(1. 山東理工大學(xué) 信息中心，淄博 255000；
2. 山東理工大學(xué) 機(jī)械工程學(xué)院，淄博 255000)
摘 要: 基于高斯過程回歸(GPR)模型，對激光粉末床熔融Ti-6Al-4V合金的致密度和表面粗糙度觀測數(shù)據(jù)進(jìn)行了機(jī)器學(xué)習(xí)，得到了高致密度合金樣品的激光功率-掃描速度的工藝優(yōu)化窗口，并探討了激光功率-掃描速度對表面粗糙度的影響。結(jié)果表明：獲得高致密(≥99.5%)合金的激光功率-掃描速度工藝窗口呈梨形，掃描速度比激光功率對致密度影響更大，且高功率條件下適宜的掃描速度范圍較寬。降低激光功率和提高掃描速度會單調(diào)增加表面粗糙度，且在低激光功率和高掃描速度下該影響更顯著。同一激光能量密度下打印的合金致密度取決于具體的掃描速度和激光功率，但表面粗糙度基本相同。優(yōu)化工藝窗口下樣品的表面粗糙度小于10 μm。實(shí)驗(yàn)證明GPR預(yù)測的優(yōu)化工藝窗口是可靠的，該方法可拓展應(yīng)用到其他合金增材工藝優(yōu)化設(shè)計中。
關(guān)鍵字: 激光粉末床熔融；機(jī)器學(xué)習(xí)；激光功率；掃描速度
（1. Information Centre, Shandong University of Technology, Zibo 255000, China;
2. School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China）
Abstract:A machine-learning approach based on Gaussian Process Regression (GPR) was proposed to optimize the processing window of laser power and scanning speed in the Ti-6Al-4V alloy fabricated by laser powder bed fusion (L-PBF). The effect of laser power-scanning speed on surface roughness of the samples was investigated as well. The predicted results from the model show that the optimized L-PBF processing window for manufacturing fully dense Ti-6Al-4V alloy with relative density ≥99.5% is pear-shaped. It is suggested that the scanning speed is more influential than laser power in relative density of the L-PBFed alloy, and the wide favorable scanning speed range can be obtained in the case of high laser power. The lower power and high scanning speed tend to increase surface roughness monotonously and the effect become more pronounced as power decreasing and scanning speed increasing. The relative density of the L-PBFed alloy depends on the specific scanning speed and laser power rather than a single energy density value. However, the surface roughness significantly depends on the energy density and the same energy density employed leads to the similar surface roughness. The optimized laser power-scanning speed processing window brings about the highly dense alloy with surface roughness less than 10 μm. The further experimental evidence proved that the GPR model established in this study is reliable and can be readily applied to the L-PBF process optimization of other metals and alloys.
Key words: laser powder bed fusion; machine learning; laser power; scanning speed
