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.     

Symptoms and risks for musculoskeletal disorders among male and female footwear industry workers

BackgroundConsidering the growing participation of woman in the labor market and the high prevalence of the musculoskeletal disorders (MSD) in the footwear industry workers, the aim of this study was to evaluate and compare female and male footwear industry workers in relation to their demographics, occupational characteristics, stress levels, MSD symptoms prevalence, physical activity levels, alcohol and tobacco use.MethodsThe study included 175 female and 182 male footwear industry workers. Data was collected using the Perceived Stress Scale – 10, the Standardized Nordic Questionnaire, the International Physical Activity Questionnaire, the Alcohol Use Disorders Identification Test, and the Fagerstrom test for Nicotine Dependence.ResultsThe female workers were younger, had less time in the company and in the current job. They reported more stress (p = 0.001), had less time for physical activity (p = 0.004), higher prevalence of MSD symptoms in the last 12 months (p = 0.003), and consumed less alcohol (p = 0.006) than the male workers.ConclusionsFemale and male footwear industry workers presented significant differences in their demographics, occupational characteristics, stress levels, MSD prevalence, physical activity levels, alcohol and tobacco use. These differences should be taken into consideration in the implementation of intervention programs for optimum outcomes.Relevance to industryFemale footwear industry workers were more stressed, presented higher prevalence of MSD symptoms, and reported less time for physical activity than male footwear industry workers. The differences between male and female workers should be taken into consideration in the implementation of MSD prevention programs.     

B737机务CBT系统建设方案的探讨

通过对AICC制订的CBT课件开发规范的探讨，阐述了CBT的基本概念和波音737飞机机务CBT的开发制作过程，以及利用Authorware开发软件制作多媒体课件的技术难点。     

跟踪世界先进技术 振兴我国智能网产业

本文全面介绍了我国跟踪智能网世界先进技术，研究开发自主产权的国产智能网情况，简述了国产主流产品ＣＩＮ系列智能网产品产业化和应用情况，对国产智能网的发展应用和产业化提出了建议。     

基于CompactPCI的应用与分析

鉴于目前电信的声讯系统中竞争的激烈性，它们急需要提高自身的系统集成度及高可用性，保证以最小的投资获得最丰厚的回报，由此出现了CompactPCI的系列产品，它不仅可以大大提高系统的集成度，同时对于系统的高可用性也进行了充分的考虑。本文将就声讯系统中基于CompactPCI的应用进行分析。     

我国核工业废放射源安全管理

废放射源易于在管理上失去控制，从而酿成事故，对人类和环境造成危害。本文分析了我国废放射源产生的原因，介绍了我国核工业废放射源的现状与安全管理的技术路线和策略，提出了当前所面临的主要问题。     

浙江产业调整的法律保障

依照先进国家和地区的经验及从浙江省的现实出发,为促进浙江产业升级和结构调整,文章提出应充分发挥法律的保障作用,建立和完善产业法律制度,加紧制定〈浙江省促进产业升级条例〉.     

Polymeric materials

Summary The Polymer Section of the Santa Barbara Workshop on Modeling of Materials is briefly reviewed. Motivation and need for modeling in polymer-based materials are outlined and the recommendations resulting from the workshop reported.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.