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

• Achilli, A., Norwood, R. A., Betancourt, W. Q., Ikner, L. A., Saez, A. E., Hamilton, K., Larkin, L., Morrison, D., Hickenbottom, K., Quon, H., Ashraf, A., Amoh-Asante, N., Shafae, M., Jung, Y., & Wilson, A. M. (2025).

  Public risk perceptions of advanced water purification in an arid urban region of the US southwest: A mixed methods study.

  . Science of the Total Environment, 180558.
• Mccormick, M. R., Shafae, M., & Wuest, T. (2025). Machine Tool Interoperability in Smart Manufacturing and Industry 4.0. IEEE Access, 13(Issue). doi:10.1109/access.2025.3585766
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  Real-time decision making is supported by data-hungry emerging technologies such as machine learning and digital twins. Innovations in manufacturing process control utilizing these technologies are constrained by interoperability. Marketing materials, sales pitches, and academic literature portray interoperability on the factory floor as seamless and robust. However, this study demonstrates that in spite of plentiful mature standards, interoperability on the factory floor is neither seamless nor robust. To exemplify interoperability in the context of an established and widely adopted standard, this study analyzes the interoperability of machine tools produced by a premier equipment builder which has exceeded US$1 billion in sales with more than 200,000 machines currently in operation worldwide. Through demonstrating and discussing non-interoperability and influencing factors, this study aims to provide insights and actionable intelligence applicable to industry and academia for laymen, end users, systems integrators, standards organizations, equipment builders, and data scientists. By leveraging phronesis and empirical evidence to demonstrate the state of interoperability, this study provides a fresh perspective on an age-old manufacturing challenge.
• Rahman, M. H., & Shafae, M. (2025). Cyber-Physical Security Vulnerabilities Identification and Classification in Smart Manufacturing: A Defense-in-Depth Driven Framework and Taxonomy. Journal of Computing and Information Science in Engineering, 25(Issue 9). doi:10.1115/1.4068844
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  The increasing cybersecurity threats to critical manufacturing infrastructure necessitate proactive strategies for vulnerability identification, classification, and assessment. Traditional approaches, which define vulnerabilities as weaknesses in computational logic or information systems, often overlook the physical and cyber-physical dimensions critical to manufacturing systems, comprising intertwined cyber, physical, and human elements. As a result, existing solutions fall short of addressing the complex, domain-specific vulnerabilities of manufacturing environments. To bridge this gap, this work redefines vulnerabilities in the manufacturing context by introducing a novel characterization based on the duality between vulnerabilities and defenses. Vulnerabilities are conceptualized as exploitable gaps within various defense layers, enabling a structured investigation of manufacturing systems. This article presents a manufacturing-specific cyber-physical defense-in-depth model, highlighting how security-aware personnel, postproduction inspection systems, and process monitoring approaches can complement traditional cyber defenses to enhance system resilience. By leveraging this model, we systematically identify and classify vulnerabilities across the manufacturing cyberspace, human element, postproduction inspection systems, production process monitoring, and organizational policies and procedures. This comprehensive classification introduces the first taxonomy of cyber-physical vulnerabilities in smart manufacturing systems, providing practitioners with a structured framework for addressing vulnerabilities at both the system and process levels. Finally, we demonstrate the application of the proposed framework through an illustrative smart manufacturing system, detailing its threat model, cyber-physical defense-in-depth strategy, vulnerability identification and mapping to the attack kill chain, empirical attack analysis, and potential mitigation strategies. This work equips manufacturers with actionable insights and a robust classification scheme to proactively address the cybersecurity challenges of modern manufacturing systems.
• Wilson, A. M., Jung, Y., Shafae, M., Amoh-Asante, N. A., Ashraf, A., Quon, H., Hamilton, K. A., Morrison, D., Larkin, L., Hickenbottom, K., Sáez, A. E., Ikner, L. A., Betancourt, W., Norwood, R. A., & Achilli, A. (2025). Public risk perceptions of advanced water purification in an arid urban region of the U.S. southwest: A mixed methods study. Science of the Total Environment, 1002. doi:10.1016/j.scitotenv.2025.180558
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  As water utilities implement potable reuse technology, there is a need to understand how to increase public acceptance and trust in public water supplies. The study objective was to use surveys and interviews in a large metropolitan area in Arizona to characterize tap water and advanced purified water acceptability, and factors contributing to (un)acceptability. Participants were recruited through a water utility email listserv for participation in an online REDCap survey and/or 1-hr Zoom interview. Surveys and interviews inquired about perceptions of tap water safety, familiarity with water reuse terms, acceptability of direct potable reuse (called “advanced water purification” in our study for consistency with state messaging), and rationales related to acceptance. Four hundred seventy-nine individuals participated in the survey, and twenty-two individuals participated in the interviews, with roughly comparable demographics for our city of interest but with slightly higher levels of household income and education. Only 36 % of survey respondents use their tap water for drinking water supplies, but (42 %) would be open to drinking advanced purified water. Semi-structured interviews were conducted in 2024 on risk-based thinking to evaluate how advanced purified water may compare to current drinking water safety and analyzed with inductive thematic analysis. Survey and interview participants wanted more reassurances (e.g., third party testing and opportunities for hands-on testing). Water utilities should prioritize transparent communication strategies, including sharing detailed third-party testing data and direct community engagement initiatives, to enhance public acceptance. Utilities can build trust through clear comparisons between advanced purified water and current tap water quality.
• 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.
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  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.
• Dastoorian, R., Shafae, M., & Wells, L. (2021). Assessing the performance of control charts for detecting previously unexplored shift types in high density spatial data. Quality Engineering, 34(1), 125-141. doi:10.1080/08982112.2021.2015384
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  As the collection and use of high-density (HD) spatial datasets has increased, the Statistical Process Control research community has strived to develop effective and efficient control charting techniques for these datasets. In general, these research efforts propose new control charting techniques and evaluate their abilities to detect different shift types. However, these works typically considered only conventional shift types, such as mean and variance shifts, which only account for a portion of the shift types that can manifest themselves in HD spatial datasets. In essence, advanced mathematical approaches are being developed for use with state-of-the-art measurement systems but assess their performance with traditional shift types developed for univariate statistics. This may hinder the effectiveness of these approaches in practice, as real-world systems may experience shift types other than (or in addition to) those addressed in the literature. The goal of this paper is to understand the ability of previously proposed control charting techniques to detect these unexplored shift types. This goal is accomplished through a simulation study that considers five different control charting techniques, identified from both the spatial statistics and spatial scan statistics literatures. The performances of these control charts are assessed against previously unexplored HD spatial dataset shift types. The results indicate that many control charting approaches were highly sensitive to variety of shift types. This suggests significant promise in the use of these approaches in systems that are susceptible to a wide variety of shift types, including shift types they were not specifically designed to detect.
• 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.
• Shafae, M., Wells, L., & Purdy, G. (2019). Correction to: Defending against product-oriented cyber-physical attacks on machining systems (The International Journal of Advanced Manufacturing Technology, (2019), 10.1007/s00170-019-03805-z). International Journal of Advanced Manufacturing Technology, 105(9). doi:10.1007/s00170-019-04576-3
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  The original version of this article contained a mistake. While Table 4 is mentioned within the article text several times, the actual Table 4 was missing in the original version of the article. Please see Table 4 below, The original article has been corrected.
• 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.
• Shafae, M. S., Wells, L. J., & Camelio, J. A. (2021). Modeling in-process machining data using spatial point cloud vs. Time series data structures. In 49th SME North American Manufacturing Research Conference, NAMRC 2021, 53.
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  In-process machining data (e.g., cutting forces and vibrations) have been typically collected and structured as time-referenced measurements (i.e., time-series data) and utilized in this structure to develop statistical data models used in process monitoring and control methods. This paper argues that a time-only-referenced representation overlooks the 3D nature of the physical process generating the data, and that machining data can be represented alternatively as functions of the tool-workpiece relative position resulting in a spatial point cloud data structure. High-density measurements of such spatially refenced data could be highly correlated to surrounding measurements, resulting in spatial correlation structures that could be of physical meaning and value to preserve and leverage. Using a simulated data study, this paper shows that preserving the spatial correlation structure of the data clearly improves the relative modeling performance when utilizing machining data point clouds versus the traditional time-referenced data structure. Specifically, this simulation study investigated the hypothesis that “considering the Gaussian process model class, the best model among all possible models developed using the spatial point cloud data structure has smaller/equal modeling and prediction errors compared to the best model among all possible models developed using the time-referenced data structure.” While this investigation was limited to considering the case of stationary isotropic processes, it demonstrated that the performance gap was relatively large. This encourages further investigations using real-world data to better understand the types of spatial correlations that exist in machining data and the specific machining regimes and process variables that would benefit the most from the spatial point cloud representation of the data.
• 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.