Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms
Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms
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DOI：
10.1007/s10845-020-01567-0
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发表时间：
2020-04-08
影响因子：
8.3
通讯作者：
Martinsen, Kristian
中科院分区：
文献类型：
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作者：
Baturynska, Ivanna;Martinsen, Kristian
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。为了使增材制造适用于医疗、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。在之前发表的文章中，作者使用统计分析开发了线性模型来预测激光烧结样本的尺寸特征。生产了两个具有相同材料、工艺和构建参数的相同构建，从而产生了 434 个用于机械测试的样品 (ISO 527-2 1BA)。开发的线性模型精度较低，因此需要应用更先进的数据分析技术。在这项工作中，机器学习技术应用于相同的数据，并将结果与先前报告的线性模型进行比较。线性回归模型对于宽度来说是最好的。多层感知器和梯度增强回归器模型在厚度和长度方面优于其他模型。提出了有关未来如何使用所开发模型的建议。
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.