Why Cyber-physical Production Systems Need a Descriptive Engineering Approach – A Case Study in Plug & Produce

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Laser micro-welding manufacturers face substantial challenges in verifying weldment quality, as the industry and applications are requiring increasingly the miniaturization and compactness of products. The problem is compounded by new stringent demands for personalized products at competitive, low costs and the highest quality levels. High-pressure equipment manufacturers, in particular, rely on ISO 3834:2021 to assure and demonstrate best welding practices but also to manage risks associated with liability issues. ISO 3834:2021, like all conventional quality management systems, offers a one-dimensional, quasi-static overview of welding quality that may fail to deal with these new challenges and underlying complexities required to deal effectively with process variability. This paper presents a framework for welding companies to integrate horizontally their suppliers and customers with their processes and products, which are also integrated vertically in the context of Smart Manufacturing or Industry 4.0. It is focused on the development of a smart quality management system for intelligent digitization of all company manufacturing and business processes. Furthermore, an innovative data-based welding quality management framework is described for laser micro-welding applications and their implementation perspectives. The research is driven by an inductive methodology and based on a seamless integration of engineering-oriented heuristic and empirical approaches that is appropriate for intelligent and autonomous quality management, given the lack of research in this niche, but increasingly important topic area.

[Interaction-ergonomic design and compatibility of AR-supported information displays using the example of a head-mounted display for industrial set-up processes]

Im Zuge des technologischen Fortschritts werden zunehmend analoge durch digitale Lösungen ersetzt. In diesem Zusammenhang treten neuartige Visualisierungsmöglichkeiten in den Vordergrund, wie z. B. Augmented Reality (AR). Der Umgang mit AR-basierten Informationsdarstellungen kann jedoch bei inkompatibler Gestaltung zu Performance-Verlusten und erhöhten physischen sowie psychischen Beanspruchungen führen. Bestehende ergonomische und nutzerzentrierte Richtlinien sowie gesetzliche Vorschriften können die Konzipierung und Usability derartiger Systeme aufgrund der rasanten technischen Weiterentwicklung nur bedingt unterstützen. Die vorliegende Ausarbeitung stellt an einem Praxisbeispiel dar, inwieweit eine cyber-physische Rüstapplikation in Form eines Head-Mounted Displays den geltenden interaktions-ergonomischen und kompatibilitäts-bezogenen Standards im Kontext industrieller Tätigkeiten und gesten-gesteuerter, binokularer AR-Systeme entspricht bzw. gerecht wird.

Praktische Relevanz: Die präsentierten Ergebnisse nehmen für die Praxis eine wichtige Rolle ein, da die vorgestellte Systematik Arbeitspersonen bei komplexen Rüst- und Montagevorgängen unterstützen soll. Auf Grundlage dieser Erkenntnisse und mithilfe weiterer Optimierungsmöglichkeiten soll ein prozesssicherer, wertschöpfender und beanspruchungsminimaler Einsatz von AR-Systemen im industriellen Umfeld angestrebt werden.

Proactive Scheduling and Reactive Real-Time Control in Industry 4.0

Scheduling in Industry 4.0 systems belongs to a class of problems that have mixed structural-temporal-logical constraints. In other words, a strong coupling is considered when product and process are created simultaneously. As a result of the proven NP-hardness of such problems, solution methods have extensively utilized different decomposition principles. The known decomposition methods in discrete optimization are founded on the difficulties in deriving analytical properties. The existing solutions in continuous optimization are based on the maximum principle and yield a dynamic process decomposition using the natural logic of time. By combining the advantages of continuous and discrete optimization, this chapter develops a decomposition method for shop floor scheduling in Industry 4.0 manufacturing systems. Technically, this study proposes to decompose dynamically the large-scale assignment matrix according to the precedence relations between the operations of the jobs and considers only the operations that satisfy these precedence relations at a given time point in small-dimensional, discrete optimization models. Continuous optimization is used to generate a schedule from the assignments found in the discrete optimization models at each time point by extremizing the Hamiltonian function at this time point subject to scheduling objective(s). In addition, the execution of the operations in time can be accurately modeled in continuous time as a continuous state variable; the machine availability and capacity disturbances at the machines are also considered. The method developed provides further insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.