Related papers: Evaluating an assembly- and disassembly-oriented e…

Analyzing a Feature Model (FM) and reasoning on the corresponding configuration space is a central task in Software Product Line (SPL) engineering. Problems such as deciding the satisfiability of the FM and eliminating inconsistent parts of…

Data-Driven Product Development (DDPD) leverages data to learn the relationship between product design specifications and resulting properties. To discover improved designs, we train a neural network on past experiments and apply Projected…

With FPGAs now being deployed in the cloud and at the edge, there is a need for scalable design methods which can incorporate the heterogeneity present in the hardware and software components of FPGA systems. Moreover, these FPGA systems…

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resilience. However, its sustainability remains underexplored, as…

Assembly systems constitute one of the most important fields in today industry. In this paper we propose an open distributed architecture for the engineering of evolvable flexible hybrid assembly systems. The proposed architecture is based…

The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented.…

Context: Formal methods (FMs) have been around for a while, still being unclear how to leverage their benefits, overcome their challenges, and set new directions for their improvement towards a more successful transfer into practice.…

In this work, we propose a fully coupled multiscale strategy for components made from short fiber reinforced composites, where each Gauss point of the macroscopic finite element model is equipped with a deep material network (DMN) which…

Developing complex software systems is costly, time-consuming and error-prone. Model- driven development (MDD) promises to improve software productivity, timeliness, quality and cost through the transformation of abstract application models…

This paper introduces a conceptual, yet quantifiable, architecture framework by extending the notion of system modularity in its broadest sense. Acknowledging that modularity is not a binary feature and comes in various types and levels,…

This paper is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this problem is proposed. In particular, a notion termed modality…

Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple,…

As robots proliferate in manufacturing, Design for Robotic Assembly (DfRA), which is designing products for efficient automated assembly, is increasingly important. Traditional approaches to DfRA rely on manual planning, which is…

Model-Based Development (MBD) is widely used for embedded controls development, with Matlab Simulink being one of the most used modelling environments in industry. As with all software, Simulink models are subject to evolution over their…

Identifying drawbacks or insufficiencies in terms of safety is important also in early development stages of safety critical systems. In industry, development artefacts such as components or units, are often reused from existing artefacts…

Modularity is the fundamental aspect of modern software engineering, however many advanced modularity techniques requires prospective technologies as part of development and operation process. In this paper, we present Refinable Function,…

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from…

Background: Contract-based Design (CbD) is a valuable methodology for software design that allows annotation of code and architectural components with contracts, thereby enhancing clarity and reliability in software development. It…

Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is…

Dynamic Mode Decomposition (DMD) is an unsupervised machine learning method that has attracted considerable attention in recent years owing to its equation-free structure, ability to easily identify coherent spatio-temporal structures in…