MOCONT

MOnitoring and CONTrol of AM metal process

Additive manufacturing, also known as 3D printing, is a new technology that obliterates the geometrical limits of the produced workpieces and promises low running costs as compared to traditional subtractive manufacturing methods. Although it has high expectations in industry, the absence of a proper in situ and real-time quality monitoring and control prohibits the penetration of this technology into an extensive practice.

Today, most monitoring solutions for additive manufacturing (AM) process available on the market are based on visual analysis and temperature measurements. We believe that these technologies have many drawbacks. Hence, within this project, the team will take another very innovative approach to tackle the in situ and real-time quality control monitoring of the AM process. We want to combine various sensors (acoustic and optical), with Machine Learning (ML) and/or Artificial Intelligence (AI).

Scope of Research Activities

Getting a fundamental understanding of the AM process using selective laser melting (SLM), including the creation of defects
Elaboration of process maps and semi-empirical models for producing specific SLM samples
Characterizing the SLM samples in terms of samples quality and defects as well as material properties
Development and validation of a signal processing unit based on ML for defect classification
Development of algorithm for cracks localization due to residual stresses
Development of a universal model able to predict the defect creation

Key Challenges and Technical Problems to Solve

Classification of the type of defects with high confidence
Localization of cracks due to residual stresses
Ability to predict the creation of a defect in order to prevent the defect

Demonstrator

Method and device for pseudo real-time and in situ quality control monitoring of an SLM process able to classify the samples quality with 95% confidence and/or localize cracks.

Linked Scientific Publications

2022

Drissi-Daoudi, R., V. Pandiyan, R. Logé, S. Shevchik, G. Masinelli, H. Ghasemi-Tabasi, A. Parrilli and K. Wasmer (2022). "Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning." Virtual and Physical Prototyping 17(2): 181-204.
Pandiyan, V., R. Drissi-Daoudi, S. Shevchik, G. Masinelli, T. Le-Quang, R. Logé and K. Wasmer (2022). "Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process." Journal of Materials Processing Technology 303: 117531.

2021

Pandiyan, V., R. Drissi-Daoudi, S. Shevchik, G. Masinelli, T. Le-Quang, R. Logé and K. Wasmer (2021). "Semi-supervised Monitoring of Laser powder bed fusion process based on acoustic emissions." Virtual and Physical Prototyping 16(4): 481-497.

2020

Pandiyan, V., R. Drissi-Daoudi, S. Shevchik, G. Masinelli, R. Logé and K. Wasmer (2020). "Analysis of time, frequency and time-frequency domain features from acoustic emissions during Laser Powder-Bed fusion process." Procedia CIRP 94: 392-397.
Shevchik, S., T. Le-Quang, B. Meylan, F. V. Farahani, M. P. Olbinado, A. Rack, G. Masinelli, C. Leinenbach and K. Wasmer (2020). "Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance." Scientific Reports 10(1): 3389.

2019

Shevchik, S. A., G. Masinelli, C. Kenel, C. Leinenbach and K. Wasmer (2019). "Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission." IEEE Transactions on Industrial Informatics 15(9): 5194-5203.
Wasmer, K., T. Le-Quang, B. Meylan and S. A. Shevchik (2019). "In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach." Journal of Materials Engineering and Performance 28(2): 666-672.

2018

Le-Quang, T., S. A. Shevchik, B. Meylan, F. Vakili-Farahani, M. P. Olbinado, A. Rack and K. Wasmer (2018). "Why is in situ quality control of laser keyhole welding a real challenge?" Procedia CIRP 74: 649-653.
Shevchik, S. A., C. Kenel, C. Leinenbach and K. Wasmer (2018). "Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks." Additive Manufacturing 21: 598-604.
Wasmer, K., T. Le-Quang, B. Meylan, F. Vakili-Farahani, M. P. Olbinado, A. Rack and S. A. Shevchik (2018). "Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach." Procedia CIRP 74: 654-658.

Leading Principal Investigator

Dr. Kilian Wasmer
Laboratory for Advanced Materials Processing, Empa

Project Consortium

Prof. Dr. Roland Logé
Laboratory of Thermomechanical Metallurgy (LMTM), EPFL
Prof. Dr. Markus Strobl / Dr. Pavel Trtik
Neutron Imaging and Activation (NIAG), PSI
Dr. Rolf Kaufmann
X-Ray Center, Empa
Dr. François Fleuret
Machine Learning Group,
Idiap Research Institute