PurposeReverse engineering (RE) can be used to derive a three-dimensional (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 RE techniques can be readily applied, but parts produced with additive manufacturing (AM) 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 compensate for distortions of reverse-engineered additively manufactured components.Design/methodology/approachThis framework uses iterative finite element simulations to predict geometric distortions and iteratively estimate the key dimensional characteristics of the part while accounting for process-induced distortion. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion AM.FindingsUsing the proposed computer-aided design (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.Originality/valueThis paper presents a remanufacturing framework combining RE and AM, leveraging geometric feature-based part compensation through process simulation, better capturing the design intent needed for RE. The approach in the present study can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during RE. The authors evaluate the merits of STL-based and CAD-based approaches by quantifying the accumulated errors induced at the different steps of the proposed approach and analyzing the impact of varying part geometries.

An attack taxonomy is essential for defending manufacturing systems against cyber-physical threats by enabling systematic understanding and classification of threat attributes. However, existing taxonomies typically focus on a limited set of attributes and fail to comprehensively integrate threat actors, system-level and operational impacts, and potential countermeasures within a unified framework. Additionally, converting taxonomy-based knowledge into actionable guidance for cybersecurity tool development and decision-making remains challenging and understudied. To address these gaps, this study introduces a comprehensive attack-countermeasure taxonomy along with a taxonomy-guided decision-support framework, providing an end-to-end approach from threat identification to mitigation in manufacturing systems. Specifically, the proposed taxonomy classifies threat actors and their intent, system behavioral deviations during threat events, attack methods, and attack targets and incorporates both operational and system-level impacts. Furthermore, a structured classification of countermeasures is integrated within the taxonomy, supported by illustrative examples of potential countermeasures. Unlike previous taxonomies, this model captures the entire attack chain—from adversarial intent to observable system deviations and corresponding countermeasures. The taxonomy’s practical implementation is demonstrated using realistic attack scenarios, real-world incidents, and relevant academic case studies. Building upon this foundation, the proposed taxonomy-guided decision-support framework shows explicitly how each taxonomy layer helps guide threat identification, risk modeling and assessment, and appropriate countermeasure selection and deployment. Moreover, the framework highlights how the taxonomy complements existing cybersecurity tools, frameworks, and methodologies to facilitate context-aware and risk-informed security decisions in smart manufacturing environments.

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.

Coaxial monitoring of the melt pool, the molten region formed during laser exposure in laser powder bed fusion (LPBF) additive manufacturing, is critical for ensuring part quality, as its morphology reflects process stability. However, melt pool images are inherently stochastic due to variations in processing conditions, imaging setups, and transient phenomena such as spatter. The impact of this variability on segmentation accuracy has not been thoroughly explored in the literature, resulting in a critical gap in the development of robust methods. As a result, existing segmentation techniques often lack the generalizability and reliability needed to perform consistently across diverse conditions. In response, we propose RAMPSeg (Robust Adaptive Melt Pool Segmentation) algorithm, an intensity-agnostic and calibration-free edge-detection-based method that introduces three key innovations: (i) data-driven optimization of edge detection parameters, (ii) a quantitative segmentation review using an edge-to-area ratio to guide refinement, and (iii) an adaptive smoothing feedback loop. Unlike prior methods, RAMPSeg avoids arbitrarily selected parameters, over- or under-segmentation, and fixed heuristics. Instead, it achieves self-regulating segmentation that dynamically balances noise reduction and boundary preservation across diverse imaging conditions, enabling more generalizable melt pool analysis. We comprehensively evaluated its effectiveness using three distinct datasets encompassing diverse process conditions, materials, machines, and imaging systems. This comprehensive evaluation demonstrated consistently high segmentation accuracy ( 90

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.

Additive Manufacturing (AM), combined with efficient in-situ resource utilization, has the potential to enable sustained presence on the Moon through in-situ production and replacement of construction blocks, engineered parts, and devices in the lunar terrain. As an initial step toward realizing this vision, this work proposes a new material formulation utilizing lunar regolith simulants (LHS-1), water, and a hydrophilic hydrogel-forming polymer (Pluronic F127) to produce a hydrogel-based lunar regolith paste suitable for material extrusion AM. This formulation enables the use of water as another potential lunar in-situ resource for the first time in the literature, considering the recent confirmation of large quantities of water-ice in the shadowed craters around the lunar poles and the presence of water on the sunlit surface of the Moon. To systematically investigate the viability of the proposed hydrogel-based lunar regolith material feedstock and the application of material extrusion AM, this work introduces a three-stage design of experiment (DOE) framework to achieve two fundamental objectives. First, the viable range of lunar regolith simulant in the hydrogel-based material formulation is identified using a categorical quality evaluation of single and multi-layer material depositions. Second, the material printability window is established in terms of printing speed and extrusion rate for different viable material compositions through hierarchically designed experiments. The observed porosity of the sintered parts decreased with increasing regolith content in the material composition, while the density and compressive strength increased with higher regolith content. For material compositions consisting of 25-40 vol% regolith simulants, the observed porosity of the sintered parts decreased from 6.4 to 2.18 %, their density increased from 0.78 to 0.89 g/cm3, and their average compressive strength ranged between 0.68 and 1.32 MPa. Finally, the printability of the material was demonstrated by producing various prototype hand tools and lab-scale construction blocks with varying geometrical complexities. This work represents a crucial step toward in situ resource utilization and sustainable in-space manufacturing, sets the stage for future investigations, and the introduced DOE driven framework offers a pathway toward systematically identifying the optimal material compositions and the corresponding process parameters to enable high-quality prints.

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.

Identifying, analyzing, and evaluating cybersecurity risks are essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach is lacking to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors' techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates the systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

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.

The threat of cyberattacks on smart manufacturing systems has been rapidly growing with the potential for a multitude of different attack types, varying from traditional espionage to sabotaging physical assets and prod-ucts. A thorough and systematic understanding of the different elements of cyberattacks, from motivation to potential consequences and respective countermeasures, is a crucial stepping-stone towards proactive manage-ment of manufacturing cybersecurity risks. This understanding is essential for developing the necessary tools to identify, prevent, detect, diagnose, and mitigate cyberattacks. In response, several attack taxonomies have been proposed in the literature as methods for recognizing and categorizing various attributes of cyberattacks, including potential attack vectors/methods, targets/locations, and consequences. However, those taxonomies only cover selected attack attributes depending on the research focus, sometimes accompanied by inconsistent naming and definitions. These seemingly different taxonomies often overlap and can complement each other to create a comprehensive knowledge base of cyberattack attributes that is currently missing in the literature. Additionally, there is a missing link from creating structured knowledge by using a taxonomy to applying this structure for cybersecurity tools development and aiding practitioners in using it. To tackle these challenges, first, this article reviews and analyzes current taxonomical classifications of manufacturing cybersecurity threat attributes and countermeasures, as well as the proliferation of the scope and coverage in current taxonomies. As a result, these taxonomies are compiled into a more comprehensive and consistent meta-taxonomy for the smart manufacturing space. The resulting meta-taxonomy provides a holistic analysis of current taxonomies and in-tegrates them into a unified structure. Based on this structure, this paper identifies gaps in current attack tax-onomies and provides directions for future improvements. Finally, the paper introduces potential use cases for attack taxonomies in smart manufacturing systems for assessing security threats and their associated risks, devising risk mitigation strategies, and informing the application of cybersecurity frameworks.

The rapid adoption of automation in the Cyber-Physical Systems (CPS) is triggering Industry 4.0 (I4.0), integrating cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity into traditionally isolated systems. These I4.0 changes are optimizing the performance of Smart Manufacturing (SM) systems at the cost of increased complexity, exposing I4.0 systems to more cyberattacks than ever before. To address these challenges, this work presents DT4I4-Secure: A Digital Twin Framework for Industry 4.0 Security. The DT4I4-Secure presents a framework to create Digital Twins (DT) for I4.0 systems using a combination of models (including physics and data-based models). This paper showcases the use of the DT framework to detect attacks on I4.0 systems by comparing observations with future predictions from the DT. This paper evaluates the performance of the DT4I4-Secure for a Computer Numerical Control (CNC) turning process manufacturing a metallic spool, wherein the experimental results show the model can predict normal operations with a mean absolute error (MAE) of 0.005081. This work also explores using an Exponentially Weighted Moving Average (EWMA) based dynamic threshold instead of a traditional static threshold for attack detection when the CNC turning process is under three separate attack scenarios. The DT4I4-Secure combined with the dynamic threshold shows a 3.46 times improvement in F 1 -Scores over all three attack scenarios for instantaneous attack detection while having 100% accuracy during the manufacturing cycle.

Identifying, analyzing, and evaluating cybersecurity risks are essential to assess the vulnerabilities of modern manufacturing infrastructures and to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. In response, this work proposes a graph-theoretic approach for risk modeling and assessment to address the lack of quantitative cybersecurity risk assessment frameworks for smart manufacturing systems. In doing so, first, threat attributes are represented using an attack graphical model derived from manufacturing cyberattack taxonomies. Attack taxonomies offer consistent structures to categorize threat attributes, and the graphical approach helps model their interdependence. Second, the graphs are analyzed to explore how threat events can propagate through the manufacturing value chain and identify the manufacturing assets that threat actors can access and compromise during a threat event. Third, the proposed method identifies the attack path that maximizes the likelihood of success and minimizes the attack detection probability, and then computes the associated cybersecurity risk. Finally, the proposed risk modeling and assessment framework is demonstrated via an interconnected smart manufacturing system illustrative example. Using the proposed approach, practitioners can identify critical connections and manufacturing assets requiring prioritized security controls and develop and deploy appropriate defense measures accordingly.

Additive manufacturingAdditive manufacturing processes such as laser powder bed fusionLaser powder bed fusion produce material by localized melting of a powder feedstock layer by layer. The small melt pools and high energy density generate very different microstructuresMicrostructures in nickel superalloysSuperalloys when compared to more traditional cast or wrought processing, including features such as cellular structures and epitaxial grain growth. The features of these microstructuresMicrostructures vary depending on local thermal history, alloy chemistry, and processing parameters. There is a need to develop a systematic understanding of the influence the local thermal conditions during solidification have on the resulting microstructureMicrostructures. Such understanding will be useful in predicting and ultimately avoiding microstructural defects such as undesirable phases or non-optimal grain structures. In this work, in-situ Longwave Infrared imaging of a laser powder bed fusionLaser powder bed fusion process is used to characterize the local thermal conditions throughout additively manufactured builds for alloy IN718Alloy IN718 processed using systematically varied process parameters. This information is then correlated to observations of the microstructural features of these alloys in the as-built condition. This correlation analysis shows clear influence of the local thermal conditions during solidification on the dimensions of the dendritic microstructuresMicrostructures formed during the build process for IN718IN718. These dendritic structures arise due to segregation of elements such as niobium during solidification, an observation which can be predicted using a Scheil modeling approach.

Comprehensive knowledge of the laser powder bed fusion (LPBF) process defects, their causal factors, and relationships can enable proactive prevention and/or mitigation of those defects to ensure the production of high-quality products. However, this knowledge is scattered in a plethora of research articles, and there is a need for a formal and structured knowledge base to document the LPBF process defects knowledge and model the complex network of relationships among those defects and their causal factors. In response, this paper proposes an ontological framework to systematically structure and represent the knowledge of LPBF defects in a sustainable, reusable, and extensible way. In doing so, we first conducted a detailed literature review and analysis of current LPBF defect surveys to systematically develop a consistent and comprehensive classification of defects and potential causal factors. Then, to effectively represent the gathered knowledge, the ontological framework was designed to: (1) organize and formalize knowledge on LPBF defects and the causal factors, (2) model the complex network of causal links and cascading effects among the defects and causal factors, (3) enable easy querying of the stored knowledge, and (4) include ontological entities that are suitable for extension and reuse. A prototype LPBF defects ontology was developed in Web Ontology Language (OWL)/Resource Description Framework (RDF) formalism using the Protégé tool to effectively realize those design requirements. The developed ontology covers thirty-one unique defects and knowledge of their causal factors, including defect-to-defect causal relationships and hierarchical categorization under four major categories of high-level defect classes. Similarly, forty-five unique causal factors were categorized under twelve major categories. The proposed ontological framework and knowledge model offer a pathway to (1) provide a comprehensive knowledge base on LPBF defects for in-depth tutoring and training of novice researchers and practitioners; (2) help investigators to identify root causes of detected defects for informed corrective action; (3) guide process planning tasks from a defect control perspective; and (4) support future application and reuse of the knowledge. This study also provides several examples to illustrate the modeling of cascading effects in causal relationships, discovering knowledge using an ontology reasoner, visualizing the complex network of causal links using OntoGraph, and retrieving stored knowledge by answering competency questions using SPARQL queries. In the future, the reasoning capabilities of our proposed ontology can also be leveraged to develop expert systems for optimizing the AM workflow and quantitatively predict and diagnose LPBF defects.

Additive manufacturing processes have enabled the production of parts with complex geometry. In addition, novel design approaches such as generative design and crowdsourced design challenges enable the rapid generation of many feasible design alternatives with similar functionality but distinct geometry. In this study, we use an illustrative example, focused on laser-based powder bed fusion of metals, to explore how geometry and topology differences among parts with the same functionality can drive differences in cost. To accomplish this, we utilize a process-based cost model that can account for how variations in part geometry of different design alternatives impact the cost of the additive manufacturing process and associated post-processing operations. The cost model identified differences of up to 14% between the least and most expensive design alternatives. Part mass and build time were the most influential factors to group different design into relatively similar cost groups. High part complexity was associated with lower part cost, and was not strongly correlated to reject rates. Comparing designs within these groups showed several conflicting factors such as additive manufacturing and post-processing scrap and reject rates, which were geometry dependent. This result highlights the need for methods to better understand and quantify the effect of part geometry on manufacturing outcomes related to cost, including powder usage, post-processing requirements, and failure rates. Such methods can help designers to weigh tradeoffs between different cost, sustainability, quality, and performance objectives to select a preferred design alternative.

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.

The overlap between operational technologies and information technology has resulted in profound improvements in the manufacturing ecosystem, but it increases the risk of a non-conventional class of cyber-attacks capable of inflicting physical damages on manufacturing processes and/or products. If successful in penetrating traditional cyber-only defenses, such attacks may not be detected timely, leading to financial losses, and potentially endangering human safety. However, malicious alterations of products and/or processes intended by these attacks can be manifested as anomalous changes in process dynamics. Hence, monitoring physical process variables such as vibration and power consumption (known as side-channels in cybersecurity literature) can provide a physical-domain defense layer to detect such attacks. Focusing on product-oriented attacks, we propose a method to connect the product design, process design, and in situ monitoring to identify the physical manifestations of these attacks. The proposed approach can verify the geometric integrity of a machined part by observing cutting power signals during machining. We utilize the process and product knowledge to segment the power signal into the cutting cycles corresponding to specific geometrical features and extract process-related information accordingly. This work primarily focuses on extracting machining times for individual geometric features in parts. Next, we use the extracted information to construct quality control charts to use in detecting geometric integrity deviations of machined parts. Finally, we demonstrate our proposed method using a case study of cyber-physical attacks on machining processes aiming to tamper with different product's dimensional and geometrical features.

This study presents a detailed analysis of the production efforts for personal protective equipment in makerspaces and informal production spaces (i.e., community-driven efforts) in response to the COVID-19 pandemic in the United States. The focus of this study is on additive manufacturing (also known as 3D printing), which was the dominant manufacturing method employed in these production efforts. Production details from a variety of informal production efforts were systematically analyzed to quantify the scale and efficiency of different efforts. Data for this analysis was primarily drawn from detailed survey data from 74 individuals who participated in these different production efforts, as well as from a systematic review of 145 publicly available news stories. This rich dataset enables a comprehensive summary of the community-driven production efforts, with detailed and quantitative comparisons of different efforts. In this study, factors that influenced production efficiency and success were investigated, including choice of PPE designs, production logistics, and additive manufacturing processes employed by makerspaces and universities. From this investigation, several themes emerged including challenges associated with matching production rates to demand, production methods with vastly different production rates, inefficient production due to slow build times and high scrap rates, and difficulty obtaining necessary feedstocks. Despite these challenges, nearly every maker involved in these production efforts categorized their response as successful. Lessons learned and themes derived from this systematic study of these results are compiled and presented to help inform better practices for future community-driven use of additive manufacturing, especially in response to emergencies.

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.

Industry 4.0 and its related technologies (e.g., embedded sensing, internet-of-things, and cyber-physical systems) are promising a paradigm shift in manufacturing automation. However, with a continual increase in device interconnectivity, securing these systems becomes crucial. As these systems evolve, opportunities for cyberattacks extend to include attacks that can physically alter parts (Product-Oriented C2P attacks). Fortunately, since these cyber-physical attacks affect the physical world, there exists potential to detect an attack through its physical manifestation. Typically, in manufacturing, quality control (QC) systems are used to detect quality losses or deviations from nominal. This paper proposes that QC tools can be adapted to act as physical detection layers as part of a defense-in-depth strategy (common IT security strategy) that increases the difficulty/cost required for a successful attack. However, effectively designing physical detection layers requires understanding the extent to which attacks can (and cannot) be designed to avoid detection. In response, this paper proposes a machining specific attack design scheme and an attack design designation system (ADDS) that provides the structure to populate a wide variety of potential attacks. To illustrate the importance of applying a defense-in-depth strategy for machining, a case study is conducted with several realistic attacks against an example machining process that collects in-situ process data. Within this case study, the proposed ADDS is employed to systematically describe how these attacks could be designed to avoid detection. Finally, through this exploration, this paper shows how employing process-domain knowledge to understand the effects of Product-Oriented attacks on process physics can further aid in detection layer designs.