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Journal of Intelligent Manufacturing

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11-04-2023

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

Authors: Javid Akhavan, Jiaqi Lyu, Souran Manoochehri

Published in: Journal of Intelligent Manufacturing | Issue 3/2024

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Abstract

This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated artifacts. The top surface point-cloud containing top surface geometry and quality information is pre-processed and passed to an improved deep Hybrid Convolutional Auto-Encoder decoder (HCAE) model used to statistically describe the artifact's quality. The HCAE’s output is comprised of 9 × 9 segments, each including four channels with the segment's probability to contain one of four labels, Under-printed, Normally-printed, Over-printed, or Empty region. This data structure plays a significant role in command generation for fabrication process optimization. The HCAE’s accuracy and repeatability were measured by a multi-label multi-output metric developed in this study. The HCAE’s results are used to perform a real-time process adjustment by manipulating the future layer's fabrication through the G-code modification. By adjusting the machine's print speed and feed-rate, the controller exploits the subsequent layer’s deposition, grid-by-grid. The algorithm is then tested with two defective process plans: severe under-extrusion and over-extrusion conditions. Both test artifacts' quality advanced significantly and converged to an acceptable state by four iterations.

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Title: A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data
Authors: Javid Akhavan
Jiaqi Lyu
Souran Manoochehri
Publication date: 11-04-2023
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 3/2024
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-023-02121-4

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