Analyse of 3D geometrical STEP file for feature recognition

Abdullah, Adil (2024) Analyse of 3D geometrical STEP file for feature recognition. Masters thesis, Dublin, National College of Ireland.

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Abstract

The areas of manufacturing, engineering and computer-aided design (CAD) help determine accurate specifications based on 3D geometric data. This study presents a deep analysis of STEP files of 3D geometry with an emphasis on feature recognition. Complex 3D models are represented in STEP files, which is a serious problem. These data structures and geometric objects can be difficult to code due to their complexity and variety. Overview of CNN and other machine learning algorithms. STEP data uses traditional feature-based methods to identify and classify holes, grooves and pockets. In this case, features such as holes, grooves and pockets are identified and classified from the STEP data using traditional feature-based methods. CNN excels at recognizing features from voxel or mesh representations for analysis. Simple geometric shapes remain competitive in terms of computational efficiency and accuracy even when treated with traditional methods. This study highlights the tradeoff between accuracy, computational requirements and model interpretation.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Byrne, Brian UNSPECIFIED
Uncontrolled Keywords:	STEP File; 3D Geometrical Model; Feature recognition in intelligence; 3D CAD Modelling
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Manufacturing Industry
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	20 Jun 2025 11:23
Last Modified:	20 Jun 2025 11:23
URI:	https://norma.ncirl.ie/id/eprint/7974

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