Design

In theory, decentralized control approaches in the manufacturing context offer several advantages over monolithic centralized systems where all functions are combined into one or into several authorities. However, practical implementation requires adaptation of the general concept of decentralization to fit individual and specific use cases, especially with regard to their sensible scope. One such use case is the assembly of high-variation products. This article shows the appropriate combination of centralized and decentralized approaches can be leveraged to achieve better planning and increased throughput in manufacturing. With flexible cycle control for work stations and suitable assistance at the assembly workstation, the previous shop-floor oriented organization style can be transformed into a series-like manufacturing process. This is done using a multi-layered infrastructure that follows the Industry 4.0 paradigm of decentralized information processing through autonomous ...

The need to sometimes respond very quickly to changes requires companies to have a high degree of flexibility and speed of reaction. Application system architectures, which usually consist of old and self-developed systems, often do not allow companies to meet these requirements. However, investment funds for new software are limited, so priorities must be set when it comes to replacing legacy systems. An adaptability analysis is an efficient analysis method for planning the renewal of the application system landscape. This article describes the procedure and results of an adaptability analysis, using the example of an internationally active automotive supplier.

A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.

The planning of logistics processes and their efficient implementation are decisive competitive factors for customized plant construction. On the construction site, however, the collection of logistics data is often neglected, preventing the project planner from building a reliable database. Related information gaps can be closed with the help of a digital shadow that collects logistics-relevant data (partially) automatically, stores them in a consistent manner and makes them available to project management. This article describes the first important results of a research project on information and communication processes in construction site logistics and explains their vital role in the development and use of a digital shadow.

Emergency organizations such as fire departments or technical relief organizations are expected to react very quickly – sometimes to unknown situations – and provide the appropriate assistance. Can principles used in these organizations be transferred to IT support, e.g. for ERP systems? An experiment in an IT service unit investigates this question – with surprising results.

In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.

With the increasing availability of IoT devices and significantly greater incorporation of Internet-enabled technologies into manufacturing processes, the idea of improving factory control through the use of artificial intelligence (AI) is also coming to the fore. Using the example of high-variation series manufacturing, this article describes which steps need to be taken to improve factory control with AI.