Abstract
Historically, cutting force and surface roughness are known to be important performance indicators in conventional machining operations and are mainly affected by material type and the choice of cutting tool. One well-known method to improve cutting tool performance is covering these tools with durable ceramic coatings to protect them from wear and thermal degradation. This work elucidates the advantage of Al2O3 and TiN coatings and presents important performance improvements in turning operation. Process parameters such as cutting speed, feed rate, cutting depth and tip radius were taken into consideration in a total of 540 experiments. The design of the experiment and a statistical analysis were performed to reveal significant process parameters. A special experimental setup was designed to measure in-situ cutting forces. The surface roughness of the machined surfaces was measured. An artificial neural network model was developed to predict optimum performance parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 893-901 |
| Number of pages | 9 |
| Journal | Materialpruefung/Materials Testing |
| Volume | 60 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - Aug 2018 |
| Externally published | Yes |
Keywords
- Artificial neural network
- Carbide/cermet cutting tools
- Cutting force
- Surface roughness
- Turning operations
Fingerprint
Dive into the research topics of 'Performance of coated and uncoated carbide/cermet cutting tools during turning'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver