Max(imilian) Schmitz

Max(imilian) Schmitz

Software Engineer II | Master of Science in Engineering Cybernetics | Master of Science in Engineering Science and Mechanics

Publications

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

Published in Ultrasonics, 2024

This research investigates the use of deep learning for the use in a delamination evaluation technique by using guided wave time-space images.

Recommended citation: Junzhen Wang, Maximilian Schmitz, Laurence J. Jacobs, Jianmin Qu, Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves, Ultrasonics, Volume 141, 2024, 107351, ISSN 0041-624X, https://doi.org/10.1016/j.ultras.2024.107351. https://www.sciencedirect.com/science/article/abs/pii/S0165212523000239

Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves

Published in SPIE Smart Structures + Nondestructive Evaluation, 2024

This research investigates the use of an LSTM to predict interfacial conditions on a coated plate.

Recommended citation: Junzhen Wang, Maximilian Schmitz, Laurence J. Jacobs, and Jianmin Qu, Deep learning-based prediction of interfacial conditions in coated plates using guided waves, Proc. SPIE 12951, Health Monitoring of Structural and Biological Systems XVIII, 129511F (9 May 2024); https://doi.org/10.1117/12.3010200. https://doi.org/10.1117/12.3010200

Machine and Deep Learning for Coating Thickness Prediction using Lamb Waves

Published in Wave Motion, 2022

This research investigates the use of machine and deep learning methods for wave inversion in nondestructive evaluation.

Recommended citation: Maximilian Schmitz, Jin-Yeon Kim, Laurence J. Jacobs, Machine and deep learning for coating thickness prediction using Lamb waves, Wave Motion, Volume 120, 2023, 103137, ISSN 0165-2125, https://doi.org/10.1016/j.wavemoti.2023.103137. https://www.sciencedirect.com/science/article/abs/pii/S0165212523000239

Deep Learning in Ultrasonic Wave Inversion for Thin Coatings

Published in Georgia Tech, 2022

This research focuses on the use of machine and deep learning to non-destructively characterize the quality of a coating in a layered system in terms of thickness and uniformness.

Recommended citation: Maximilian Schmitz, Deep Learning in Ultrasonic Wave Inversion for Thin Coatings, Master’s Thesis, Georgia Institute of Technology, Atlanta, GA, 2022. http://hdl.handle.net/1853/66518

Gaussian Processes for Automatic Controller Gains Tuning in Robotics and Control (unpublished)

Published in (unpublished), 2020

This paper was the final documentation in the class ECE6254 Statistical Machine Learning and describes the use of Gaussian processes for automatic and safe controller gains tuning.

Recommended citation: Schmitz, Maximilian, Gray, Justin, Oh, Jaeyo, Lu, Yuwei, Kanwar, Bharat (2022). "Gaussian Processes for Automatic Con- troller Gains Tuning in Robotics and Control." (unpublished). https://github.com/sjmxschm/sjmxschm.github.io/raw/master/files/ece_6254_gps_project_report.pdf

Project Report: Optimize a Single Track Vehicle Model with Non-linear State-Feedback Controller (unpublished)

Published in (unpublished), 2020

This paper worked as a final project report for the engineering cybernetics graduate project competition in advanced concepts of control theory. It describes a the development of the used control algorithms as well as the optimization of the race trajectory.

Recommended citation: Schmitz, Maximilian, Ruehle, Josias. (2020). "Project Report: Optimize a Single Track Vehicle Model with Non-linear State-Feedback Controller ." (unpublished). https://github.com/sjmxschm/sjmxschm.github.io/raw/master/files/KRT_reportGroup20.pdf