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

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.