Typeset

The toolfree production of parts using 3D printing technology enables dynamic use of the production area. On the one hand, this makes it possible to react flexibly to changes and, on the other hand, to achieve a high level of efficiency in the production units. The blockchain technology enables a common database between the participants. This leads to a verifiable collaboration in the case of the relationship between customer and manufacturer. This paper describes how available additive manufacturing resources can be identified and offered in a decentralized production network.

Digitization in engineering promises automated workflows, higher speed and lower costs in the development of automation solutions. The prerequisite for this is not only modularization based on a structured description language, but also uniform, interdependent modeling that ensures automated data exchange across system boundaries. In order to achieve a broad application, the underlying ontology should be based on existing norms and standards and be available in open source applications. However, the collaborative and consistent development of such an ontology requires a structured, methodical procedure and an associated modelling map that serves as an orientation for standardized, work-sharing modelling. A possible approach for the required procedure model and the related map will be presented in this article and validated using AutomationML. The presented approach should point out a possible direction and stimulate further process-controlled modelling efforts of ontologies.

A newly developed hand guided collaborative robot system will be used for manual mechanical joining process in the final assembly of aircrafts. The tool can be moved quickly and precisely to reach all joining positions avoiding physical effort for the operator. Special focus was given on the integrated handling of the entire system. The interlinked sensory of all subsystems ensures a smart control of the system. A mobile device was implemented to increase the usability and to foster the employees’ acceptance of the solution. It enables significantly improved process documentation, reproducibility and transparency.

The interworking of Manufacturing Execution Systems (MES) and operating resources is a prerequisite for the flexible and versatile production in Smart Factories of Industry 4.0. This article describes a qualitative and quantitative analysis of an MES integration based on an industrial laser assistance system for worker guidance. It analyzes the situation and requirements from a user perspective with special consideration of implemented systems, interfaces, protocols as well as Plug & Produce. The study uses qualitative analysis results from opinion makers and quantitative analysis results from leading manufacturing companies among others from the automotive and aerospace industry. Thus, the study supports decision making for MES investments in Industry 4.0.

Handling increasing complexity is a major challenge within the manufacturing industry. Methods from Industrie 4.0, e. g. data analytics, can support in reducing complexity. Currently, benefits of implementing data analytics within large scale manufacturing are limited. For this purpose, a study regarding the potentials and obstacles for data analytics in large scale manufacturing was conducted. The results of this study show the necessity of adaptive data availability, strategic prioritization as well as scalable data analytics in order for data analytics to be successful.

The performance of assistance systems, especially in the automotive sector, has become an unique selling point. The trend toward Autonomous driving represents the expected impact of innovation resulting from the exploitation of the latest technologies. Besides autonomous driving, other areas of application for autonomous systems could trigger social change - the prime example being industrial production. The following article presents a planning approach tailored to the complex engineering task of planning and designing autonomous systems for industrial applications.

Existing factories face multiple problems due to their hierarchical structure of decision making and control. Cyber-physical systems principally allow to increase the degree of autonomy to new heights. But which degree of autonomy is really useful and beneficiary? This paper differentiates diverse definitions of autonomy and approaches to determine them. Some experimental findings in a lab environment help to answer the question raised in this paper.

Cyber-Physical Systems (CPS) are an example of the close connection between the digital and the real world. This connection makes the development of the systems more complex. Methods of Artificial Intelligence (AI) such as machine learning help companies to use these systems for new application scenarios. Image and speech recognition capabilities enable new, closer forms of cooperation between humans and CPS that previously did not work for occupational safety reasons. At the same time, machine learning enhances the cognitive abilities of CPS. They can work independently in situations which are difficult to plan.

Why Lean-Management is the solid basis for the fourth industrial revolution: The fourth industrial revolution seems to be the current issue number one. Apart from that well-known consultancies determine a not satisfactory level of implementation concerning Lean methods. The question rises to what extent the implementation of Lean Methods is useful for the introduction of industry 4.0 or if these methods can even be seen as a precondition. Therefore, all common Lean principles and methods were contrasted with the principles and technologies of the industry 4.0. In conclusion, Lean Management is a solid basis for the successful implementation of the fourth industrial revolution for the production of the future. Lean Management can finally be seen as the essential precondition.

The economic and environmental impacts of globalization are forcing companies to form their supply chain more efficient. Vendor Managed Inventory (VMI) is a widely used concept that is primarily implemented because of its cost savings. Based on a comprehensive survey in the logistics industry in Germany, this study provides sustainable and ecological recommendations for companies planning or already working daily with VMI.