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Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

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A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision.

Vinicius V E Nogueira^1,2, Luiz F Barca³, Tales C Pimenta²

¹Federal Institute of Paraná, Campo Largo 83607-140, Brazil.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This study introduces an automated machine vision system for precise planar dimension measurements of mechanical parts. The cost-effective prototype offers high accuracy and repeatability, enhancing manufacturing evaluations.

Keywords:

close-range terrestrial photogrammetry computer vision monocular vision planar measurement

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Area of Science:

Mechanical Engineering
Computer Vision
Metrology

Background:

Traditional measurement methods for mechanical parts can be time-consuming and prone to human error.
Automated optical inspection systems offer potential for increased efficiency and accuracy in dimensional analysis.
Existing machine vision solutions may be limited by cost or flexibility for certain manufacturing applications.

Purpose of the Study:

To develop and validate a cost-effective, automated system for precise planar dimension evaluation of mechanical parts using machine vision.
To demonstrate the system's capability for accurate and repeatable measurements, enhancing existing manual processes.
To present a functional prototype leveraging accessible industrial computer vision components.

Main Methods:

Utilized a machine vision configuration with standard industrial components.
Implemented an improved subpixel edge-detection algorithm for enhanced measurement precision.
Conducted traceability tests and comparisons with calibrated measurement machines.

Main Results:

Achieved high precision in measuring gauge blocks, with a mean error of 0.008 mm and standard deviation of 0.0063 mm.
Demonstrated a maximum deviation of 0.013 mm for circular sample evaluation compared to a calibrated machine.
Validated the system's effectiveness and suitability for planar dimension assessments.

Conclusions:

The developed prototype successfully automates planar dimension evaluations with high accuracy and repeatability.
The system offers a cost-effective and accessible alternative to current machine vision solutions in manufacturing.
This approach expands the application of machine vision, particularly where cost or agility is a constraint.