Open Access

Cycle time optimization is a fundamental skill for manufacturing planners to avoid bottlenecks and thus increase throughput of production. A learning factory, which replicates real-world manufacturing scenarios, provides an ideal environment for students to acquire this essential skill. Traditionally, cycle times in these scenarios have been manually recorded using stopwatches. This practice has become increasingly outdated with the proliferation of Industry 4.0 and Internet of Things systems that automatically take these measurements in industries, which the learning factories are designed to emulate. However, the high costs and implementation efforts associated with these systems can pose significant challenges for learning factories to adapt. To address these challenges, this paper proposes a cost-effective system for automatic cycle time measurements in learning factories. The system is composed of inexpensive and commercially available hardware such as microcontroller development boards, Radio-Frequency Identification (RFID) readers and a custom software based on open-source software that is free to use. It enables fast and economical retrofitting of existing production scenarios by equipping production stations with RFID readers and product trays with RFID tags. The solution not only enhances the realism of learning factories in terms of cycle time measurements but also introduces the students to key Industry 4.0 concepts like automation, digitalization, and real-time data tracking. By integrating this affordable system, learning factories can better align their practices with industry standards, thereby improving the training quality and preparing students more effectively for the future manufacturing environment.

Technologies related to Industry 4.0, such as the Internet of Things (IoT) and Artificial Intelligence (AI), find increasing applications in manufacturing systems. However, the technical implementation of IoT-based or AI-based solutions requires interaction and information exchange between the various components of complex information processing systems. Students of interdisciplinary study programs, such as industrial engineering, often possess conceptual yet isolated knowledge of manufacturing systems, IT infrastructure, and information processing without proficiency regarding application programming interface (API) usage. However, APIs are paramount for enabling the interaction of individual components of complex information processing systems. Unfortunately, adapting the general descriptions in API documentation to a student's specific application is often challenging, hindering a comprehensive hands-on learning experience for students training on implementing applications into manufacturing systems of learning factories. Therefore, this paper proposes a novel approach for leveraging Large Language Models (LLMs) to facilitate the utilization of APIs for students’ hands-on training on implementing applications and the respective information processing within the context of manufacturing systems and learning factories. The proposed approach comprises an LLM extended using context data specific to the employed test API and enables user interaction via a natural language dialogue-based chat interface.

Chatbots are increasingly being used for mental health care related to stress and other issues. With the advent of ChatGPT, conversations with chatbots have become commonplace, and a variety of support has become possible. However, future developments are expected to determine what kind of personality and persona mental healthcare chatbots will have and how they will express their emotions. Chatbots often have emotional models to show empathy to users, but only the psychological information of the users is considered, and there are few studies that cover physiological information as well. Therefore, in this study, to give the chatbot characteristics suitable for the user, we proposed a method for expressing emotions using three types of input information regarding active listening in emotional support.

Die digitale Abbildung von Maschinen und Prozessen zur Entwicklung, Auslegung, Simulation und Optimierung schreitet immer weiter voran. Steigende Leistungsfähigkeit von Rechnerkapazitäten und der Einsatz lernender Algorithmen erlaubt die immer exaktere Wiedergabe der einzelnen Komponenten, Maschinen und Abläufen. Aufgrund der Komplexität von Werkzeugmaschinen besteht aber dennoch erheblicher Bedarf zur Integration zusätzlichen Wissens in die bei der Erstellung von Digitalen Zwillingen verwendeten Daten.

The design of operational amplifiers faces the trade-off between power and speed. Increasing power-efficiency according to a certain speed requires a multi-stage design with an optimal compensation network that should be stable under all possible operating conditions. From this point of view, this paper proposes a three-stage operational amplifier with an enhanced AC boosting compensation (ACBC) using complementary indirect Miller compensation. The proposed amplifier is implemented in GlobalFoundries 22FDX (22 nm FD-SOI technology). In the worst case of the post-layout simulation, the proposed design provides a power efficiency IFOMs of 231k MHz.pF/mW, which is 2x larger compared to similar designs with ACBC as the standard compensation technique while driving a 500 pF load. According to this IFOMs, the design achieves an open-loop gain of 103.5 dB, a gain-bandwidth product of 8.5 MHz with a phase margin of 41.4 while the amplifier operates with a 10% reduced supply voltage of 0.72 V in ss-corner under 125. A total current of only 18.7 including the bias-network, is consumed.