Month: January 2024

Sketches to models…

Image by 127071 from Pixabay

https://www.computer.org/csdl/proceedings-article/models/2023/248000a173/1SOLExN0XaU

It’s been a while since I worked with models and I looked a bit at how things have evolved. As I remember, one of the major problems with modelling was one of its broken promises – simplicity.

The whole idea with modelling was to be able to sketch things, discuss candidate solutions and then to transfer them on paper. However, in practice, this never worked like that – the sheer process to transfer a solution from the whiteboard to a computer took time. Maybe even so much time that it was not really worth the effort of informal sketches.

Now, we have CNNs and all kinds of ML algorithms, so why not use that? This paper studies exactly this.

The paper “SkeMo: Sketch Modeling for Real-Time Model Component Generation” by Alisha Sharma Chapai and Eric J. Rapos, presents an approach for automated and real-time model component generation from sketches. The approach is based on a convolutional neural network which can classify the sketches into model components, which is integrated into a web-based model editor, supporting a touch interface. The tool SkeMo has been validated by both calculating the accuracy of the classifier (the convolutional neural network) and through a user study with human participants. At the moment, the tool supports classes and their properties (including methods and attributes) and relationships between them. The prototype also allows updating models via non-sketch interactions with models. During the evaluation the classifier performed with an average precision of over 97%. The user study indicated the average accuracy of 94%, with the maximum accuracy for six subjects of 100%. This study shows how we can successfully employ machine learning into the process of modeling to make it more natural and agile for the users.

Modelling digital twins…

Image by 652234 from Pixabay

https://www.computer.org/csdl/proceedings-article/models/2023/248000a013/1SOLEPphpHa

Digital twins are becoming increasingly important. They provide a possibility to monitor their real twin without the need for costly measurements and sending technicians to the site where the real twin is located. However, development of them is not so easy and is almost one-off for every twin pair.

The paper “A Model-driven Approach for Knowledge-based Engineering of Industrial Digital Twins” presents a new approach to constructing digital twins for factories. Authored by Sushant Vale, Sreedhar Reddy, Sivakumar Subramanian, Subhrojyoti Roy Chaudhuri, Sri Harsha Nistala, Anirudh Deodhar, and Venkataramana Runkana, it introduces a method that enhances efficiency of monitoring and predictive maintenance of industrial plants.

Typically, digital twins are created manually for each plant, which is a labor-intensive process. This paper proposes a model-driven method, structured on three levels of abstraction: the meta-level, plant-type level, and plant-instance level. The meta-level outlines universal structures and vocabulary, the plant-type level focuses on knowledge specific to various plant types, and the plant-instance level details a digital twin for a specific plant. These levels correspond to different user roles: platform builders, plant type experts, and plant experts, respectively. This hierarchical structure enables element reuse across different plants and types, streamlining the digital twin development process. The effectiveness of this method is exemplified in a case study of an iron ore sinter plant.

The process begins with establishing high-level Key Performance Indicators (KPIs) such as sinter throughput or reduction degradation index. These KPIs are then translated into a mathematical model, followed by a causal graph, and finally, a digital twin design/model. Remarkably, this approach significantly reduced the time required to formulate the quality optimization problem to approximately one week, down from two months, marking a substantial improvement in efficiency. In conclusion, this paper demonstrates the substantial advantages of a multi-level modeling approach in designing digital twins, offering a more efficient, standardized, and scalable solution.