選擇性雷射燒熔（SLM）是一種加法製造（AM）技術，用來逐層建立復雜的三維零件，使用雷射光束將金屬粉末熔合在一起。目前SLM面臨的最大挑戰之一是找到製造3D複雜零件的最佳加工參數。在這項研究中，將計算機模擬和統計技術結合起來，進而找出製造高密度/較少微裂痕/較低表面粗糙度的簡單三維零件的最佳參數。首先，建立復雜的模擬設計，為了使模擬尺寸達到最小，然後根據粉末粒度分佈建立模擬模型，提取所有熔池特徵。然後建立一個複雜的數據驅動模型（神經網絡）來預測不同參數下的熔池特徵，並建立代替模型來減少電腦模擬的運行時間。某些新的標準被提出，如利用表面粗糙度和溫度梯度來獲得最佳區域。最後得到利用SLM方式製造出密度為99.97％，材料為SS 316L的立方體。此外，利用我們所提出的方法，可以不用切開任何斷面來看熔池深度，而熔池深度就能被預測。因為我們發現熔池深度預測與實驗數據誤差小於3％。

Selective laser melting (SLM) is an additive manufacturing (AM) process that builds complex three-dimensional parts, layer by layer, using a laser beam to fuse fine metal powder together. Current one of the biggest challenges in SLM is to find the optimal process parameters for manufacturing the 3D complex parts. In this study, the method of integrating the computer simulation and statistical techniques to find the optimal process parameters for manufacturing the simple 3D parts with high density/less microcrack/less surface roughness would be proposed. At first, the complex simulation design is constructed to minimize the simulation size and then simulation model will be set up based on powder size distribution, laser power, scanning speed, layer thickness and etc. Finally, the melt pool characteristics like depth and peak temperature in this study will be extracted. And then build up a complex data-driven model (neural network) to predict all the melt pool characteristics with different parameters, and also by establishing the surrogate model in reducing the running time in a computer is implemented in this study. Some new criteria like surface roughness and temperature gradient are proposed to approach the optimal zone. As a result, the density of 99.97% of SS 316L in a cube made by SLM system is obtained. Additionally, based upon the proposed methodology the melt pool depth would be predicted without cutting any cross-section of the melt pool in experiments. It is found that the error between prediction and experimental data in melt pool depth is less than 3%.

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