Design of an IoT System for the Palletized Distribution Supply Chain with Model-Based Systems Engineering Tools

In recent years, Internet-of-Things technology (IoT) has been the subject of research in diverse fields of applications. IoT plays an essential role in transitioning enterprises towards a more interconnected paradigm of manufacturing, logistics, services, and business, known as Industry 4.0. This paper presents an operational concept for a system that implements IoT technology in pallets, which are used to move products along supply chains. These sensors will help us gain insight into the conditions experienced by products and unit loads. Having this capability will allow us to obtain the information necessary for better control of product distribution along the supply chain, and to design packaging that is more efficient and effective in protecting products during distribution. In this paper, we show how Model-Based Systems Engineering (MBSE) can be leveraged to create models that capture the required system behaviors, and we address the complexity of an IoT system within the domain of packaging and logistics applications.

Software Architecture Design of Animation Studies Platform Using Structure-Behavior Coalescence Method

Animation education in the new media era is moving toward the goal of cultivating high-end talents. The development of an architecture-oriented animation studies platform provides guarantee for the training of talents in terms of teaching quality. This research uses the Internet as the medium and mobile phones and computer clients as the main technology platforms, starting from the software architecture and constructing the system model of the animation studies platform according to the Structure-Behavior Coalescence (SBC) method. The core theme of Model-Based Systems Engineering (MBSE) is a modeling language with model consistency of systems structure and systems behavior. This paper developed Structure-Behavior Coalescence State Machine (SBC-SM) as the formal language for the MBSE animation studies platform design model singularity. The model consistency will be fully guaranteed in the MBSE animation studies platform design when the SBC state machine approach is adopted. It not only improves the efficiency of platform development but also reduces the difficulty and risk of platform development.

A Tensor-Based Formulation of Hetero-Functional Graph Theory

Recently, hetero-functional graph theory (HFGT) has developed as a means to mathematically model the structure of large-scale complex flexible engineering systems. It does so by fusing concepts from network science and model-based systems engineering (MBSE). For the former, it utilizes multiple graph-based data structures to support a matrix-based quantitative analysis. For the latter, HFGT inherits the heterogeneity of conceptual and ontological constructs found in model-based systems engineering including system form, system function, and system concept. These diverse conceptual constructs indicate multi-dimensional rather than two-dimensional relationships. This paper provides the first tensor-based treatment of hetero-functional graph theory. In particular, it addresses the “system concept” and the hetero-functional adjacency matrix from the perspective of tensors and introduces the hetero-functional incidence tensor as a new data structure. The tensor-based formulation described in this work makes a stronger tie between HFGT and its ontological foundations in MBSE. Finally, the tensor-based formulation facilitates several analytical results that provide an understanding of the relationships between HFGT and multi-layer networks.

Digital Twin-Driven Process and Equipment FMECA Generation for Smart Manufacturing Applications

Qualtech Systems, Inc. (QSI)’s integrated tool set, consisting of TEAMS-Designer® and TEAMS-RDS® provides a comprehensive digital twin-driven and model-based systems engineering approach that can be deployed for fault management throughout the equipment life-cycle – from its design for fault management to condition-based maintenance of the deployed equipment. In this paper, we present QSI’s approach towards adapting and enhancing their existing model-based systems engineering (MBSE) approach towards a comprehensive digital twin that incorporates constructs necessary for development of a Process Failure Modes and Criticality Analysis (P-FMECA) and integrates that with an Equipment FMECA. The paper will discuss the various levels of automation towards incorporation of these model constructs and their reuse towards automation of the development of the different digital twins and subsequently the automatic generation of the combined Process and Equipment FMECA. This automated ability to develop the integrated FMECA that incorporates both Process-level Failure Modes and Equipment-level Failure Modes allows the system designer and operators to correlate and identify process failures down to their root causes at the equipment-level and thereby producing a comprehensive actionable systems-level view of the entire Smart Manufacturing facility from a fault management design and operations perspective. The paper will present the application of this novel technology for the Advanced Metal Finishing Facility (AMFF) at the Warner-Robins Air Logistics Complex (WR-ALC) in Robins Air Force Base, Georgia, as part of WR-ALC’s initiative towards model-based enterprise (MBE) and smart manufacturing.

A Comprehensive Commercialization Framework for Nanocomposites Utilizing a Model-Based Systems Engineering Approach

Nanocomposites provide outstanding benefits and possibilities compared to traditional composites but struggle to make it into the market due to the complexity and large number of associated challenges involved in, as well as lack of standards for, nanocomposite commercialization. This article proposes a commercialization framework utilizing market analysis and systems engineering to support the commercialization process of such high technologies. The article demonstrates the importance and usefulness of utilizing Model-Based Systems Engineering throughout the commercialization process of nanocomposite technologies when combining it with the Lean LaunchPad approach and an engineering analysis. The framework was validated using a qualitative research method with a case study approach. Applying this framework to a nanocomposite, called ZT-CFRP technology, showed tremendous impacts on the commercialization process, such as reduced market and technological uncertainties, which limits the commercialization risk and increases the chance for capital funding. Furthermore, utilizing the framework helped to decrease the commercialization time and cost due to the use of a lean engineering analysis. This framework is intended to assist advanced material-based companies, material scientists, researchers and entrepreneurs in academia and the industry during the commercialization process by minimizing uncertainties and risks, while focusing resources to reduce time-to-market and development costs.

Connecting Building Design with the Digital Factory by Design Languages to Explore Different Solutions

In engineering, design decisions in one domain exhibit multiple consequences in other domains. These consequences result from the often more or less hidden coupling between the different design domains. In order to examine these consequences, models need to be created. In practice, this is challenging due to the exchange of data between different engineering domains, since different software applications are often used and the effort involved with manual model creation. In this paper, we explore the use of graph-based design languages in a Model-Based Systems Engineering (MBSE) approach to link the digital factory with building design. We also show that the use of a common formal representation based on the Unified Modeling Language (UML) supports the interoperability between the two domains. Finally, we demonstrate how the engineering knowledge for the preliminary design of a factory building can be formally described using graph-based design languages and how the production line of the digital factory can then be used as an input to automatically create valid preliminary designs for the factory building.1