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CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning

Published in INDIN, 2022

Authors: Jiahui Wang, Haiyue Zhu , Haoren Guo , Abdullah Al Mamun , Prahlad Vadakkepat , Tong Heng Lee

3D part segmentation plays a critical role in improving the efficiency and reducing defects in CAM/CAD manufacturing workflows, particularly for CNC machinery. Traditional segmentation methods rely on fully-supervised learning, which requires large annotated datasets and struggles to generalize to new segmentation tasks. To address these limitations, the authors propose a few-shot learning approach that improves generalization and flexibility by using fewer samples. Their method, inspired by the attMPTI network, incorporates a multi-prototype approach with self-attention, along with transform net and center loss block, to better capture 3D features while reducing storage space and enhancing processing efficiency. Read more

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Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools

Published in INDIN, 2022

Authors: Haoren Guo, Haiyue Zhu , Jiahui Wang , Prahlad Vadakkepat , Weng Khuen Ho , Tong Heng Lee

Predicting the Remaining Useful Lifetime (RUL) of machines is crucial for preventing tool wear and breakdown in Industry 4.0, but fully-supervised models struggle due to limited labeled data from broken machines. To address this, the authors propose a masked self-supervised learning method that uses unlabeled data for RUL prediction, showing superior performance on the C-MAPSS dataset compared to fully-supervised approaches. Read more

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Few-shot point cloud semantic segmentation via contrastive self-supervision and multi-resolution attention

Published in ICRA, 2023

Authors: Jiahui Wang, Haiyue Zhu , Haoren Guo , Abdullah Al Mamun , Clarence W De Silva , Tong Heng Lee

This paper introduces a few-shot point cloud semantic segmentation approach optimized for real-world applications. Unlike existing methods that rely on fully-supervised pretraining with large annotated datasets, which leads to biased feature extraction, the proposed method employs a contrastive self-supervision framework for pretraining. This approach uses class-agnostic contrastive supervision and a learnable augmentor to eliminate feature bias, while a multi-resolution attention module and center-concentrated multi-prototype enhance feature extraction and mitigate intra-class sparsity. Experiments demonstrate state-of-the-art performance, with a case study showcasing its effectiveness in practical CAM/CAD segmentation. Read more

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Lightweight Compressed Temporal and Compressed Spatial Attention with Augmentation Fusion in Remaining Useful Life Prediction

Published in IECON, 2023

Authors: Haoren Guo, Haiyue Zhu , Jiahui Wang , Vadakkepat Prahlad , Weng Khuen Ho , Clarence W de Silva , Tong Heng Lee

This paper proposes a non-transformer model, Compressed Temporal and Compressed Spatial (CTCS) Attention, for predicting Remaining Useful Lifetime (RUL), addressing concerns with transformer-based models such as high computational complexity and ineffective handling of low data. The CTCS model efficiently captures temporal and spatial information with pre- and post-positional encodings, while an Augmentation Fusion Module (AFM) improves understanding of data invariances. Evaluated on the C-MAPSS dataset, the proposed model outperforms transformer-based methods in accuracy while reducing computational cost by up to 32 times fewer FLOPs. Read more

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Few-Shot Point Cloud Semantic Segmentation for CAM/CAD via Feature Enhancement and Efficient Dual Attention

Published in IECON, 2023

Authors: Jiahui Wang, Haiyue Zhu , Haoren Guo , Abdullah Al Mamun , Clarence W De Silva , Tong Heng Lee

This paper presents a few-shot learning approach for 3D semantic segmentation in CAM/CAD workflows, aimed at improving the segmentation of novel classes in scenarios with high mixture but low volume. The method introduces Sequential Dual Attention (SDA) to capture both channel and spatial features, along with a non-parametric feature enhancement block to improve class recognition in the feature space. Compared to other few-shot models, the proposed approach achieves superior performance across multiple few-shot segmentation scenarios on two datasets, demonstrating significant improvements over baseline methods. Read more

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Remaining Useful Life Prediction via Frequency Emphasizing Mix-Up and Masked Reconstruction

Published in IEEE Transaction on Artificial Intelligence (TAI), 2024

Authors: Haoren Guo, Haiyue Zhu , Jiahui Wang , Vadakkepat Prahlad , Weng Khuen Ho , Clarence W de Silva , Tong Heng Lee

This paper addresses the challenge of predicting Remaining Useful Lifetime (RUL) in machinery within Industry 4.0 by proposing a framework that improves data utilization through frequency-domain analysis and semi-supervised learning. The Frequency Emphasizing Mix-Up Module (FEMM) enhances feature extraction, while the Masked Autoencoder Reconstruction Auxiliary Learning (MARAL) utilizes unlabeled data from unrestricted domains. Evaluations on the C-MAPSS dataset show the model outperforms others, achieving significant accuracy improvements, particularly with minimal labeled data. The results demonstrate a notable reduction in RMSE and overall prediction error. Read more

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