Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts
Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts
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DOI：
10.1115/1.4038598
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发表时间：
2018-03-01
影响因子：
4
通讯作者：
Bian, Linkan
中科院分区：
文献类型：
--
作者：
Khanzadeh, Mojtaba;Rao, Prahalada;Bian, Linkan
尽管增材制造（AM）可以实现复杂的几何形状，但阻碍其在关键任务应用中使用的主要障碍是AM零件缺乏几何精度。现有的几何尺寸和公差（GD&T）特性是基于简单的界标特征来定义的，因此，需要进行定制以捕获具有复杂几何形状的零件中的细微差异。因此，这项工作的目标是使用称为自组织映射（SOM）的无监督机器学习（ML）方法从激光扫描坐标的大数据集量化增材制造零件的几何偏差。中心假设是，集群识别的SOM对应于特定类型的几何偏差，这反过来又与某些AM工艺条件。在熔丝制造（FFF）AM工艺中，在不同工艺条件下制造的部件上测试该假设。本研究的成果如下：（1）可视化和量化的工艺条件和几何精度之间的联系，在FFF和（2）显着减少所需的点云数据的数量来表征几何精度。这项研究的重要性在于，这种无监督ML方法导致完全量化零件几何精度所需的数据点不到100多万个数据点中的3%。
Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.