A Framework for Automatic Dynamic Constraint Verification in Cyber Physical System Modeling Languages

Design of Cyber-Physical Systems (CPSs) involves overlapping the domains of control theory, network communication, and computational algorithms. Involving multiple domains within the same design greatly increases the system complexity. Furthermore, the physical nature of CPSs generally involves important safety constraints where constraint violations can be catastrophic. The design of CPSs benefits from focusing on the construction of abstracted, high-level models in a DomainSpecific Modeling Language (DSML). A Domain-Specific Modeling Environment (DSME) may aid in the design of such complex systems by enforcing structural design constraints during the construction of models. Models built using a DSME may also use compilers or interpreters to produce real working, low-level artifacts that represent the high-level design. Though each model in a DSME may abide by a formal specification, the behavior of a design may violate dynamic constraints if deployed. Engineers are tasked to ensure that models behave safely by implementing their expert knowledge after using appropriate verification tools. Constraint violations may be eliminated by a modification of the model based on verification feedback, known as Dynamic Constraint Feedback (DCF). Mending such constraint violations is a task generally performed by the model designer. Such a process could potentially be automated through the capture of well-known design practices. The challenging task when automating model correction then becomes in the design of a DSML. A designer of a DSML may have a clear understanding of how to design the syntax and semantics for their domain, but there are no formal methods for implementing verification tools for automatic model correction. Such a framework could greatly aid in the selection of available verification tools, implement well-established design methods, and model dynamic constraints. Presented is the Dynamic Constraint Feedback Metamodeling Language (DCFML), a new metamodel to implement DCF upfront in DSML design. This particular solution provides a concrete solution to the abstraction of the various components of DCF, and then appends them to the DSML design process provided by a DSME

MODEL-BASED PREDICTIVE ANALYTICS FOR ADDITIVE AND SMART MANUFACTURING

Qualification and certification for additive and smart manufacturing systems can be uncertain and very costly. Using available historical data can mitigate some costs of producing and testing sample parts. However, use of such data lacks the flexibility to represent specific new problems which decreases predictive accuracy and efficiency. To address these compelling needs, in this dissertation modeling techniques are introduced that can proactively estimate results expected from additive and smart manufacturing processes swiftly and with practical levels of accuracy and reliability. More specifically, this research addresses the current challenges and limitations posed by use of available data and the high costs of new data by tailoring statistics-based metamodeling techniques to enable affordable prediction of these systems. The result is an integrated approach to customize and build predictive metamodels for the unique features of additive and smart manufacturing systems. This integrated approach is composed of five main parts that cover the broad spectrum of requirements. A domain-driven metamodeling approach uses physics-based knowledge to optimally select the most appropriate metamodeling algorithm without reliance upon statistical data. A maximum predictive error updating method iteratively improves predictability from a given dataset. A grey-box metamodeling approach combines statistics-based black-box and physics-based white-box models to significantly increase predictive accuracy with less expensive data overall. To improve computational efficiency for large datasets, a dynamic metamodeling method modifies the traditional Kriging technique to improve its efficiency and predictability for smart manufacturing systems. Finally, a super-metamodeling method optimizes results regardless of problem conditions by avoiding the challenge with selecting the most appropriate metamodeling algorithm. To realize the benefits of all five approaches, an integrated metamodeling process was developed and implemented into a tool package to systematically select the suitable algorithm, sampling method, and combination of models. All the functions of this tool package were validated and demonstrated by the use of two empirical datasets from additive manufacturing processes

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

This paper motivates, describes, demonstrates in use, and evaluates the Open Models Laboratory (OMiLAB)—an open digital ecosystem designed to help one conceptualize and operationalize conceptual modeling methods. The OMiLAB ecosystem, which a generalized understanding of “model value” motivates, targets research and education stakeholders who fulfill various roles in a modeling method\u27s lifecycle. While we have many reports on novel modeling methods and tools for various domains, we lack knowledge on conceptualizing such methods via a full-fledged dedicated open ecosystem and a methodology that facilitates entry points for novices and an open innovation space for experienced stakeholders. This gap continues due to the lack of an open process and platform for 1) conducting research in the field of modeling method design, 2) developing agile modeling tools and model-driven digital products, and 3) experimenting with and disseminating such methods and related prototypes. OMiLAB incorporates principles, practices, procedures, tools, and services required to address the issues above since it focuses on being the operational deployment for a conceptualization and operationalization process built on several pillars: 1) a granularly defined “modeling method” concept whose building blocks one can customize for the domain of choice, 2) an “agile modeling method engineering” framework that helps one quickly prototype modeling tools, 3) a model-aware “digital product design lab”, and 4) dissemination channels for reaching a global community. In this paper, we demonstrate and evaluate the OMiLAB in research with two selected application cases for domain- and case-specific requirements. Besides these exemplary cases, OMiLAB has proven to effectively satisfy requirements that almost 50 modeling methods raise and, thus, to support researchers in designing novel modeling methods, developing tools, and disseminating outcomes. We also measured OMiLAB’s educational impact

Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Sensor grid middleware metamodeling and analysis

Sensor grid is a platform that combines wireless sensor networks and grid computing with the aim of exploiting the complementary advantages of the two systems. Proper integration of these distinct systems into effective, logically single platform is challenging. This paper presents an approach for practical sensor grid implementation and management. The proposed approach uses a metamodeling technique and performance analysis and tuning as well as a middleware infrastructure that enable practical sensor grid implementation and management. The paper presents our implementation and analysis of the sensor grid. © 2014 Srimathi Chandrasekaran et al