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General Information
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2024 Vol.14(1): 39-43
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, and Eiichi Aoyama
Department of Mechanical Engineering, Doshisha University, 1-3, Tatara Miyakodani, Kyotanabe, Kyoto, Japan Email: tanashun0706@gmail.com (S.T.); thirogak@mail.doshisha.ac.jp (T.H.); eaoyama@mail.doshisha.ac.jp (E.A.)
*Corresponding author

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


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.

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).

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