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

Javid Akhavan, Jiaqi Lyu and Souran Manoochehri ()
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Javid Akhavan: Stevens Institute of Technology
Jiaqi Lyu: Stevens Institute of Technology
Souran Manoochehri: Stevens Institute of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 3, No 25, 1389-1406

Abstract: 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.

Keywords: Smart additive manufacturing; Point cloud processing; Defect detection and classification; Machine learning (ML) and deep learning (DL); Digital manufacturing; Adaptive process control (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02121-4

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