Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks

By S. Shevchik, C. Kenel, Christian Leinenbach, K. Wasmer. 2017

405 citations recorded by the source corpus.

Subfields: Automotive Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/62c4539204bc355ec7805344cdd7984ea6ed5352

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. T. Mukherjee
2. T. DebRoy
3. H. Wei
4. W. Y. Yeong
5. Swee Leong Sing
6. J. Elmer
7. Huijun Li
8. Zengxi Pan

In Mechanical Engineering

1. T. Mukherjee
2. T. DebRoy
3. H. Wei
4. W. Y. Yeong
5. J. Elmer
6. Swee Leong Sing
7. J. Milewski
8. Zengxi Pan

In Mechanics of Materials

1. T. Mukherjee
2. T. DebRoy

Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, methodology): “[209] S.”
• In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review (2021, methodology): “[30] utilised Acoustic emission spectroscopy AES and artificial neural network analysis to categorise samples (poor, medium, and high quality) as defined through ratings of part porosity.”
• Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “[35,36] presented a study on in situ quality monitoring for SLM using acoustic emission (AE) and NNs, which is depicted in Fig.”
• Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches (2020, methodology): “Combining acoustic emission (recorded using a fiber Bragg grating sensor) and training a neural network, Shevchik et al [75, 76] performed in situ quality monitoring in MAM to identify the formation of porosity during the build, as presented in figure 10.”
• A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system (2020, methodology): “1007/s00170-016-9366-x [73] Shevchik, S.”
• A review on machine learning in 3D printing: applications, potential, and challenges (2020, result): “Spectral convolutional neural networks (SCNN) was found to have higher classification accuracies (83, 85 and 89% for high, medium and poor workpiece qualities) (Shevchik et al. 2018) compared to the reinforced learning technique (74, 79 and 82% for high, medium and poor workpiece qualities) (Wasmer ”
• Metallurgy, mechanistic models and machine learning in metal printing (2020, background): “…; geometry prediction 167 ; thermal deformation compensation 203 ; process parameter optimization 204 ; anomaly detection 174 ; quality monitoring 205,206 ; topology optimization 207 Decision tree Progressively classifies a group of variables based on rules and displays them as an upside-down…”