Abstract
Abstract
In the era of competitive digital innovation, product manufacturing companies need rapid customisation and ability to create uniqueness in new product development to stay competitive in the consumer market. Till recent times, this requirement heavily depended on CAD designer’s ability and experience to produce creative designs. This paper presents a novel feature-independent CAD design/modelling approach combined with a neural network that enables the creation of random parametric 3D CAD design variants using the Boundary Representation (B-Rep) method. This method is rapid and thus offers about 10 to 100 concept alternatives to the client in 10 to 30 minutes. Additionally, the paper also highlights the suitability of the proposed Neural Network method in creating 3D deep learning datasets to train generative design models like 3D GANs to further enhance 3D designs specifically targeted in product design problems.
Publisher
Research Square Platform LLC
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