ETCIIM 2022 Abstracts

Area 1 - International Workshop on Emerging Trends and Case-Studies in Industry 4.0 and Intelligent Manufacturing
Full Papers Short Papers

Area 1 - International Workshop on Emerging Trends and Case-Studies in Industry 4.0 and Intelligent Manufacturing

Full Papers
Paper Nr: 2
Title:

Digital Twin-enabled Application Architecture for the Process Industry
Authors:

Christoph Nölle, Asier Arteaga, Joseba Egia, Antonio Salis, Gabriele De Luca and Norbert Holzknecht
Abstract: We develop a software platform architecture for the integration of heterogeneous software applications in the process industries, ranging from physical simulation models to data-driven AI applications and visualizations. Digital twins play a key role in this architecture, providing harmonised interfaces to a diverse set of data sources, based on a domain-specific data model rooted in standard ontologies. We investigate the applicability of existing standards and open-source software, demonstrating on the one hand their immense potential for software-based innovations in the process industries, but also highlighting some shortcomings and the need for further developments. Finally, a concrete implementation and data model for the production of steel long products is presented.
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Paper Nr: 3
Title:

Cognitive Solutions in Process Industry: H2020 CAPRI Project
Authors:

Cristina Vega, Daniel Gómez and Aníbal Reñones
Abstract: The CAPRI project is a H2020 project that develops Cognitive Solutions (CS) to the Process Industry and a Cognitive Automation Platform (CAP) towards the Digital Transformation of process industries. CAPRI enables cognitive tools to provide to the existing process industries flexibility of operation, improving the performance and quality control of its products and flows. The project is developing and testing different CS’s at each automation level, from sensors to planning. The content of this paper is focused on the CAPRI asphalt production applying different CS’s for the sensors and control levels. Specifically the paper discusses a cognitive sensor for measuring filler quantity to the filter at drying process (noted as CAS2) and cognitive control concept applied to optimize the operation of the rotary dryer (noted as CAC1). The paper explains also how the CS’s are being integrated by means of an open source architecture based on FIWARE. The paper provides also open access to the data and algorithms used as part of the commitment of CAPRI with open science.
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Paper Nr: 4
Title:

ECD Test: An Empirical Way based on the Cumulative Distributions to Evaluate the Number of Clusters for Unsupervised Clustering
Authors:

Dylan Molinié and Kurosh Madani
Abstract: Unsupervised clustering consists in blindly gathering unknown data into compact and homogeneous groups; it is one of the very first steps of any Machine Learning approach, whether it is about Data Mining, Knowledge Extraction, Anomaly Detection or System Modeling. Unfortunately, unsupervised clustering suffers from the major drawback of requiring manual parameters to perform accurately; one of them is the expected number of clusters. This parameter often determines whether the clusters will relevantly represent the system or not. From literature, there is no universal fashion to estimate this value; in this paper, we address this problem through a novel approach. To do so, we rely on a unique, blind clustering, then we characterize the so-built clusters by their Empirical Cumulative Distributions that we compare to one another using the Modified Hausdorff Distance, and we finally regroup the clusters by Region Growing, driven by these characteristics. This allows to rebuild the feature space’s regions: the number of expected clusters is the number of regions found. We apply this methodology to both academic and real industrial data, and show that it provides very good estimates of the number of clusters, no matter the dataset’s complexity nor the clustering method used.
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Paper Nr: 6
Title:

Traveling Salesman Problem: A Case Study of a Scheduling Problem in a Steelmaking Plant
Authors:

Kai Krämer, Ludger van Elst and Asier Arteaga
Abstract: To be competitive as a company today, it is important to have key competences such as flexibility and the ability to offer a wide range of products and minimize costs. In this article, we report on an steelmaking plant and its scheduling problem. We have interpreted the optimization problem as a travelling salesman problem and show how it can be modelled. To minimize the problem we chose the simulated annealing algorithm and see how the object function can be adapted to consider-factory based constraints and how to fasten the computation time with simple techniques.
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Short Papers
Paper Nr: 1
Title:

Asset Administration Shell Generation and Usage for Digital Twins: A Case Study for Non-destructive Testing
Authors:

Fatih Yallıç, Özlem Albayrak and Perin Ünal
Abstract: In a real manufacturing site, containing non-destructive testing machinery components and sensors, we have implemented and validated Asset Administration Shell, using different tools and technologies. The tools included emerging technologies-related tools, such as Administration Shell IO, Eclipse BaSyx, and low code programming software for event-driven applications, namely Apache StreamPipes and Node-RED. We have also developed International Data Spaces connectors for data exchange using previously created Asset Administration Shells. All of the implementations in the case study have been implemented by the same developer, the first author, while the developed outputs have been verified and validated by different various testers. In this paper, we present the emerging digital twin technologies, and share different solution architectures using these technologies for the purpose of secure, standard and interoperable digital twin solutions, and data exchange between different International Data Spaces connectors. We conclude that the presented designs are easy to implement. We found Admin Shell IO to be easier to use than the Eclipse BaSyX. Our future studies will contain the use of Fraunhofer Advanced Asset Administration Shell Tools for digital twin development in the same environment, and a comparison of the implementations using different methodologies and tools.
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Paper Nr: 5
Title:

Wireless Industrial Communication and Control System: AI Assisted Blind Spot Detection-and-Avoidance for AGVs
Authors:

Sergiy Melnyk, Shreya Tayade, Mervat Zarour and Hans D. Schotten
Abstract: An Edge cloud based industrial control systems set high requirements on the latency and reliability of wireless communication link. In order to improve the performance of the communication system, an approach of industrial control and communication co-design is proposed. The system consists out of three components; Artificial Intelligence(AI) control, Industrial control and Communication control. An AI predictive algorithm forecasts the expected signal strength and detects the potential coverage blind spots on a factory floor. Based on this, industrial control system adjusts the paths for AGVs in order to spatially as well as timely avoid the communication drops. The communication control manages the communication resources taking into consideration the present control requirements and AI predicted channel information. Besides, the communication system is enhanced by multi-RAT capability in order to further increase the communication reliability. The investigations show that AI based industrial control and communication co-design approach provides an increase of the reliability of communication link. Even more, the proposed system features the ability of reliability guarantee, based on the applications’ requirements.
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