This paper describes the transformation of a rolling mill aggregate from a stand-alone solution to a fully integrated cyber physical production system Within this process, already existing load cells were substituted and additional inductive and magnetic displacement sensors were applied After calibration, those were fully integrated into a six-layer digitalization architecture at the Smart Forming Lab at the Chair of Metal Forming (Montanuniversitaet Leoben) Within this framework, two front end human machine interfaces were designed, where the first one serves as a condition monitoring system during the rolling process The second user interface visualizes the result of a resilient machine learning algorithm, which was designed using Python and is not just able to predict and adapt the resulting rolling schedule of a defined metal sheet, but also to learn from additional rolling mill schedules carried out This algorithm was created on the basis of a black box approach, using data from more than 1900 milling steps with varying roll gap height, sheet width and friction conditions As a result, the developed program is able to interpolate and extrapolate between these parameters as well as different initial sheet thicknesses, serving as a digital twin for data-based recommendations on schedule changes between different rolling process steps Furthermore, via the second user interface, it is possible to visualize the influence of this parameters on the result of the milling process As the whole layer system runs on an internal server at the university, students and other interested parties are able to access the visualization and can therefore use the environment to deepen their knowledge within the characteristics and influence of the sheet metal rolling process as well as data science and especially fundamentals of machine learning This algorithm also serves as a basis for further integration of materials science based data for the prediction of the influence of different materials on the rolling result To do so, the rolled specimens were also analyzed regarding the influence of the plastic strain path on their mechanical properties, including anisotropy and materials’ strength

/pdf/transformation-of-a-rolling-mill-aggregate-to-a-cyber-342q3tx0i2.pdf

Transformation of a rolling mill aggregate to a cyber physical production system: from sensor retrofitting to machine learning

Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.

https://www.mdpi.com/1424-8220/22/8/2972/pdf?version=1649840127

Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing.

Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

ABSTRACT While the fourth industrial revolution continues to change manufacturing enterprises all over the world, not all enabling key technologies are taught sufficiently at universities. For this purpose, a new lecture at the Montanuniversität Leoben was designed, teaching students of miscellaneous engineering disciplines, the fundamentals of digitalization and digital transformation, tailored to the requirements of the industry. To elevate the necessary data from these segments, a representative part of the Austrian metal forming companies was analyzed by conducting a large-scale survey. For the sake of teaching efficiency, a survey revealing students' knowledge regarding digitalization and digital transformation and preferred learning methods were also carried out. As a result, a stakeholder-oriented lecture was developed. Furthermore, a general framework on how innovative transdisciplinary academic lectures in engineering can be developed in an efficient, effective, and practical way was derived, which aims to close the gap between modern engineering education and required practical skills.

Evidence-based redesign of engineering education lectures: theoretical framework and preliminary empirical evidence

The advancement of laser ultrasonics has increased rapidly in recent years, providing applications for materials characterization as well as for industrial utilization, as a quality control device. The wide-ranging capabilities for high-temperature in-situ analysis of a variety of microstructural characteristics offers a multitude of possibilities for usage in R&D. To date, this is the only known method that has been successfully deployed for in-situ materials characterization, as well as in the harsh environment of the metalworking industry. Combined with the enablers, introduced by the fourth industrial revolution, and the conjunction of a laser ultrasonic system with a smart production lab, it has great potential to contribute to lower rejection rates, better recyclability, and consequently to a more sustainable production. In this review, the potential for systemic sustainability is explained throughout a part of the value chain, in the context of Industry 4.0. In addition, the integration of the methodology into a miniaturized Smart Production Lab is demonstrated, with the intention of incorporating it as a substantial part of the creation of a digital twin. Such a lab is designed to serve as an interface between laboratory and industry, in order to reveal the possibilities of digital transformation, Industry 4.0, and the application of highly flexible systems such as the laser-ultrasonic system for companies.

/pdf/the-key-role-of-laser-ultrasonics-in-the-context-of-3gr8vqbx.pdf

The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

https://www.mdpi.com/1424-8220/21/9/2944/pdf

Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

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Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education