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Precision Measurements and Parametric Models of Vertebral Endplates

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Published on: September 17, 2019

Deep-Learning-Guided Point Cloud Modeling with Applications in Intelligent Manufacturing.

Guifeng Wang¹, Ning Shuigen², Jianzhang Xiao¹

¹Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province, Jinhua Polytechnic, Jinhua 321007, Zhejiang, China.

Applied Bionics and Biomechanics

|October 17, 2022

Summary

This study introduces a novel fuzzy learning method for automatic shape recognition using point cloud data, achieving over 90% accuracy for 36 machining features. The approach enhances robustness in CAD/CAPP/CAM integration.

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Area of Science:

Computer Science
Manufacturing Engineering
Artificial Intelligence

Background:

Machining feature recognition is crucial for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) systems.
Traditional methods often lack robustness in image reasoning for processing features.

Purpose of the Study:

To propose an automatic processing shape recognition method with enhanced robustness.
To leverage fuzzy learning on surrounding point cloud data for improved feature recognition.

Main Methods:

Utilized a framework originating from convolutional neural networks (CNNs) within a PointNet stage, employing Cloud Recurrent Neural Networks (RNNs).
Developed a method based on fuzzy learning of processing surrounding point cloud data.
Constructed a spot staining data sample library for training the feature recognizer.

Main Results:

Achieved robot-style notification of 36 processing shapes with a recognition accuracy rate exceeding 90%.
Demonstrated robustness against shape intersections and peripheral interference in point cloud data.

Conclusions:

The proposed fuzzy learning method offers a simple and efficient approach to automatic machining shape recognition.
The method shows usable robustness and confirmation performance, though not suitable for point cloud data with defects.