Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki

The important aspect of computer-aided process planning (CAPP) is to recognize part’s surfaces and features to aid downstream intelligent manufacturing. The automatic recognition of surfaces and features will lead to successful attainment of generative CAPP. Feature recognition works performed so fa...

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Main Author:	S Kataraki, Pramodkumar
Format:	Thesis
Language:	English
Published:	2019
Subjects:	T Technology TJ1-1570 Mechanical engineering and machinery
Online Access:	http://eprints.usm.my/46731/1/Development%20Of%20Generative%20Computer-Aided%20Process%20Planning%20For%20Cnc%20Milling%20Parts_Pramodkumar%20S%20Kataraki.pdf
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spelling	my-usm-ep.467312021-11-17T03:42:12Z Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki 2019-02-01 S Kataraki, Pramodkumar T Technology TJ1-1570 Mechanical engineering and machinery The important aspect of computer-aided process planning (CAPP) is to recognize part’s surfaces and features to aid downstream intelligent manufacturing. The automatic recognition of surfaces and features will lead to successful attainment of generative CAPP. Feature recognition works performed so far do not recognize all regular form and freeform volumetric features, and do not generate delta volume (DV) for the recognized features. The works do not address the classification of freeform volumetric features. So there is a need for novel classification of features and approach to auto-recognize features so as to auto-generate DV for each recognized feature for the attainment of generative CAPP. An effort has been made to novel classify the features into regular form and freeform features which are further sub-classified into surface features and volumetric features. The overall delta volume (ODV) is classified into SDVF, SDVT, SDVF filled region, SDV-VF, and SDVR. Algorithm is developed to auto-recognize surfaces of a milling part and auto-generate ODV. The algorithm auto-generates exploded view of ODV, auto-labels the sub-delta volumes (SDVs) and determines the level of complexity to manufacture a part. The generated ODV is validated by percentage error (%) and machining of parts. The algorithm selects the type of machining operation to be performed and auto-allocates each SDV-VF to the face it belongs to. The surface and volumetric features of a part are successfully auto-recognized and estimated DV, results table are auto-generated. The SDVT developed contiguous to SDVF for freeform faces, overcomes the complex DV for roughing process. The DV discontinuity and overlap limitation that occurred in few studies are eliminated. The designation of feature faces and colour coding of faces of SDV-VF expresses the type of feature present in a part. The validation of developed algorithm by percentage error (%) shows error less than 0.1% and the machine selection criteria suggests user the type of milling machine needed to manufacture a part based on level of complexity. 2019-02 Thesis http://eprints.usm.my/46731/ http://eprints.usm.my/46731/1/Development%20Of%20Generative%20Computer-Aided%20Process%20Planning%20For%20Cnc%20Milling%20Parts_Pramodkumar%20S%20Kataraki.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Mekanik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology TJ1-1570 Mechanical engineering and machinery
spellingShingle	T Technology TJ1-1570 Mechanical engineering and machinery S Kataraki, Pramodkumar Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
description	The important aspect of computer-aided process planning (CAPP) is to recognize part’s surfaces and features to aid downstream intelligent manufacturing. The automatic recognition of surfaces and features will lead to successful attainment of generative CAPP. Feature recognition works performed so far do not recognize all regular form and freeform volumetric features, and do not generate delta volume (DV) for the recognized features. The works do not address the classification of freeform volumetric features. So there is a need for novel classification of features and approach to auto-recognize features so as to auto-generate DV for each recognized feature for the attainment of generative CAPP. An effort has been made to novel classify the features into regular form and freeform features which are further sub-classified into surface features and volumetric features. The overall delta volume (ODV) is classified into SDVF, SDVT, SDVF filled region, SDV-VF, and SDVR. Algorithm is developed to auto-recognize surfaces of a milling part and auto-generate ODV. The algorithm auto-generates exploded view of ODV, auto-labels the sub-delta volumes (SDVs) and determines the level of complexity to manufacture a part. The generated ODV is validated by percentage error (%) and machining of parts. The algorithm selects the type of machining operation to be performed and auto-allocates each SDV-VF to the face it belongs to. The surface and volumetric features of a part are successfully auto-recognized and estimated DV, results table are auto-generated. The SDVT developed contiguous to SDVF for freeform faces, overcomes the complex DV for roughing process. The DV discontinuity and overlap limitation that occurred in few studies are eliminated. The designation of feature faces and colour coding of faces of SDV-VF expresses the type of feature present in a part. The validation of developed algorithm by percentage error (%) shows error less than 0.1% and the machine selection criteria suggests user the type of milling machine needed to manufacture a part based on level of complexity.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	S Kataraki, Pramodkumar
author_facet	S Kataraki, Pramodkumar
author_sort	S Kataraki, Pramodkumar
title	Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
title_short	Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
title_full	Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
title_fullStr	Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
title_full_unstemmed	Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki
title_sort	development of generative computer-aided process planning for cnc milling parts_pramodkumar s kataraki
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Mekanik
publishDate	2019
url	http://eprints.usm.my/46731/1/Development%20Of%20Generative%20Computer-Aided%20Process%20Planning%20For%20Cnc%20Milling%20Parts_Pramodkumar%20S%20Kataraki.pdf
_version_	1747821719187357696

Development Of Generative Computer-Aided Process Planning For Cnc Milling Parts_Pramodkumar S Kataraki

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