.png){kind=icon}
返回
Real-Time Stringing Detection for Additive Manufacturing
.png){kind=icon}
OA
AI
DOI：10.3390/jmmp9030074.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Additive Manufacturing (AM), commonly known as 3D printing, has gained significant traction across various industries due to its versatility and customization potential. However, the process remains time-consuming, with print durations ranging from hours to days depending on the complexity and size of the object. In many cases, errors occur due to object misalignment, material stringing due to nozzle overflow, and filament blockages, which can lead to complete print failures. Such errors often go undetected for extended periods, resulting in substantial losses of time and material. This study explores the implementation of traditional computer vision, image processing, and machine learning techniques to enable real-time error detection, specifically focusing on stringing-related anomalies. To address data scarcity in training machine learning models, we also release a new dataset and improve upon the results achieved by the Obico server model, one of the most prominent tools for stringing detection. Our contributions aim to enhance process reliability, reduce material wastage, and optimize time efficiency in AM workflows.
Keyword:
additive manufacturing
error detection
machine learning
computer vision
image processing
blender
期刊
J
IF:
3.3
论文数:
1.9K
被引数:
3.2K
机构
引用论文
Toward Accurate Fused Deposition Modeling 3D Printer Fault Detection Using Improved YOLOv8 With Hyperparameter Optimization使用改进的YOLOv8和超参数优化实现精确的熔融沉积建模3D打印机故障检测.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容