳工件表面粗糙度，最後比較原始設計跟最佳設計的品質特性，其中原始工件長為 ，而最佳工件長為 ，長度約增加1.023%；工件表面粗糙度由21.554 降低至8.887 ，粗糙度約降低58.769%。
This study uses Taguchi method to perform the optimal analysis of surface roughness in 3D printing. The workpiece would meet the designed dimensions and also reduce the surface roughness. Firstly, cubic shell-shaped workpieces were made by 3D printers, and confocal laser scanning microscopy was employed to measure the length, width, and surface roughness of the workpiece, and roughness parameter is the root mean square height of the scale-limited surface. Then, nozzle temperature, feed rate, layer thickness, platform temperature, fill rate, fill patterns, travel speed, and shell numbers were selected as control factors during the experiments. After that, orthogonal arrays were chosen in accordance with total degree of freedom of control factors. These control factors and level values were put into the orthogonal arrays for the experiments.
According to the results obtained from analysis of variance (ANOVA), shell numbers, feed rate, and travel speed significantly influence the length of the workpieces. The surface roughness of the workpieces are highly affected by layer thickness and nozzle temperature, whereas there is no influence on the width of the workpieces significantly. The optimal design of three quality characteristics was found after the signal-to-noise ratios (S/N) of the factorial effects were compared. The combination of the process parameters required by 3D printer’s standard quality was treated as original design. After that, all of quality characteristics’ optimal designs and original design were taken as confirmation experiments. According to the validation, the S/N of length and surface roughness fell within confidence interval, indicating the model employed by this study could accurately predict the length and surface roughness of the workpieces produced by 3D printers. Lastly, original design’s quality characteristics and optimal design’s quality characteristics were compared. The length of the original design’s workpiece was 17.817 mm whereas optimal design’s workpiece was 17.825 mm, indicating the length had increased by 1.023%. The surface roughness of the workpiece dropped from 21.544 to 8.887 , indicating the surface roughness had decreased by 58.769%.