论文标题
3D-EDM：3D打故障的早期检测模型
3D-EDM: Early Detection Model for 3D-Printer Faults
论文作者
论文摘要
随着3D打印机的出现，价格范围和尺寸不同，它们不再仅适用于专业人士。但是，完美使用3D打印机仍然具有挑战性。特别是在融合沉积法的情况下，很难通过准确的校准执行非常困难。先前的研究表明，使用机器学习方法的传感器数据和图像数据可以检测到这些问题。但是，由于额外安装其他传感器，很难应用提出的方法。考虑到将来的实际用途，我们专注于使用易于收集数据的轻质早期检测模型。通过卷积神经网络提出的早期检测模型显示出明显的故障分类精度，二进制分类任务为96.72％，多分类任务分别为93.38％。通过这项研究，我们希望3D打印机的普通用户可以准确使用打印机。
With the advent of 3D printers in different price ranges and sizes, they are no longer just for professionals. However, it is still challenging to use a 3D printer perfectly. Especially, in the case of the Fused Deposition Method, it is very difficult to perform with accurate calibration. Previous studies have suggested that these problems can be detected using sensor data and image data with machine learning methods. However, there are difficulties to apply the proposed method due to extra installation of additional sensors. Considering actual use in the future, we focus on generating the lightweight early detection model with easily collectable data. Proposed early detection model through Convolutional Neural Network shows significant fault classification accuracy with 96.72% for the binary classification task, and 93.38% for multi-classification task respectively. By this research, we hope that general users of 3D printers can use the printer accurately.
