Study of geometrical defects of free-form surface machined using neural network

The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluatin...

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Autores principales:	Benattia Bloul, Hélène Chanal, Benaoumeur Aour, Nargess Chtioui
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/3281a1b5e15546efbf01f23e0a191bc2
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spelling	oai:doaj.org-article:3281a1b5e15546efbf01f23e0a191bc22021-12-02T03:33:34ZStudy of geometrical defects of free-form surface machined using neural network1687-814010.1177/16878140211060973https://doaj.org/article/3281a1b5e15546efbf01f23e0a191bc22021-11-01T00:00:00Zhttps://doi.org/10.1177/16878140211060973https://doaj.org/toc/1687-8140The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluating the defects of complex forms. The originality of the approach is the use of artificial intelligence to position the cloud of measured points, obtained with a three-dimensional measuring machine equipped with a contactless sensor, with regard to the 3D CAD model of the THA. The artificial intelligence algorithm used is based on neural networks that are trained using a virtual positioning realized with 3D CAD software. Finally, the difference between the positioned point cloud and the CAD model allows us to evaluate the shape defect of the measured THA surface. We found that the error of the proposed method is at the vicinity of micron scale.Benattia BloulHélène ChanalBenaoumeur AourNargess ChtiouiSAGE PublishingarticleMechanical engineering and machineryTJ1-1570ENAdvances in Mechanical Engineering, Vol 13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mechanical engineering and machinery TJ1-1570
spellingShingle	Mechanical engineering and machinery TJ1-1570 Benattia Bloul Hélène Chanal Benaoumeur Aour Nargess Chtioui Study of geometrical defects of free-form surface machined using neural network
description	The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluating the defects of complex forms. The originality of the approach is the use of artificial intelligence to position the cloud of measured points, obtained with a three-dimensional measuring machine equipped with a contactless sensor, with regard to the 3D CAD model of the THA. The artificial intelligence algorithm used is based on neural networks that are trained using a virtual positioning realized with 3D CAD software. Finally, the difference between the positioned point cloud and the CAD model allows us to evaluate the shape defect of the measured THA surface. We found that the error of the proposed method is at the vicinity of micron scale.
format	article
author	Benattia Bloul Hélène Chanal Benaoumeur Aour Nargess Chtioui
author_facet	Benattia Bloul Hélène Chanal Benaoumeur Aour Nargess Chtioui
author_sort	Benattia Bloul
title	Study of geometrical defects of free-form surface machined using neural network
title_short	Study of geometrical defects of free-form surface machined using neural network
title_full	Study of geometrical defects of free-form surface machined using neural network
title_fullStr	Study of geometrical defects of free-form surface machined using neural network
title_full_unstemmed	Study of geometrical defects of free-form surface machined using neural network
title_sort	study of geometrical defects of free-form surface machined using neural network
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/3281a1b5e15546efbf01f23e0a191bc2
work_keys_str_mv	AT benattiabloul studyofgeometricaldefectsoffreeformsurfacemachinedusingneuralnetwork AT helenechanal studyofgeometricaldefectsoffreeformsurfacemachinedusingneuralnetwork AT benaoumeuraour studyofgeometricaldefectsoffreeformsurfacemachinedusingneuralnetwork AT nargesschtioui studyofgeometricaldefectsoffreeformsurfacemachinedusingneuralnetwork
_version_	1718401721760219136

Study of geometrical defects of free-form surface machined using neural network

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