Sensors

Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.

Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.

Drone technology and the use of artificial intelligence (AI) offer promising advantages in various sectors, including in inspection. The use of innovative inspection technologies can make inspections more efficient overall. This research project examines various legal and economic aspects of AI-based autonomous drone inspections. It also develops a target process that represents the use of an AI-based drone inspection and controls the use of such inspection technology. In particular, this article focuses on a collaborative approach to this new inspection methodology.

The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production.

Facility managers for industrial properties are faced with the challenge of optimizing the energy efficiency of their facilities in the face of ever-increasing energy demand and rising energy costs. Digital processes that enable the comprehensive monitoring, analysis and control of energy demand offer an effective way to reduce costs, increase energy efficiency and make optimal use of resources. Based on IoT technologies and open-source software, a cost-effective, wireless and flexible retrofit solution for real-time energy data collection has been developed.

Motion-Mining® is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining® is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.