Volume 63, Issue 10 - Special Issue: Industrie 4.0 / Jürgen Beyerer, Jürgen Jasperneite, Olaf Sauer

Industrie 4.0 aims at improving value chains and value-added networks in industry. Technologically the approach is based on the introduction of cyber-physical systems. Their capabilities form the basis for smart systems. This paper presents technological approaches for Industrie 4.0 and introduces use cases as a specification technique for application scenarios. A demonstrator for additive manufacturing is presented. Lessons learnd from this demonstrator have led to the definition of capabilities for vertical and horizontal integration within Industrie 4.0.

One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPS) which 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 newest and foreseeable further developments of computer science, information and communication technologies on the 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 generally – and particularly in the CIRP community – which point towards CPPS. Expectations and the related new R&D challenges are also outlined.

In order to be prepared for future challenges facing the industrial production domain, Cyber-Physical Production Systems (CPPS) consisting of intelligent entities which collaborate and exchange information globally are being proclaimed recently as part of Industrie 4.0. In this article the requirements of CPPS and abilities of agents as enabling technology are discussed. The applicability of agents for realizing CPPS is exemplarily shown based on three selected use cases with different requirements regarding real-time and dependability. The paper finally concludes with opportunities and open research issues that need to be faced in order to achieve agent-based CPPSs.

Shortening product lifecycles and small lot sizes require manufacturing systems to adapt increasingly fast. Many existing machine tools, handling and logistics systems provide a generic functionality that is not bound to a specific product. But this flexibility and reconfigurability on the level of individual resources is lost in automated systems that are limited to the production of a fixed set of product variants. We propose a unified abstraction for the skills provided by the available resources and the product-specific manufacturing requirements. From these high-level descriptions, executable manufacturing procedures are derived, exposed as services and dynamically orchestrated at runtime in order to achieve the manufacturing goals.

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.

Due to global competition and increasing product complexity, the complexity of production systems has grown significantly in recent years. This places an increasing burden on automation developers, systems engineers and plant constructors. Intelligent assistance systems and smart automation systems are a possible solution to face this complexity: The machines, i.e. the software and assistance systems, take over tasks that were previously carried out manually by experts. At the heart of this concept are intelligent anomaly detection approaches based on models of the system behaviors. Intelligent assistance systems learn these models automatically: Based on data, these systems extract most necessary knowledge about the diagnosis task. This paper outlines this data-driven approach to plant analysis using several use cases from industry.

In this paper, we analyze the specific requirements of interacting with cyber-physical systems and propose a design approach that is driven by user needs and makes use of an expanded toolbox that contains state-of-the-art interaction technologies including Smart Glasses and Wearables. We present several examples of assistance systems in industrial production that use these interaction technologies and discuss the corresponding usability and implementation aspects.

This contribution applies methods and languages of Model-Based Systems Engineering to the field of production system engineering. The goal is an integrated modeling of objects, processes and systems. This approach improves knowledge transfer between the stakeholders involved and enables model-based design and verification.

This paper provides an overview about the conceptual work performed by the VDI/VDE GMA working group 7.21 towards the specification of a reference model for Industrie 4.0 service architectures. It focuses on the conceptual design of service-oriented architecture (SOA) relying upon the “I40 component” as a core basic concept across the whole value network. Furthermore, the paper discusses the relevance of reference models of the Internet of Things and Services as a foundation.