Concept of optimization for creating new knowledge integration methods for project teams in mechanical engineering

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.