doi: 10.7763/IJMO.2024.V14.847
Novel Selection Method of Drilling Condition Based on Data Mining of a Microdrill Catalog Database
Shunya Tanaka
*
,
Toshiki Hirogaki
,
Eiichi Aoyama
- Department of Mechanical Engineering, Doshisha University, 1-3, Tatara Miyakodani, Kyotanabe, Kyoto, Japan
- Manuscript received April 10, 2023
- revised May 24, 2023
- accepted August 23, 2023
- published March 7, 2024
Abstract
Printed circuit boards (PCBs) are circuits written on copper foil fixed to boards that are composed of electrically nonconductive glass fiber cloth and resin. Electrical products are becoming more miniaturized. As a result, drilling the PCBs has become increasingly difficult. Micro-drilling of PCBs is suitable for forming through holes. The drilling quality also affects the copper plating quality, which affects the reliability of the electrical connection. Thus, it is necessary to improve PCB drilling technology, develop drilling methods that increase productivity, and secure the reliability of the electrical connection. Many studies on the cutting force, temperature, and quality of drilled holes in PCB drilling have been conducted. Research on the cutting force in micro-drilling revealed that increasing the cutting force decreased the quality of the drilled holes and affected the breakage of microdrills. Currently, skilled engineers select the tools and drilling conditions, which is difficult for unskilled engineers. In micro-drilling, engineers must select drilling conditions such as the spindle speed and infeed rate. The system must support tool selection and drilling condition decisions based on open knowledge and data.
Keywords
- microdrill
- drilling conditions
- data mining
- machine-learning
- random forest
- printed circuit boards
How to Cite
Shunya Tanaka, Toshiki Hirogaki, and Eiichi Aoyama, "Novel Selection Method of Drilling Condition Based on Data Mining of a Microdrill Catalog Database," International Journal of Modeling and Optimization, vol. 14, no. 1, pp. 39-43, 2024. https://doi.org/ 10.7763/IJMO.2024.V14.847
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).