Optimization of technological preparation for production through semantic route modeling

Background

The challenge of automating Computer-Aided Process Planning (CAPP) in mechanical engineering remains relevant. Existing CAD/CAM systems fail to provide end-to-end design due to insufficient integration, deterministic algorithms, and the disconnect between design and manufacturing data. As a result, process engineers are forced to spend considerable time on routine operations: searching for information across disparate reference databases, manually transferring parameters from drawings to process sheets, and repeatedly entering the same data into GOST-compliant documentation. This reduces responsiveness to schedule changes and increases overall production costs.

Aim

To develop a technical specification and justify an information-logical model based on a directed acyclic graph that enables automated route generation, reduces technological preparation time, and improves decision quality through semantic data connectivity.

Methods

The proposed solution is an information-logical model based on a directed acyclic graph. The model relies on five integrated reference databases (materials, equipment, tooling, standard solutions, and quality control), which formalize the process. The system's operational algorithm is described: process routes are automatically generated based on the 3D model geometry and a knowledge base, while the process engineer verifies them, with provision for machine learning.

Results

Implementation of this approach reduces technological preparation time and enhances production transparency. This creates a solid foundation for integration with Manufacturing Execution System (MES) and Enterprise Resource Planning (ERP) systems and for implementing the digital twin concept.

Conclusion

Using the developed information-logical model shortens the technological preparation cycle and speeds up decision-making.