FDM 3D打印中YOLOv5模型挤压下故障检测的比较

Muhammad Lut, Liwauddin Abd Latib, M. A. Ayob, Nurasyeera Rohaziat
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引用次数: 0

摘要

熔融沉积材料(FDM) 3D打印技术已经发展到可以与各种材料一起使用的地步。然而,它仍然面临着质量控制方面的挑战,因为在整个打印过程中,打印模型中的缺陷经常无法被发现。因此,时间和材料很容易浪费。通过采用智能系统,可以检测整个打印过程中的故障,并暂停或停止打印活动以应用纠正措施,可以防止此类问题。因此,本文采用尺寸为n、s、m、l和xl的YOLOv5模型,采用600、1200和2400张图像三组不同的数据,对3D打印中挤压下的失效对象检测进行了研究。仿真结果表明,YOLOv5模型能够根据模型尺寸和数据集的不同,有效地检测出3D打印中的挤压失效。结果表明,在所有模型和数据集中,包含2400张图像的YOLOv5xl模型的检测精度最高。这些发现对于提高3D打印过程的可靠性和效率具有潜在的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
YOLOv5 Models Comparison of Under Extrusion Failure Detection in FDM 3D Printing
The fused deposited material (FDM) 3D printing technique has advanced to the point that it can now be utilised with a variety of materials. However, it still confronts challenges in quality control as flaws in the printed model frequently go undetected throughout the printing process. Consequently, time and materials easily go to waste. By employing an intelligent system that can detect faults throughout the printing process and pauses or halts the printing activity to apply corrective actions can prevent such issues. Hence, this paper presents a study on failure object detection of under extrusion in 3D printing by using YOLOv5 models of size n, s, m, l and xl with three different sets of data consisting of 600, 1200 and 2400 images. Simulation results showed that the YOLOv5 models were able to effectively detect under extrusion failures in 3D printing with varying accuracy based on the model size and data set. Based on the results, the YOLOv5xl model with a dataset of 2400 images achieved the highest detection accuracy among all models and datasets. These findings have potential implications for improving the reliability and efficiency of 3D printing process.
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