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Updated: Nov 27, 2025

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Objective 3D Printed Surface Quality Assessment Based on Entropy of Depth Maps.

Jarosław Fastowicz1, Marek Grudziński2, Mateusz Tecław1

  • 1Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, 70-313 Szczecin, Poland.

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|December 3, 2020
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Summary

This study introduces an automated method for assessing 3D print surface quality using depth map analysis and entropy. This technique enables real-time quality control during additive manufacturing, saving resources.

Keywords:
3D prints3D scanningadditive manufacturingdepth mapsimage analysisimage entropymachine visionsurface quality assessment

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Area of Science:

  • Additive Manufacturing
  • Quality Control
  • Computer Vision

Background:

  • Additive manufacturing (AM) technologies are rapidly advancing, necessitating robust methods for monitoring printing processes and ensuring final product quality.
  • Current quality assessment methods often rely on visual inspection or post-processing analysis, which can be time-consuming and subjective.
  • Integrating real-time quality assessment directly into the 3D printing workflow is crucial for efficiency and defect prevention.

Purpose of the Study:

  • To develop an automated, objective method for assessing the surface quality of 3D printed objects during the additive manufacturing process.
  • To enable real-time feedback for potential in-process corrections or early termination of low-quality prints.
  • To evaluate the effectiveness of entropy analysis on 3D scan data for surface regularity assessment, independent of filament color.

Main Methods:

  • The study proposes an automatic objective assessment of surface quality based on the analysis of depth maps obtained from 3D scanners.
  • Entropy analysis is applied to the 3D scan data to evaluate surface regularity.
  • The method is designed for integration with devices equipped with built-in 3D scanners for in-process monitoring.

Main Results:

  • The proposed entropy analysis of 3D scans effectively evaluates surface regularity, irrespective of filament color, overcoming limitations of visible light image analysis.
  • The depth map analysis allows for objective, real-time assessment of surface quality during the 3D printing process.
  • The approach demonstrated encouraging results for in-process quality monitoring and defect detection.

Conclusions:

  • The developed method provides a reliable, automated approach for assessing 3D print surface quality using depth maps and entropy analysis.
  • This technique facilitates real-time quality control, enabling resource savings (filament, time, energy) by allowing corrections or process abortion.
  • Future work may involve combining this depth map-based approach with camera-based vision methods for enhanced quality assessment.