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Drilling force prediction and drill wear monitoring for PCB drilling process based on spindle current signal

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Abstract

In the drilling process of multilayer printed circuit boards (PCB), drill wear will reduce the stability of the drilling process and affect the processing efficiency and drilling quality, so the prediction of drilling force and the monitoring of drill wear status during the drilling process are particularly important. A spindle current-based drilling force prediction and drill wear monitoring technique with theoretical models of drilling force, drill wear, and current signal is investigated to realize a feasible and low-cost online monitoring system for PCB drilling process. The prediction and monitoring technique consists of a semi-empirical theoretical model of the drilling force and torque considering drilling parameters and drill wear as well as a theoretical model between drilling torque and spindle current. The coefficients in the theoretical models were determined by measuring drilling force and spindle current signals through the drilling experiments with variable parameters and drill wear. The experimental results show that the relative errors between the predicted and experimental results of spindle current increment with variable drilling parameters and wear amounts are basically less than 10% and the prediction accuracy of the drilling force model is good when the drill flank is worn in the early and middle stages, which shows that the proposed spindle current-based drilling force prediction and drill wear monitoring technique has high prediction accuracy as well as strong effectiveness.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author or the first author on reasonable request.

Code availability

The code used during the current study is available from the corresponding author or the first author on reasonable request.

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Funding

This research is supported by Innovation Research Fund of State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University [grant number SKLT2020C12].

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Authors and Affiliations

  1. Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipments and Control, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Qifeng Tan, Hao Tong & Yong Li

  2. State Key Laboratory of Tribology in Advanced Equipment (SKLT), Tsinghua University, Beijing, 100084, China

    Qifeng Tan, Hao Tong & Yong Li

Authors
  1. Qifeng Tan
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  2. Hao Tong
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  3. Yong Li
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Contributions

Qifeng Tan: investigation, conceptualization, methodology, formal analysis, data curation, validation, software, writing—original draft. Hao Tong: investigation, conceptualization, funding acquisition, project administration, writing—editing. Yong Li: funding acquisition, project administration, writing—reviewing and editing.

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Correspondence to Yong Li.

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Tan, Q., Tong, H. & Li, Y. Drilling force prediction and drill wear monitoring for PCB drilling process based on spindle current signal. Int J Adv Manuf Technol 126, 3475–3487 (2023). https://doi.org/10.1007/s00170-023-11302-7

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  • DOI: https://doi.org/10.1007/s00170-023-11302-7

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