In-situ laser-based process monitoring and in-plane surface anomaly identification for additive manufacturing using point cloud and machine learning

Jiaqi Lyu, Javid Akhavan Taheri Boroujeni, Souran Manoochehri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Additive Manufacturing (AM) is a trending technology with great potential in manufacturing. In-situ process monitoring is a critical part of quality assurance for AM process. Anomalies need to be identified early to avoid further deterioration of the part quality. This paper presents an in-situ laser-based process monitoring and anomaly identification system to assure fabrication quality of Fused Filament Fabrication (FFF) machine. The proposed data processing and communication architecture of the monitoring system establishes the data transformation between workstation, FFF machine, and laser scanner control system. The data processing performs calibration, filtering, and segmentation for the point cloud of each layer acquired from a 3D laser scanner during the fabrication process. The point cloud dataset with in-plane surface depth information is converted into a 2D depth image. Each depth image is discretized into 100 equal regions of interest and then labeled accordingly. Using the image dataset, four Machine Learning (ML) classification models are trained and compared, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Hybrid Convolution AutoEncoder (HCAE). The HCAE classification model shows the best performance based on F-scores to effectively classify the in-plane anomalies into four categories, namely empty region, normal region, bulge region, and dent region.

Original languageEnglish
Title of host publication41st Computers and Information in Engineering Conference (CIE)
ISBN (Electronic)9780791885376
DOIs
StatePublished - 2021
Event41st Computers and Information in Engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021 - Virtual, Online
Duration: 17 Aug 202119 Aug 2021

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2

Conference

Conference41st Computers and Information in Engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021
CityVirtual, Online
Period17/08/2119/08/21

Keywords

  • Additive manufacturing
  • Anomaly detection
  • Machine learning
  • Point cloud processing
  • Process monitoring

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