{"id":103448,"date":"2024-02-20T13:57:49","date_gmt":"2024-02-20T12:57:49","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103448"},"modified":"2024-05-14T04:33:33","modified_gmt":"2024-05-14T02:33:33","slug":"sensors","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/tools\/sensors\/","title":{"rendered":"Sensors"},"content":{"rendered":"\n\n\n
\n
\n \n
\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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 CO\u2082 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Enabling the Future of Manufacturing with Digital Twins<\/h4>\n
Opportunities and obstacles<\/div>
Javad Ghofrani<\/a> \n \"ORCID<\/a>, Darian Lemke<\/a>, Tassilo S\u00f6ldner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 72-81<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Open-Source and Cost-Effective Digital Twin<\/h4>\n
A case study with two weeks to succeed<\/div>
Shantall Cisneros Saldana<\/a> \n \"ORCID<\/a>, Sonali Pratap<\/a>, Parth Punekar<\/a>, Sampat Acharya<\/a>, Heike Markus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844\/I4SE.25.3.62<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Collaborative\n <\/picture>\n <\/div>\n
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\t\t

Collaborative Drone Inspection<\/h4>\n
A new approach to inspection work with AI support<\/div>
Till Becker<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 94-100<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Real-time\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Real-time Reactions for Automated Guided Vehicles (AGV)<\/h4>\n
Monitoring and controlling with long latencies<\/div>
Dominik Augenstein<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 6 | Pages 56-62<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Analyzing\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Analyzing Work Processes with Motion Capture Systems<\/h4>\n
Solution and implementation principles<\/div>
Hermann L\u00f6dding<\/a> \n \"ORCID<\/a>, Silas P\u00f6ttker<\/a> \n \"ORCID<\/a>, Tim Jansen<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The double transformation describes the necessary change in the economy in the dimensions of ecology and digitalization. Motion capture systems offer new possibilities for recording and analyzing work processes in industrial assembly. They visualize motion sequences with high frequency, precision and resolution. The question therefore arises as to how the technology can be used in the context of digital transformation to further develop the analysis of work processes and the design of workplaces. Our article discusses this on the basis of solution principles and describes implementation principles for the development of upcoming digital assistance systems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 5 | Pages 43-49 | DOI 10.30844\/I4SE.24.5.42<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Digital Platform Frameworks for Manufacturing Companies<\/h4>\n
A review<\/div>
Marcel Rojahn<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In recent years, digital platforms have established themselves as a central concept in the IT field. Due to the wide variety of digital platforms available on the market, there is still a need for clear comparison with criteria to enable interested parties to select, change, operate and further develop these platforms. The following paper aims to contribute to the facilitation of this comparison by undertaking a systematic literature review of digital platform frameworks in the context of the Industrial Internet of Things (IIOT) for manufacturing companies and thus providing a basis for a number of potential ways to effectively compare current digital platforms and ecosystems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 2 | Pages 8-15 | DOI 10.30844\/I4SE.24.2.8<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Energy\n <\/picture>\n <\/div>\n
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\t\t

Energy Efficiency Through Intelligent Electricity Data Acquisition<\/h4>\n
Wireless retrofit solution based on IoT technologies and open-source software for existing industrial buildings<\/div>
Sergej Kreber<\/a>, Kevin Kutzner<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 2 | Pages 87-93<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Motion-Mining\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Motion-Mining Compared to Traditional Lean Tools<\/h4>\n
Sensor-supported analysis of manual processes in manufacturing and logistics<\/div>
Hendrik Appelhans<\/a>, Christopher Borgmann<\/a>, Carsten Feldmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Motion-Mining\u00ae 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\u00ae 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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | Edition 2 | Pages 24-31<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Cost-efficient\n <\/picture>\n <\/div>\n
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\t\t

Cost-efficient Digitization of Refrigerating Appliances Recycling<\/h4>\n
Digital twins and the path to a sustainable future<\/div>
Christian Thiehoff<\/a>, Georgii Emelianov<\/a> \n \"ORCID<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a>, Jochen Schiemann<\/a>, Mikhail Polikarpov<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>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. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 1 | Pages 76-82<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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