The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a cyber-physical system tightly integrating manufacturing enterprises in the physical world with virtual enterprises in cyberspace. To a great extent, realizing the full potential of cyber-physical systems depends on the development of new methodologies on the Internet of Manufacturing Things (IoMT) for data-enabled engineering innovations. This paper presents a review of the IoT technologies and systems that are the drivers and foundations of data-driven innovations in smart manufacturing. We discuss the evolution of internet from computer networks to human networks to the latest era of smart and connected networks of manufacturing things (e.g., materials, sensors, equipment, people, products, and supply chain). In addition, we present a new framework that leverages IoMT and cloud computing to develop a virtual machine network. We further extend our review to IoMT cybersecurity issues that are of paramount importance to businesses and operations, as well as IoT and smart manufacturing policies that are laid out by governments around the world for the future of smart factory. Finally, we present the challenges and opportunities arising from IoMT. We hope this work will help catalyze more in-depth investigations and multi-disciplinary research efforts to advance IoMT technologies.

Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of modern science. Time series data from complex systems capture the dynamic behaviors and causalities of the underlying processes and provide a tractable means to predict and monitor system state evolution. However, the nonlinear and non-stationary dynamics of the underlying processes pose a major challenge for accurate forecasting. For most real-world systems, the vector field of state dynamics is a nonlinear function of the state variables; i.e., the relationship connecting intrinsic state variables with their autoregressive terms and exogenous variables is nonlinear. Time series emerging from such complex systems exhibit aperiodic (chaotic) patterns even under steady state. Also, since real-world systems often evolve under transient conditions, the signals obtained therefrom tend to exhibit myriad forms of non-stationarity. Nonetheless, methods reported in the literature focus mostly on forecasting linear and stationary processes. This article presents a review of these advancements in nonlinear and non-stationary time series forecasting models and a comparison of their performances in certain real-world manufacturing and health informatics applications. Conventional approaches do not adequately capture the system evolution (from the standpoint of forecasting accuracy, computational effort, and sensitivity to quantity and quality of a priori information) in these applications.

Additive Manufacturing (AM) methods have become increasingly efficient and industrially viable in the past ten years. These methods offer the freedom of complexity to the designers and choices of localized and pull-based production system to the managers. These propositions of AM have been enabling custom manufacturing and are catalysts for rapid growth of additive manufacturing (AM). This paper analyzes the general characteristics of AM supply chain and proposes three AM supply chain models based on the specific nature of the industry. Our description of the models emphasizes on adopting an holistic view of the AM supply chain and therefore includes raw material, printer hardware and the virtual supply chain. Throughout the product life cycle of additively manufactured products, the interlacing of the virtual supply chain (digital thread) with the physical supply chain and their operations fundamentally make the AM process a cyber-physical system (CPS). Therefore, the technology brings along with it benefits of a CPS as well as a new class of attack vectors. We discuss the possible attacks (printer, raw material and design level), risks (reverse engineering, counterfeiting and theft) and provide an enhanced risk classification scheme. We contend that the traditional cybersecurity methods need to evolve to address the new class of attack vectors that threaten the AM supply chain and also discuss the nature of existing solutions that help in addressing the risks and attack threats. In providing an holistic view of the AM supply chain the interdependencies of the processes in the AM supply chain are presented and we elucidate the effects of local attack vectors on the entire supply chain. Further, we discuss the existing security measures to mitigate the risk and identify the existing gap in AM security that needs to be bridged.

To improve service reliability, many transit agencies include significant amounts of slack in the schedule. However, too much slack in the schedule reduces service frequency, given a fixed vehicle fleet size. We study the problem of determining the optimal slack that minimizes the passengers’ expected waiting times under schedule-based control. By applying a D/G/c queue model, we show that the system is stable if slack is added in the schedule. For a single-bus loop transit network, we derive convexity of mean and variance of bus delays and provide an exact solution if the travel time is exponentially distributed. For the case of multiple buses and other travel time distributions, we provide several approximation approaches and compare them to simulation results. The simulation results show that our approximations are good for interval of appropriate slack, which often contains the optimal value.

This paper presents research about the adaptation of Contour Crafting, a novel prototyping process, for two carefully chosen uncured ceramic materials. The details of the construction and operation of the fabrication machine, as well as procedures and results from our preliminary experimentation are concisely presented.