Mohammed Shafae

Md Habibor Rahman, Rocco Cassandro, Thorsten Wuest et al.

An attack taxonomy offers a consistent and structured classification scheme to systematically understand, identify, and classify cybersecurity threat attributes. However, existing taxonomies only focus on a narrow range of attacks and limited threat attributes, lacking a comprehensive characterization of manufacturing cybersecurity threats. There is little to no focus on characterizing threat actors and their intent, specific system and machine behavioral deviations introduced by cyberattacks, system-level and operational implications of attacks, and potential countermeasures against those attacks. To close this pressing research gap, this work proposes a comprehensive attack taxonomy for a holistic understanding and characterization of cybersecurity threats in manufacturing systems. Specifically, it introduces taxonomical classifications for threat actors and their intent and potential alterations in system behavior due to threat events. The proposed taxonomy categorizes attack methods/vectors and targets/locations and incorporates operational and system-level attack impacts. This paper also presents a classification structure for countermeasures, provides examples of potential countermeasures, and explains how they fit into the proposed taxonomical classification. Finally, the implementation of the proposed taxonomy is illustrated using two realistic scenarios of attacks on typical smart manufacturing systems, as well as several real-world cyber-physical attack incidents and academic case studies. The developed manufacturing attack taxonomy offers a holistic view of the attack chain in manufacturing systems, starting from the attack launch to the possible damages and system behavior changes within the system. Furthermore, it guides the design and development of appropriate protective and detective countermeasures by leveraging the attack realization through observed system deviations.

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Nazmul Hasan, Apurba Kumar Saha, Andrew Wessman et al.

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.