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Reading Direct-Part Marking Data Matrix Code in the Context of Polymer-Based Additive Manufacturing.

Daniel Matuszczyk¹, Frank Weichert¹

¹Department of Computer Science VII, Technical University Dortmund, 44227 Dortmund, Germany.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

A new deep learning method effectively detects low-contrast data matrix codes on 3D printed polymer parts. This approach enhances traceability in manufacturing, even on lightweight devices like smartphones.

Keywords:

additive manufacturing direct-part marking neural networks

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Area of Science:

Additive Manufacturing
Computer Vision
Materials Science

Background:

Direct-part marking is crucial for traceability in manufacturing.
Low-contrast codes on polymer-based selective laser sintering (SLS) parts present detection challenges.
Existing methods struggle with unicolored, low-contrast markings.

Purpose of the Study:

To develop a robust method for detecting and decoding direct-part-marked, low-contrast Data Matrix codes on polymer-SLS parts.
To enable code readability on lightweight, mobile devices for assembly line applications.
To improve traceability in additive manufacturing processes.

Main Methods:

A deep learning approach for locating Data Matrix codes.
An image encoding network for decoding localized codes.
Generative Adversarial Networks (GANs) to enhance training data with rendered images.

Main Results:

High mean average precision of 97.38% for code localization.
Achieved a code readability rate of 89.36%.
The system is suitable for deployment on mobile devices and low-cost sensors.

Conclusions:

The novel deep learning approach successfully addresses the challenge of reading low-contrast codes on polymer-SLS parts.
This method significantly enhances traceability in assembly line production.
The system's compatibility with mobile devices offers a flexible and cost-effective solution for industrial applications.