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

Abstract

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.

Intelligent Retrofitting Paradigm for Conventional Machines towards the Digital Triplet Hierarchy

“…The most imperative ones are the contemporary system's digital consciousness, the condition of the machinery, and the performance requirement that determines the type of connectivity. Digital retrofitting [38][39][40][41][42][43][44][45], also known as Brownfield development [37], is an IoT upgrade that improves the utilisation of existing infrastructure with legacy equipment and software. These two definitions, according to some authorities, can be employed alternatively [40].…”

“…Digital retrofitting [38][39][40][41][42][43][44][45], also known as Brownfield development [37], is an IoT upgrade that improves the utilisation of existing infrastructure with legacy equipment and software. These two definitions, according to some authorities, can be employed alternatively [40]. In this context, the overwhelming majority of the articles that were reviewed made use of smart retrofitting to provide predictions regarding the outcomes of developed assets.…”

“…Moreover, at the advanced level of the industrial automation pyramid, predictive and decision-making strategies can be deployed through a virtual representation of physical assets not only for the automation and control level but also for both maintenance and management levels. According to B. Ralph et al [40], a six-layer digitalisation architecture was developed and integrated into a fully integrated cyber-physical production system for the rolling aggregate as a new retrofitting approach for the transformation of outdated machine systems into a low-cost, smart, and user-friendly approach with machine-learning algorithms. Digitalisation of sensors and controllers in conjunction with the creation of digital twin structures included not only condition monitoring and project management-related data but also process data, with the ultimate aim of developing multiple low-cost user-centred CPPs as a result of the smartly retrofitted approach.…”

Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review

“…The implementation of I-CPS in metal forming industry requires sophisticated interactions among different digital technologies. Taking the digital transformation of a rolling mill as an example, the full integration of I-CPS in the rolling mill incorporates the sensing technology for collecting real-time big data, big data processing technology, and data visualisation technology for mapping the effects of milling parameters [32].…”

Smart Retrofit: An Innovative and Sustainable Solution

“…In addition, smart retrofitting emphasises the implementation of software systems, Digital Twins and machine learning algorithms in old machines to integrate them with other "smart" devices. A Digital Twin, in particular, is a technology that enables, together with the integration of intelligent sensors, the transformation of obsolete machine systems into a low-cost, intelligent and user-friendly system that enables condition monitoring, project management data and access to process data, with the ultimate goal of developing more user-centred CPS [22]. Another goal of smart retrofitting is the performance of predictive maintenance by introducing the influence of a virtual model of the physical counterpart and mitigating high-risk incidents for technicians and industrial assets.…”