Production Control

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Digital Twins for Production

Digital Twins for Production

RAPIDZ — Resource analysis and process integration through digital twins
Christian Salzig ORCID Icon, Julia Burr ORCID Icon, Sophie Hertzog
In today’s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 6-12 | DOI 10.30844/I4SE.25.3.6
STAG — Bridging Data from Shop Floor to IT World

STAG — Bridging Data from Shop Floor to IT World

An automated mapping approach for improved access to shop floor data
Oliver Amft ORCID Icon, Dovydas Girdvainis ORCID Icon, Christoph Rathfelder ORCID Icon
Collecting data from different sources on the shop floor and making it accessible to different IT systems is one of the core tasks during the process of factory digitization. Due to the different protocols and interfaces, the data collection task comes with unique challenges. With the Sensor Technology Adapter Gateway (STAG), we present a solution that closes the gap between the shop floor and the IT system’s backend. STAG is an industry-grade middleware that automates translations between data models and protocols.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 14-22 | DOI 10.30844/I4SE.25.3.14
Open-Source and Cost-Effective Digital Twin

Open-Source and Cost-Effective Digital Twin

A case study with two weeks to succeed
Shantall Cisneros Saldana ORCID Icon, Sonali Pratap, Parth Punekar, Sampat Acharya, Heike Markus ORCID Icon
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.
Industry 4.0 Science | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844/I4SE.25.3.62
Boosting Competitiveness in Small Batch Production

Boosting Competitiveness in Small Batch Production

Scalable and flexible body-in-white production line with collaborative mobile robots
Walid Elleuch, Tadele Belay Tuli ORCID Icon, Martin Manns ORCID Icon
Due to the higher customization of products to customer groups and needs, body-in-white manufacturing industries are facing higher variant assembly at the later stages of the production line, thus increasing production costs per unit. Flexible production processes that involve flexible material flows, non-rigid manufacturing sequences, and the automatic reconfiguration of tools are regarded as the pillars of a resilient production system. This article presents a conceptual solution for flexible Body-in-White sheet metal production with autonomous collaborative robotic systems to make product costs affordable for a higher competitive advantage.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 60-67
The “InTraLab” Learning Factory

The “InTraLab” Learning Factory

Gaining experience and knowledge in digitally transformed work environments
Norbert Gronau ORCID Icon, Malte Rolf Teichmann, Malte Teichmann
Learning factories offer a practical environment for simulating production processes in which learners can acquire skills through the direct application of new technologies. The Industrial Transformation Lab (InTraLab) models hybrid production processes by combining real-world demonstrators and virtual simulations. This enables learners to acquire the skills that are crucial for the digitally transformed world of work.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 46-51
Error Management in Production

Error Management in Production

Current situation and challenges in the industry
Johannes Prior ORCID Icon, Milan Brisse ORCID Icon, Nikita Govorov, Robert Egel ORCID Icon, Bernd Kuhlenkötter ORCID Icon
This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 38-45
Introduction of Machine Learning in Production

Introduction of Machine Learning in Production

An SME-specific, holistic guide
Manuel Savadogo, Malte Stonis ORCID Icon, Peter Nyhuis ORCID Icon
Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 88-95
Parameter Optimization for a Brine Injector

Parameter Optimization for a Brine Injector

Development of an AI pipeline using an example from the meat industry
Tim Zeiser ORCID Icon, Theo Lutz ORCID Icon, Corinna Köters ORCID Icon, Maik Schürmeyer, Alexander Prange ORCID Icon
The production of cooked ham involves a number of challenges. In production, cuts of meat are put through in a multi-stage curing and cooking process involving brine. This can lead to fluctuations in quality due to structural defects in the meat. The result: the brine is not optimally absorbed. An AI model trained on historical data intends to solve the problem.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 40-46
Intelligent Shopfloor Assistants

Intelligent Shopfloor Assistants

Increasing productivity through the use of generative AI
Eckart Uhlmann ORCID Icon, Julian Polte ORCID Icon, Christopher Mühlich ORCID Icon, Yassin Elsir
In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced.
Industry 4.0 Science | Volume 40 | 2024 | Edition 6 | Pages 64-71
Double Transformation in Mechanical and Plant Engineering

Double Transformation in Mechanical and Plant Engineering

Digitalization and sustainability for one-of-a-kind and small-batch manufacturers
Christoph Laroque ORCID Icon, Deike Gliem ORCID Icon, Sigrid Wenzel ORCID Icon
A decisive competitive factor for smaller and medium-sized manufacturers of one-of-a-kind and small batches is their products’ timely completion, delivery and commissioning. Precise logistics planning is just as important as production control. However, the processes are often characterized by uncertainties, e.g. due to local conditions at the customer or cooperation with suppliers. Digital shadows for data evaluation in real time offer a convincing solution.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 10-17 | DOI 10.30844/I4SE.24.5.10
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