Dimensional accuracy in additive manufacturing (AM) is still an issue compared with the tolerances for injection molding. In order to make AM suitable for the medical, aerospace, and automotive industries, geometry variations should be controlled and managed with a tight tolerance range. In the previously published article, the authors used statistical analysis to develop linear models for the prediction of dimensional features of laser-sintered specimens. Two identical builds with the same material, process, and build parameters were produced, resulting in 434 samples for mechanical testing (ISO 527-2 1BA). The developed linear models had low accuracy, and therefore needed an application of more advanced data analysis techniques. In this work, machine learning techniques are applied for the same data, and results are compared with the previously reported linear models. The linear regression model is the best for width. Multilayer perceptron and gradient boost regressor models have outperformed other for thickness and length. The recommendations on how the developed models can be used in the future are proposed.

与注塑成型的公差相比，增材制造（AM）的尺寸精度仍然是一个问题。为了使AM适用于医疗，航空航天和汽车行业，应在严格的公差范围内控制和管理几何形状变化。在先前发表的文章中，作者使用统计分析来开发线性模型来预测激光烧结试样的尺寸特征。使用相同的材​​料，工艺和构建参数生成了两个相同的构建，从而产生了434个用于机械测试的样本（ISO 527-2 1BA）。所开发的线性模型精度较低，因此需要应用更高级的数据分析技术。在这项工作中，机器学习技术被应用于相同的数据，并将结果与​​先前报道的线性模型进行比较。线性回归模型最适合宽度。多层感知器和梯度增强回归模型在厚度和长度方面均优于其他模型。提出了有关将来如何使用已开发模型的建议。