Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors

By P. Rao, J. Liu, D. Roberson, Zhenyu James Kong, Christopher B. Williams. 2015

269 citations recorded by the source corpus.

Subfields: Automotive Engineering, Biomedical Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/33ecd9687c5a5120e1a0d34e6ab5fc00ea56c6c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Yan Wang
2. Haixi Wu
3. Zhonghua Yu

In Industrial and Manufacturing Engineering

1. Yan Wang
2. Haixi Wu
3. Zhonghua Yu

Citations in the corpus

• In situ monitoring of FDM machine condition via acoustic emission (2015, methodology): “Sensing techniques have also been used to ensure the quality of FDM built parts [11, 27, 40, 41].”
• Prediction of surface roughness in extrusion-based additive manufacturing with machine learning (2019, methodology): “[18] introduced an online monitoring method for failure detection in the FFF process.”
• Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model (2017, background): “Abnormal states of machine condition, such as nozzle wear, clogging, material run-out, and filament breakage, may occur during the fabrication process [35,34].”
• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, unknown): “[105], a novel method for detecting fi lament breakage and nozzle clogging in fused deposition modeling (FDM) has been developed, with an SVM as the condition classi fi er.”