Project Details
Description
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 results and effects:
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.
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.
Development of AI solutions to control and influence the evolution of tool wear (to shape it), predict tool damage and estimate process efficiency.
Expected results and effects:
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.
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.
Acronym | ONCODE |
---|---|
Status | Active |
Effective start/end date | 2023/11/06 → 2026/11/05 |
Funding
- Swedish Government Agency for Innovation Systems (Vinnova)