3D- EDM：3D打印机故障早期检测模型
3D-EDM: Early Detection Model for 3D-Printer Faults
摘要：
随着不同价格范围和尺寸的3D打印机的出现，它们不再仅仅适用于专业人士。然而，完美地使用3D打印机仍然具有挑战性。特别是，在熔融沉积法的情况下，很难进行精确的校准。以前的研究表明，这些问题可以通过机器学习方法使用传感器数据和图像数据来检测。然而，由于额外安装额外的传感器，因此难以应用所提出的方法。考虑到未来的实际应用，我们专注于生成轻量级的早期检测模型，易于收集的数据。提出的早期检测模型，通过卷积神经网络表现出显着的故障分类准确率为96.72%的二分类任务，93.38%的多分类任务分别。通过本研究，希望普通3D打印机用户能够准确使用3D打印机。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
