Online monitoring

Dynamical processes such as laser, complex friction or crack related failure are in many cases either very difficult or time consuming to model. The main reasons are divers such as high non-linearity in the process (laser), or high stochasticity in the process (tribology and fracture mechanics). Under such circumstances, online monitoring is the only alternative to guarantee the process robustness. Hence, research at Empa focused on developing novel in situ and real-time monitoring systems for online diagnostic of components and/or structural materials. In this field, our mission is threefold.

  • First, get fundamental understanding of the dynamical processes.

  • Second, getting a full understanding of the sensors and develop opto-acoustic sensors

  • Third, develop new machine learning (ML) or Artificial Intelligence (AI) algorithms

  • Fourth, putting 1-3 together, develop new in situ and real-time monitoring systems to detect any process quality deviation, defect creation and/or microstructure and predict the remaining lifetime for components and/or structural materials used in industry.

 

 

Selected projects

  • In situ and real-time monitoring of AM process

  • In situ and real-time monitoring of laser welding processes

  • Control of laser welding process

  • In situ and real-time monitoring of laser osteotomy

  • Online monitoring of tribological systems to avoid friction-related failures

  • Online monitoring of pre-weakening of rocks using electrical discharge

 

Selected publications

  • Shevchik S.A., Kenhagho H.N., Le-Quang T., Neige F., Meylan B., Guzman R., Cattin P., Zam A., and Wasmer K., "Machine Learning Monitoring for Laser Osteotomy", Journal of Biophotonics, Vol. 14, Issue 4, paper ID: e202000352, pp: 1-11, 2021,
    https://doi.org/10.1002/JBIO.202000352

  • Shevchik S.A., Zanoli S., Saeidi F., Meylan B., Flück G., Wasmer K., "Monitoring of Friction-Related Failures Using Diffusion Maps of Acoustic Time Series" Mechanical Systems and Signal Processing, Vol. 148, Issue February 2021, paper ID: 107172, pp: 1-14, 2021,
    https://doi.org/10.1016/j.ymssp.2020.107172

  • Meylan B., Shevchik S.A., Parvaz D., Mosaddeghi A., Simov V., and Wasmer K., "Acoustic Emission and Machine Learning for In Situ Monitoring of a Gold–Copper Ore Weakening by Electric Pulse", Journal of Cleaner Production, Vol. 280, Issue: 1, Paper ID: 124348, pp: 1-12, 2021,
    https://doi.org/10.1016/j.jclepro.2020.124348

  • Pandiyan V., Drissi-Daoudi R., Shevchik S.A., Masinelli G., Logé R., and Wasmer K., "Analysis of Time, Frequency and Time-Frequency Domain Features From Acoustic Emissions During Laser Powder-Bed Fusion Process" Procedia CIRP, Vol. 94, Issue 2020, pp: 392–397, 2020,
    https://doi.org/10.1016/j.procir.2020.09.152

  • Masinelli G., Le-Quang T., Zanoli S., Wasmer K., and Shevchik S.A., “Adaptive Laser Welding Control: A Reinforcement Learning Approach", IEEE Access, Vol. 8, pp: 103803 - 103814, 2020,
    https://doi.org/10.1109/ACCESS.2020.2998052

  • Shevchik S.A., Le-Quang T., Meylan B., Vakili-Farahani F., Olbinado M.P., Rack A., Masinelli G., Leinenbach C., and Wasmer K., “Supervised Deep Learning for Real-Time Quality Monitoring of Laser Welding with X-Ray Radiographic Guidance", Scientific Report, Vol. 10, paper ID: 3389, 2020,
    https://doi.org/10.1038/s41598-020-60294-x

  • Shevchik S.A., Masinelli G., Kenel C., Leinenbach C., and Wasmer K., “Deep Learning For In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission”, IEEE Transactions on Industrial Informatics, Vol. 19, Issue 9, pp: 5194-5203, 2019, https://doi.org/10.1109/TII.2019.2910524

  • Shevchik S.A., Kenel C., Leinenbach C. and Wasmer K., “Acoustic Emission for In Situ Quality Monitoring in Additive Manufacturing Using Spectral Convolutional Neural Networks”, Additive Manufacturing, Vol. 21, Issue May 2018, pp: 598-604, 2018,
    https://doi.org/10.1016/j.addma.2017.11.012

 

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