ModiYacht : intelligent CAD tool for parametric, generative, attributive and interactive modelling of yacht hull forms
Khan, Shahroz and Gunpinar, Erkan and Mert Dogan, Kemal and Sener, Bekir and Kaklis, Panagiotis (2022) ModiYacht : intelligent CAD tool for parametric, generative, attributive and interactive modelling of yacht hull forms. In: SNAME 14th International Marine Design Conference, 2022-06-26 - 2022-06-30. (https://doi.org/10.5957/imdc-2022-311)
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Abstract
This work proposes ModiYacht, a novel design tool for modelling optimised and customer-centred yacht hull forms. The proposed system incorporates parametric, generative, attribute-based and interactive design models. Parametric modelling uses high-level shape modifiers as quality operators to generate smooth and feasible designs. Because innovative and creative design is essential to a successful product, we also introduce an N-subpopulation-based teaching learning-based optimisation model that generates a variety of solutions to meet user design and performance requirements. Design attributes are developed using machine learning to shorten the communication gap between customers and designers. The generative designs can be used to provide interactive models for customers so that they can fully appreciate the hull-form's features and appearance prior to manufacturing. For this purpose, ModiYacht uses a design-space-shrinking technique during optimisation that guides the exploration process to focus the computational workload on user-preferred options.
ORCID iDs
Khan, Shahroz ORCID: https://orcid.org/0000-0003-0298-9089, Gunpinar, Erkan, Mert Dogan, Kemal, Sener, Bekir and Kaklis, Panagiotis;-
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Item type: Conference or Workshop Item(Paper) ID code: 82369 Dates: DateEvent26 June 2022Published15 April 2022AcceptedSubjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 16 Sep 2022 08:53 Last modified: 11 Nov 2024 17:07 URI: https://strathprints.strath.ac.uk/id/eprint/82369