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Title: Smart automated computer-aided process planning (ACAPP) for rotational parts based on feature technology and STEP file
Author: Al-wswasi, Mazin Ghazi
ISNI:       0000 0004 7972 8642
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2019
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The concept of smart manufacturing comprises high levels of adaptability with rapid design changes, digital information technology, and more data training. This differs from traditional manufacturing, which depends on constant inputs for the generation of process planning to manufacture a part or requires human intervention if any of the input changes. Smart manufacturing has become a vital issue in the manufacturing industry since the start of the twenty-first century, in terms of time and production cost. One of the most effective concepts for achieving a smart manufacturing industry is the use of Computer-Aided Process Planning (CAPP) which is the key technology that connects Computer-Aided Design (CAD) and the Computer-Aided Manufacturing (CAM) processes. A lot of effort has been spent taking CAPP systems to the next upgraded level that is Automated Computer- Aided Process Planning (ACAPP) in order to provide complete information about the product, in a way that is automated, fast, and accurate. One of the most import aspects in creating an ACAPP system is the use of feature technology, as it is the first step in converting the design to manufacturing features. This includes in particular the development of efficient Automatic Feature Recognition (AFR) systems and solving features intersecting issues. The implementation of AFR techniques is an indispensable concept for transferring product data between CAD and ACAPP systems. Different international Product Data Exchange (PDE) standards, such as Drawing Exchange Format (DXF), Initial Graphic Exchange Specification (IGES), and Standard for the Exchange of Product (STEP) files are used to accomplish this purpose. Although many AFR techniques and systems have been developed to serve this aim, each of them has limitations. For example, each system is restricted to recognise a specific set of predefined manufacturing features; hence, if new features are included in the model design, they will not be recognised. In this work, a novel and smart interactive AFR (SI-AFR) system has been proposed for recognising features of rotational parts. A parser has been developed to extract the geometrical and topological information of a part design from a STEP file and to send it to the next steps. Then, the system manipulates the extracted information to facilitate the feature recognition process. During this progression, the system contributes to solving issues considered drawbacks in previous works, such as identifying the convexity and concavity of toroidal surfaces and efficiently isolating faces that belong to holes and internal shapes. Finally, the feature recognition process has been divided into two parts: recognition of predefined features and smart interactive feature recognition. This has been written using C# coding to extract the features' geometrical and topological information from the STEP file. Whilst the first part of the proposed system has the ability of recognising 54 predefined features, the main contribution of this research is concentrated in the second part of the system which allows new features to be detected, identified, and added to the predefined feature set. This is achieved by extracting the type and specification of each face, the geometrical and topological relation between each two adjacent faces, and the number of the faces that form the new feature. Due to its ability in identifying predefined and new features, it is believed that the system represents a new generation of feature recognition systems. Also, a "features subtraction" system has been created as an optimal solution for complex features intersecting cases. It takes the final manufacturing features from the SI-AFR system as an input. The system has seven steps for analysing, processing, and calculating intermediate features. The intermediate features represent layers of material to be removed, in an optimal sequence. These are recognised by scanning in all directions of the part, to determine the intersecting areas between the final manufacturing features. Such a system provides a whole vision of transferring a blank into the desired shape via step-by-step rough turning, drilling, and boring processes. The results from the SI-AFR and features subtraction systems depend on the geometrical and topological information of the pre-defined and new features. These are analysed for the purpose of automatically generating CAPP outputs, such as the process selection, cutting tools, sequence of operations, and generating G-code. This is to reduce the time and production cost, as well as human intervention, and hence significantly contributes to an organisations efforts in sustainability. The proposed ACAPP system has been practically validated, clearly demonstrating how it surpasses the capabilities of traditional CAM software, since all the outputs are achieved automatically, which CAM software are currently not capable of. The final manufacturing features of the part have been produced accurately, compared to the design features, in terms of specified design dimensions and tolerances. The current version of the system covers rotational symmetrical parts, however this work can be extended to include rotational non-symmetrical and prismatic parts.
Supervisor: Ivanov, Atanus ; Cheng, K. Sponsor: Ministry of Higher Education & Scientific Research, Republic of Iraq
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Smart manufacturing ; Automatic feature recognition ; Features subtractions