Scientific publications

This document outlines the TNO-ESI vision and strategic direction for research and development in the area of systems of systems, focused on the high-tech equipment industry. A system-of-systems (SoS) is a large-scale, non-monolithic, distributed, heterogeneous system, built from multiple interacting constituent systems (CS), which are independently operating systems themselves.

Manufacturing companies of complex distributed cyber-physical systems (dCPS) are encountering challenges with respect to designing their next-generation machines. They need efficient Design Space Exploration (DSE) techniques to evaluate possible design decisions and their consequences on nonfunctional aspects of the systems.

The lack of explicit and precise specifications of software interfaces between components often leads to integration issues during development and maintenance. To address this, we have developed a framework named ComMA (Component Modeling and Analysis) that supports model based engineering of high-tech systems by precisely defining components and their interfaces.

Software analysis often relies on pattern matching in terms of Abstract Syntax Trees (ASTs), but AST patterns are known to be tedious to specify. Concrete syntax patterns with placeholders have been proposed as a user-friendly alternative. Several designs for this proposal have been implemented, but these typically focus on specific parsing technologies.

This work presents the latest advancements in leveraging artificial intelligence (AI) and digital twin (DT) technologies to automate the operation of electron microscopes, with a particular focus on exploring the feasibility of automated electron microscope alignment. The alignment of electron microscopes is a laborious process, demanding significant time and expert knowledge.

WP3 is concerned with the development of a technical approach and a reference architecture for DT-supported AI-based system optimisation. System optimisation can be performed by connecting AI to both the physical system and its DT. By allowing the AI to take control over the DT, a learning cycle based on reward and punishment can be constructed to validate its actions.

The ASIMOV project investigates the combination of Digital Twins (DTs) and Artificial Intelligence (AI) to find the opportunities and challenges for automated optimization and calibration of complex high-tech systems in complex environments. In many cases the actual system is not available for training AI components, therefore a dedicated digital twin or digital model is set up for providing that training data.

This document describes fundamental considerations towards a process for identification of relevant parameters for a digital twin. The process includes identifying parameters in the real-world product (physical twin) which are relevant for its digital twin given a specific use case and describing methods to acquire and use these parameters.

The optimization of complex cyber-physical systems is a crucial task for their correct functioning, usability, and commercial viability. Due to their complexity, scale and resource intensiveness, conventionalmanual optimization is infeasible in many instances. We investigate the combination of the Digital Twin paradigm and Reinforcement Learning framework to address the long response times, limited availability ofdata, and the intractability of such systems.

Compositionality supports the manipulation of large systems by working on their components. For model-based testing, this means that large systems can be tested by modelling and testing their components: passing tests for all components implies passing tests for the whole system. In previous work [14], we defined mutual acceptance for specification models and proved that this is a sufficient condition for compositionality in model-based testing.