One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet. Cyber-physical production systems (CPPS), relying on the latest, and the foreseeable further developments of computer science, information and communication technologies on one hand, and of manufacturing science and technology, on the other, may lead to the 4th industrial revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots in general – and in particular to the CIRP community – which point towards CPPS. Expectations towards research in and implementation of CPS and CPPS are outlined and some case studies are introduced. Related new R&D challenges are highlighted.

Cyber-physical systems in manufacturing

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

A Manufacturing Big Data Solution for Active Preventive Maintenance

Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange

Industry 4.0: A bibliometric analysis and detailed overview

Smart manufacturing is increasingly becoming the common goal of various national strategies. Smart interconnection is one of the most important issues for implementing smart manufacturing. However, current solutions are not tended to realize smart interconnection in dealing with heterogeneous equipment, quick configuration and implementation, and online service generation. To solve the issues, industrial Internet-of-Things hub (IIHub) is proposed, which consists of customized access module (CA-Module), access hub (A-Hub), and local service pool (LSP). A set of flexible CA-Modules can be configured or programed to connect heterogeneous physical manufacturing resources. Besides, the IIHub supports manufacturing services online generation based on the service encapsulation templates and also supports quick configuration and implementation for smart interconnection. Furthermore, related smart analysis and precise management have the potential to be achieved. Finally, a prototype is given to illustrate the functions of the proposed IIHub, and to show how IIHub realizes smart interconnection.

IIHub: An Industrial Internet-of-Things Hub Toward Smart Manufacturing Based on Cyber-Physical System

Interoperability between OPC UA and AutomationML

Abstract Objects to change within a manufacturing enterprise can be products, technological or logistical processes, parts of the manufacturing facilities or a company's organization. For this paper we assume, that IT systems are also objects to change – they have to be adapted to changes to products and facilities on the shop floor. Today the adaption of IT systems is managed and done manually – therefore the authors propose an automated way of changing the production's IT-systems. For this purpose two main ideas are described: reading and interpreting a self-description of production equipment and enrichment of these descriptions with data from the “digital factory” bridging the gap between planning and operating IT-systems and thus enabling higher adaptivity of manufacturing systems.

Requirements and concept for Plug-and-Work

OPC UA & Industrie 4.0 - Enabling Technology with High Diversity and Variability☆

AutomationML (Automation Markup Language) is an XML based data format for the exchange of plant engineering information. Its mission is to interconnect engineering tools of different disciplines, e.g. process, mechanical or control engineering. AutomationML enables the vendor independent storage of interlinked engineering data that is usually distributed into different engineering tools. This enables a wide range of future tool functionality not available and not possible today. For this, AutomationML defines a number of extended concepts for different engineering aspects. One of them is the process-resource-product concept which was introduced with version 1.1 and published at the Hannover trade fair 2009. This paper explains this concept by means of an industrial example.

Three-view-concept for modeling process or manufacturing plants with AutomationML

To model increasingly adaptive production systems, skills are used to describe generic capabilities of the system components. In this paper, the authors extend the well-known division of production entities into product, process, and resource (PPR) with a skill definition. There are two main advantages for this approach: First, using PPR for the skill definition allows easy integration into existing models and tools. Second, there is a natural tendency to define very generic skills to capture all possible use cases. But at some point, skills have to be translated into precise instructions for execution. The model makes this dichotomy explicit and provides a common taxonomy for stakeholders concerned with skills on different abstraction levels.

Miriam Schleipen

Papers

Interoperability between OPC UA and AutomationML

Requirements and concept for Plug-and-Work

OPC UA & Industrie 4.0 - Enabling Technology with High Diversity and Variability☆

Three-view-concept for modeling process or manufacturing plants with AutomationML

PPRS: Production skills and their relation to product, process, and resource