2025

Procedia Computer Science

Journal year: 2025 | Journal number: 270

scientific article / paper

english

EN This article presents a comprehensive concept of a system supporting the effective and optimized integration of knowledge within project teams operating in the mechanical engineering field. The proposed solution is based on a two-stage methodology that combines advanced artificial intelligence technologies, natural language processing, and optimization algorithms to enable the automatic analysis and synthesis of knowledge from diverse, often heterogeneous sources. A key feature of the concept is the integration of optimization methods at every stage of the process – from information collection and selection, through the identification of research gaps, to the generation of innovative solutions and scientific models. Optimization encompasses both data exploration and processing procedures as well as the dynamic adaptation of the project team environment, thus enabling efficient knowledge management and acceleration of the innovation cycle. The system is designed for flexible implementation in both academic and industrial environments, providing support for multidisciplinary teams and opening new perspectives for sustainable technological development. The application of state-of-the-art optimization algorithms allows for continuous improvement of knowledge integration and decision-making processes, making the proposed system a significant step towards maximizing the effectiveness of research and development activities in mechanical engineering.

5260 - 5269

10.1016/j.procs.2025.09.653

https://www.sciencedirect.com/science/article/pii/S1877050925033265

29th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 10-12.09.2025, Osaka, Japonia

CC BY-NC-ND (attribution - noncommercial - no derivatives)

open journal

final published version

12.2025

at the time of publication

5

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