Abstract
For surface quality control, a digital twin model was created and tested. A prediction model with an embedded neuro-fuzzy adaptive inference engine calculates and compares the surface roughness with the required values by utilizing real-time inputs on process parameters, tool wear, acoustic emission, and force signals. Fuzzy logic controls create control commands to modify the machining variables and achieve an appropriate surface quality. Simulation results demonstrate that the developed DT system significantly reduces errors between desired and predicted surface roughness from 11 to 0.8% and effectively controls surface quality in CNC machining online. Graphical Abstract: (Figure presented.)
| Original language | English |
|---|---|
| Pages (from-to) | 2117-2130 |
| Number of pages | 14 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 135 |
| Issue number | 5-6 |
| DOIs | |
| State | Published - Nov 2024 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keywords
- Intelligent digital twin
- Machining
- Surface roughness
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering
