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首页> 美国卫生研究院文献>Polymers >Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing
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Use of Data Mining Techniques for the Prediction of Surface Roughness of Printed Parts in Polylactic Acid (PLA) by Fused Deposition Modeling (FDM): A Practical Application in Frame Glasses Manufacturing

机译：数据挖掘技术通过熔融沉积建模（FDM）预测聚乳酸（PLA）中印刷零件的表面粗糙度：在​​眼镜框制造中的实际应用

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摘要

In the present work, ten data mining algorithms have been used to generate models capable of predicting the surface roughness of parts printed on polylactic acid (PLA) by using fused deposition modeling (FDM). The models have been trained using experimental data measured on 27 horizontal (XY) and 27 vertical (XZ) specimens, printed using different values for the parameters studied (layer height, extrusion temperature, print speed, print acceleration and flow). The models generated by multilayer perceptron (MLP) and logistic model trees (LMT) have obtained the best results in a cross-validation. Although it does not obtain such optimal results, the J48 algorithm (C4.5) allows the generation of models in the form of a decision tree. These trees permit to determine which print parameters have an influence on the surface roughness. For XY specimens, the surface roughness measured in the direction parallel to the extrusion path (R ) depends on the flow, the print temperature and the layer height; in the direction perpendicular to the extrusion path, the surface roughness (R ) depends only on the flow. For XZ specimens, the surface roughness measured in the direction parallel to the extrusion path (R ) depends only on the print speed; in the direction perpendicular to the extrusion path (R ), it depends on the layer height and the extrusion temperature. According to the study carried out, the most suitable set up provides values of R , R , R and R equal to 0.46, 1.18, 0.45 and 11.54, respectively. A practical application of this work is the manufacture of PLA frame glasses using FDM.
机译：在当前的工作中，已使用十种数据挖掘算法来生成能够通过使用熔融沉积建模（FDM）预测印在聚乳酸（PLA）上的零件的表面粗糙度的模型。使用在27个水平（XY）和27个垂直（XZ）样品上测得的实验数据对模型进行了训练，并针对所研究的参数（层高，挤出温度，印刷速度，印刷加速度和流动）使用不同的值进行印刷。多层感知器（MLP）和逻辑模型树（LMT）生成的模型在交叉验证中获得了最佳结果。尽管无法获得最佳结果，但J48算法（C4.5）允许以决策树的形式生成模型。这些树可以确定哪些打印参数对表面粗糙度有影响。对于XY标本，在平行于挤出路径（R）的方向上测量的表面粗糙度取决于流量，印刷温度和层高；在垂直于挤出路径的方向上，表面粗糙度（R）仅取决于流动。对于XZ样品，在平行于挤出路径（R）的方向上测量的表面粗糙度仅取决于打印速度；在垂直于挤出路径（R）的方向上，它取决于层高和挤出温度。根据进行的研究，最合适的设置提供的R，R，R和R值分别等于0.46、1.18、0.45和11.54。这项工作的实际应用是使用FDM制造PLA框架眼镜。

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