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Automatic identification of mechanical parts for robotic disassembly using the PointNet deep neural network

Automatic identification of mechanical parts for robotic disassembly using the PointNet deep... Identification is the first step towards the manipulation of parts for robotic disassembly and remanufacturing. PointNet is a recently developed deep neural network capable of identifying objects from 3D scenes (point clouds) irrespective of their position and orientation. PointNet was used to recognise 12 instances of components of turbochargers for automotive engines. These instances included different mechanical parts, as well as different models of the same part. Point clouds of partial views of the parts were created from CAD models using a purpose-developed depth-camera simulator, reproducing various levels of sensor imprecision. Experimental evidence indicated PointNet can be consistently trained to recognise with accuracy the objects. In the presence of sensor imprecision, the accuracy in the recall phase can be increased adding stochastic error to the training examples. Training 12 independent classifiers, one for each part, did not yield significant improvements in accuracy compared to using one classifier for all the parts. [Submitted 13 September 2019; Accepted 27 March 2020] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Manufacturing Research Inderscience Publishers

Automatic identification of mechanical parts for robotic disassembly using the PointNet deep neural network

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1750-0591
eISSN
1750-0605
DOI
10.1504/ijmr.2022.121591
Publisher site
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Abstract

Identification is the first step towards the manipulation of parts for robotic disassembly and remanufacturing. PointNet is a recently developed deep neural network capable of identifying objects from 3D scenes (point clouds) irrespective of their position and orientation. PointNet was used to recognise 12 instances of components of turbochargers for automotive engines. These instances included different mechanical parts, as well as different models of the same part. Point clouds of partial views of the parts were created from CAD models using a purpose-developed depth-camera simulator, reproducing various levels of sensor imprecision. Experimental evidence indicated PointNet can be consistently trained to recognise with accuracy the objects. In the presence of sensor imprecision, the accuracy in the recall phase can be increased adding stochastic error to the training examples. Training 12 independent classifiers, one for each part, did not yield significant improvements in accuracy compared to using one classifier for all the parts. [Submitted 13 September 2019; Accepted 27 March 2020]

Journal

International Journal of Manufacturing Research – Inderscience Publishers

Published: Jan 1, 2022

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