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.     

A conceptualization of intended learning outcomes supporting self‐regulated learners in indicating learning paths

Intended learning outcomes (ILOs) indicate what learners will be able to achieve after they are taught. Traditionally, ILOs are expressed as plain text or unstructured documents. What if all ILOs of a specific course of study can be conceptualized through a structured diagrammatic technique? It was hypothesized that learners can benefit from this conceptualization in learning, especially in self‐regulated learning. The aim of this study was to investigate whether the ILOs represented in unstructured or structured formats can facilitate learners to identify learning paths. The results revealed that the mean ratings of all learning paths were statistically significantly higher with structured ILOs.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

实体造型中的几何约束

实体造型是一个基于约束的过程，完成从功能约束到几何约束、再到代数约束的转化而得到实体模型。本文讨论了几何约束的层次性及其表示，并且对几何约束同设计意图的关系进行了研究，提出了基于ＣＳＧ／ＧＣＧ／Ｂ－ｒｅｐ的模型表示。     

In the theory of blast waves

The behavior of the blast wave from an exploding spherical volume is investigated mathematically over the entire range of blast-wave propagation. The computational method uses analytical results for a similar problem for a point explosion with counter pressure and the theory of an asymptotically equivalent point explosion. The asymptotic solution of the gas-dynamic equations away from the explosion site is matched to the initial conditions of blast-wave formation due to the disintegration of the detonation shock upon its arrival at the boundary of the exploding volume. The spatial distribution of the blast-front pressure is found for combustible gaseous systems and solid explosives. The theoretical results obtained agree quantitatively with available experimental measurements. __________ Translated from Fizika Goreniya i Vzryva, Vol. 42, No. 4, pp. 93–99, July–August, 2006.     

A frame-based knowledge representation model and its mapping to nested relations

The first part of this paper introduces the basic constructs of a frame representation model and gives a formal definition for them. Subsequently the nested relational model (NF²) is described as an extension of the classical relational model to support relation-valued attributes. In the second part of this paper both models are compared with each other and a mapping of frame representation structures to NF² structures is specified. The structural similarities between frames and NF² relations are made clear and it is shown that their main difference is due to the type polymorphism introduced by the concept hierarchy of the frame model. This causes type collisions to occur in the strictly typed NF² model when frames are directly mapped to NF² structures. Two solutions to this problem are suggested and compared. The paper concludes with a reformulation of query operations of the frame model in terms of NF² algebra operations.