To find a robust combination of selective laser melting (SLM) process parameters to achieve the highest relative density of 3D printed parts, predicting the relative density of 316L stainless steel 3D printed parts was studied using a set of machine learning algorithms. The SLM process brings about the possibility to process metal powders and built complex geometries. However, this technology’s applicability is limited due to the inherent anisotropy of the layered manufacturing process, which generates porosity between adjacent layers, accelerating wear of the built parts when in service. To reduce interlayer porosity, the selection of SLM process parameters has to be properly optimized. The relative density of these manufactured objects is affected by porosity and is a function of process parameters, rendering it a challenging optimization task to solve. In this work, seven supervised machine learning regressors (i.e., support vector machine, decision tree, random forest, gradient boosting, Gaussian process, K-nearest neighbors, multi-layer perceptron) were trained to predict the relative density of 316L stainless steel samples produced by the SLM process. For this purpose, a total of 112 data sets were assembled from a deep literature review, and 5-fold cross-validation was applied to assess the regressor error. The accuracy of the predictions was evaluated by defining an index of merit, i.e., the norm of a vector whose components are the statistical metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). From this index of merit, it is established that the use of gradient boosting regressor shows the highest accuracy, followed by multi-layer perceptron and random forest regressor.

为了找到选择性激光熔化（SLM）工艺参数的可靠组合以实现3D打印零件的最高相对密度，使用一组机器学习算法研究了预测316L不锈钢3D打印零件的相对密度。SLM工艺带来了加工金属粉末和构建复杂几何形状的可能性。但是，由于分层制造过程的固有各向异性，该技术的适用性受到限制，该各向异性会在相邻层之间产生孔隙，从而在使用时会加速内置零件的磨损。为了减少层间孔隙率，必须适当优化SLM工艺参数的选择。这些制成品的相对密度受孔隙率的影响，并且是工艺参数的函数，使其面临着艰巨的优化任务。在这项工作中，训练了七个监督机器学习回归器（即，支持向量机，决策树，随机森林，梯度提升，高斯过程，K近邻，多层感知器），以预测316L不锈钢样品的相对密度。由SLM流程生产。为此，从深入的文献综述中收集了总共112个数据集，并进行了5倍交叉验证以评估回归误差。预测的准确性是通过定义一个指标来评估的，即指标的向量的范数，其成分是统计指标：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（支持向量机，决策树，随机森林，梯度提升，高斯过程，K近邻，多层感知器）经过训练，可以预测SLM过程生产的316L不锈钢样品的相对密度。为此，从深入的文献综述中收集了总共112个数据集，并进行了5倍交叉验证以评估回归误差。预测的准确性是通过定义一个指标来评估的，即指标的向量的范数，其成分是统计指标：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（支持向量机，决策树，随机森林，梯度提升，高斯过程，K近邻，多层感知器）经过训练，可以预测SLM过程生产的316L不锈钢样品的相对密度。为此，从深入的文献综述中收集了总共112个数据集，并进行了5倍交叉验证以评估回归误差。预测的准确性是通过定义一个指标来评估的，即指标的向量的范数，其成分是统计指标：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（对多层感知器进行了培训，以预测SLM工艺生产的316L不锈钢样品的相对密度。为此，从深入的文献综述中收集了总共112个数据集，并进行了5倍交叉验证以评估回归误差。预测的准确性是通过定义一个指标来评估的，即指标的向量的范数，其成分是统计指标：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（对多层感知器进行了培训，以预测SLM工艺生产的316L不锈钢样品的相对密度。为此，从深入的文献综述中收集了总共112个数据集，并进行了5倍交叉验证以评估回归误差。预测的准确性是通过定义一个指标来评估的，即指标的向量的范数，其成分是统计指标：均方根误差（RMSE），平均绝对误差（MAE）和确定系数（R ²）。根据该指标，可以确定使用梯度增强回归器显示出最高的准确性，其次是多层感知器和随机森林回归器。