Mohammed Shafae

Interests

Research

Cyber-Physical Systems Security; Smart Manufacturing Systems; Statistical Process Monitoring; Manufacturing Process Data Analytics (Modeling, Monitoring, and Diagnosis); Advanced Metrology Systems Data Driven Quality Control

Courses

Scholarly Contributions

Journals/Publications

• Hasan, N., Saha, A. K., Wessman, A., & Shafae, M. (2024). Machine Learning-based Layer-wise Detection of Overheating Anomaly in LPBF using Photodiode Data. Manufacturing Letters.
  More info

  Overheating anomaly detection is essential for the quality and reliability of parts produced by laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on the detection of overheating anomalies using photodiode sensor data. Photodiode sensors can collect high-frequency data from the melt pool, reflecting the process dynamics and thermal history. Hence, the proposed method offers a machine learning (ML) framework to utilize photodiode sensor data for layer-wise detection of overheating anomalies. In doing so, three sets of features are extracted from the raw photodiode data: MSMM (mean, standard deviation, median, maximum), MSQ (mean, standard deviation, quartiles), and MSD (mean, standard deviation, deciles). These three datasets are used to train several ML classifiers. Cost-sensitive learning is used to handle the class imbalance between the "anomalous" layers (affected by overheating) and "nominal" layers in the benchmark dataset. To boost detection accuracy, our proposed ML framework involves utilizing the majority voting ensemble (MVE) approach. The proposed method is demonstrated using a case study including an open benchmark dataset of photodiode measurements from an LPBF specimen with deliberate overheating anomalies at some layers. The results from the case study demonstrate that the MSD features yield the best performance for all classifiers, and the MVE classifier (with a mean F1-score of 0.8654) surpasses the individual ML classifiers. Moreover, our machine learning methodology achieves superior results (9.66% improvement in mean F1-score) in detecting layer-wise overheating anomalies, surpassing the existing methods in the literature that use the same benchmark dataset. [Journal_ref: ]
• Rahman, M. H., Hamedani, E. Y., Son, Y., & Shafae, M. (2024). Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment. Journal of Computing and Information Science in Engineering, 24(7).
• Rahman, M. H., Hayes, A. K., Muralidharan, K., Loy, D. A., & Shafae, M. (2024). Additive manufacturing of hydrogel-based lunar regolith pastes: A pathway toward in-situ resource utilization and in-space manufacturing. Journal of Manufacturing Processes, 118, 269-282.
• Hasan, N., Rahman, M. H., Wessman, A., Smith, T., & Shafae, M. (2023). Process Defects Knowledge Modeling in Laser Powder Bed Fusion Additive Manufacturing: An Ontological Framework. Manufacturing Letters, 35, 822-833.
• Rahman, M. H., Wuest, T., & Shafae, M. (2023). Manufacturing cybersecurity threat attributes and countermeasures: Review, meta-taxonomy, and use cases of cyberattack taxonomies. Journal of Manufacturing Systems, 68, 196-208.
• Budinoff, H. D., & Shafae, M. (2022). Connecting part geometry and cost for metal powder bed fusion. The International Journal of Advanced Manufacturing Technology, 121(9), 6125-6136.
• Dastoorian, R., Wells, L., & Shafae, M. (2022). Assessing the performance of control charts for detecting previously unexplored shift types in high density spatial data. Quality Engineering, 34(1), 125-141.
• Rahman, M. H., & Shafae, M. (2022). Physics-based detection of cyber-attacks in manufacturing systems: A machining case study. Journal of Manufacturing Systems.
• Budinoff, H. D., Bushra, J., & Shafae, M. (2021). Community-driven PPE production using additive manufacturing during the COVID-19 pandemic: Survey and lessons learned. Journal of Manufacturing Systems, 60, 799-810.
• Shafae, M. S., Wells, L. J., & Camelio, J. A. (2021). Modeling in-process machining data using spatial point cloud vs. time series data structures. Procedia Manufacturing, 53, 44-51.
• Shafae, M. S., Wells, L. J., & Purdy, G. T. (2019). Defending against product-oriented cyber-physical attacks on machining systems. The International Journal of Advanced Manufacturing Technology, 1-21.
• Wells, L. J., Shafae, M. S., & Camelio, J. A. (2016). Automated Surface Defect Detection Using High-Density Data. Journal of Manufacturing Science and Engineering, 138(7), 071001-071001-10.
• Shafae, M. S., Dickinson, R. M., Woodall, W. H., & Camelio, J. A. (2015). Cumulative Sum Control Charts for Monitoring Weibull-distributed Time Between Events. Quality and Reliability Engineering International, 31(5), 839-849.

Proceedings Publications

• Lin, Y. -., Shao, S., Rahman, M. H., Shafae, M., & Satam, P. (2023, 2023). DT4I4-Secure: Digital Twin Framework for Industry 4.0 Systems Security. In 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 0200-0209.
• Zhang, Y., Hasan, N., Middendorf, J., Spears, T., Smith, T., Zhang, F., Shafae, M., & Wessman, A. (2023, 2023). Correlating Alloy Inconel 718 Solidification Microstructure to Local Thermal History Using Laser Powder Bed Fusion Process Monitoring. In Proceedings of the 10th International Symposium on Superalloy 718 and Derivatives. TMS 2023. The Minerals, Metals & Materials Series., 595-611.
• Wells, L. J., Shafae, M. S., & Camelio, J. A. (2013, 2013). Automated Part Inspection Using 3D Point Clouds. In The ASME 2013 International Manufacturing Science and Engineering Conference.