Toolbox for Online control and Design of tool wear mechanisms when cutting difficult-to-machine materials (ONCODE)
| Reference number | |
| Coordinator | Lunds universitet - Lunds Tekniska Högskola Inst f maskinvetenskaper |
| Funding from Vinnova | SEK 2 150 000 |
| Project duration | November 2023 - November 2026 |
| Status | Ongoing |
| Venture | Advanced digitalization - Enabling technologies |
| Call | AI for advanced digitalization, 2 |
Purpose and goal
Development of AI solutions to control and influence the evolution of tool wear (to shape it), predict tool damage and estimate process efficiency.
Expected effects and result
The project addresses the development of a toolbox for an AI-based platform/demonstrator of PCM when machining difficult-to-cut materials (relatively expensive materials for responsible parts, where precision and quality are of vital importance) with applications in aerospace and automotive industries (Ti- and Ni-based). The developed solution(s) will also be of great interest to tool manufacturers in the form of a recommender system for customers with different needs.
Planned approach and implementation
Using Reinforcement Learning (RL) terminology, the problem statement can be formulated as follows: development of the agent which consists of the interacting AI-based Digital Twin (DT) of the process, TCM, and Decision Making (DM) blocks reacting on the lubricant/coolant supply and estimating process efficiency through the observations obtained by the array of sensors. Non-RL solution will look like several interacted AI solutions (TCM - DT - DM) integrated into the PCM system.