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On-line estimation of laser-drilled hole depth using a machine vision method.

Chao-Ching Ho¹, Jun-Jia He, Te-Ying Liao

¹Department of Mechanical Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan. HoChao@yuntech.edu.tw

Sensors (Basel, Switzerland)

|November 1, 2012

Summary

This study introduces a machine vision method to estimate laser-drilled hole depths in real time. The system correlates cumulative plasma size with hole depth for increased productivity.

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Area of Science:

Manufacturing Engineering
Optical Engineering
Materials Science

Background:

Real-time monitoring of laser drilling processes is crucial for quality control and productivity.
Traditional methods for depth measurement can be time-consuming and may disrupt the manufacturing process.
Developing non-invasive, in-situ inspection systems is a key challenge in advanced manufacturing.

Purpose of the Study:

To develop and present a novel machine vision-based method for real-time monitoring and estimation of laser-drilled hole depths.
To establish a correlation between laser-induced plasma characteristics and drilled hole dimensions.
To create a low-cost, on-line inspection system to enhance productivity in laser machining.

Main Methods:

Utilizing on-line image acquisition and analysis during laser machining.

Keywords:

laser drilling laser machining laser-drilled hole depth machine vision on-line estimation

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Correlating machining process parameters with analyzed images.

Developing a machine vision algorithm to estimate hole depth based on image data.

Investigating the relationship between the cumulative size of the laser-induced plasma region and the hole depth.

Main Results:

A strong linear correlation was identified between the cumulative plasma size and the estimated depth of laser-drilled holes.
The machine vision method demonstrated high confidence in real-time depth estimation.
A low-cost on-line inspection system was successfully developed and implemented.
The system operates effectively in air under standard atmospheric conditions with gas assist.

Conclusions:

The proposed machine vision method offers a reliable and efficient approach for real-time depth estimation of laser-drilled holes.
The established correlation between plasma size and hole depth provides a novel basis for in-situ quality control.
The developed system has the potential to significantly increase productivity in laser machining applications.