Abstract
Laser ablation of polycrystalline cubic boron nitride (PCBN) material has been a great interest to cutting tool design and machining community due to distinct advantages offered by laser surface texturing on flank and rake surfaces of cutting tools for improved friction, reduced tool wear, and enhanced effectiveness of coolant application. There are challenges on controlling the ablation depth and surface integrity induced by laser processing on the PCBN material with CBN grains often with secondary phase as titanium and tungsten carbide, aluminum nitride/aluminum diboride. Surface topography and surface integrity impose effects on the resultant wear and thermal fatigue performance on the cutting tool material. This study investigates the process maps concerning the effect of laser processing parameters on ablation depth of PCBN gathered from several research works on laser ablation and proposes a simulation to predict the laser ablation depth and profile on various CBN content substrates. The results on laser ablation depth are validated against the work in literature as well as experiments conducted using high repetition rate nanosecond laser pulses. Additionally, relations between laser and scanning parameters on the ablation depth have been identified using thermal modeling combined with machine learning, bringing a deeper understanding for texture design and planning of laser surface processing of PCBN.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 785-800 |
| Number of pages | 16 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 118 |
| Issue number | 3-4 |
| DOIs | |
| State | Published - Jan 2022 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering
Keywords
- Laser processing
- Machine learning
- Modeling
- Polycrystalline cubic boron nitride