Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

SiC/SiC mini-composites with multilayer ZrSiO4 interphase: Room-temperature properties and toughening mechanism

SiC/SiC mini-composites reinforced with SiC fibers coated with different numbers of ZrSiO₄ sublayers prepared via a non-hydrolytic sol-gel process were fabricated. The tensile strength and work of fracture of the prepared SiC/SiC mini-composites were determined, and the relationship between their mechanical properties and fracture morphologies was discussed. The toughening mechanism and the variation tendency of their mechanical properties were further elaborated by analyzing the interfacial debonding morphologies of the SiC/SiC mini-composites with 1 and 4 layers of ZrSiO₄ interphase as well as the results of prior studies. A relatively rare phenomenon—the delamination of the multilayer ZrSiO₄ interphase in the SiC/SiC mini-composites but not on the SiC fibers—was observed, which clearly demonstrated the weak bonding between the ZrSiO₄ sublayers in the SiC/SiC mini-composites. The ZrSiO₄ sublayer delamination mechanism was then explained based on the high-magnification morphologies found in and beside the ZrSiO₄ interphase.     

Biological Water Treatment by a Hybrid Fluidized-Bed Bioreactor: Theoretical Study

A hybrid fluidized-bed bioreactor for water purification was proposed and analyzed. It is a novel type of bioreactor characterized by hitherto unknown stationary and dynamic features. Steady-state characteristics of this hybrid bioreactor with external liquid circulation are presented. A quantitative analysis of steady-state properties of the bioreactor was performed with the aid of an original mathematical model developed for a double-substrate aerobic microbiological process. A steady-state analysis of aerobic processes characterized by different oxygen demand was performed. The effect of essential parameters was evaluated, including carbonaceous substrate concentration in the feed stream to the apparatus, aeration intensity, total residence time of a liquid in the bioreactor, and height of the bed of fine carrier particles.     

A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust nonlinear feedback–feedforward control of a coupled kinetic Monte Carlo–finite difference simulation

Robust nonlinear feedforward–feedback controllers are designed for a multiscale system that dynamically couples kinetic Monte Carlo (KMC) and finite difference (FD) simulation codes. The coupled codes simulate the copper electrodeposition process for manufacturing on-chip copper interconnects in electronic devices. The control objective is to regulate the current density subject to the condition that the steady-state fluctuation of the overpotential remains bounded within ±0.01 V. The controller designs incorporate a low-order stochastic model that captures the input–output behavior of the coupled KMC–FD code. The controllers achieve the objectives and the closed-loop responses implemented on the low-order model and the coupled KMC–FD code match well within stochastic variations. The nonlinear feedforward control reduces the rise time of the controller response while the feedback control ensures robustness in the presence of model uncertainty.     

STATIONARY TANGENT: THE DISCRETE AND NON-SMOOTH CASE

Abstract. In Keich (2000 ),we define a stationary tangent process, or a locally optimal stationary approximation, to a real non-stationary smooth Gaussian process. This paper extends the idea by constructing a discrete tangent – a `locally' optimal stationary approximation – for a discrete time, real Gaussian process. Analogously to the smooth case, our construction relies on a generalization of the recursion formula for the orthogonal polynomials of the spectral distribution function. More precisely, we use a generalization of the Schur parameters to identify the stationary tangent. By way of discretizing, we later demonstrate how this tangent can be used to obtain `good' local stationary approximations to non-smooth continuous time, real Gaussian processes. Further, we demonstrate how, analogously to the curvatures in the smooth case, the Schur parameters can be used to determine the order of stationarity of a non-smooth process.