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A METHOD FOR REDUCING FUZZINESS AND ACCELERATING NEW PRODUCT MODELLING IN CAD: THE CASE OF DESIGN FOR MANUFACTURING

Published online by Cambridge University Press:  19 June 2023

Jean-Bernard Bluntzer ,
Régis Barret  and
Egon Ostrosi
Jean-Bernard Bluntzer*
Affiliation:
Université de Technologie de Belfort-Montbéliard, France
Régis Barret
Affiliation:
Université de Technologie de Belfort-Montbéliard, France
Egon Ostrosi
Affiliation:
Université de Technologie de Belfort-Montbéliard, France
*
Bluntzer, Jean-Bernard, Université de Technologie de Belfort-Montbéliard, France, France, [email protected]

Abstract

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Improvements in product development can increase the competitiveness of firms. However, new product development in CAD systems involves difficulties and uncertainties that increase along with the pressure to develop the products. A distinct characteristic of CAD modeling for new product development is its uncertainty. This is because the information is usually approximate and incomplete during CAD modeling. Thus, the main objective of this paper is to propose a robust and flexible CAD approach to reduce uncertainty and accelerate new product modeling in the context of design for manufacturing. This methodology permits the convergence towards different product forms depending on the selected manufacturing process. Application of this approach has shown that when uncertainty is high, approving a complete CAD modeling results in a delay in product development. In contrast, CAD modeling using fuzzy models results in a gain of valuable development time because the model is completed when knowledge about manufacturing technologies, company fit and capabilities, and markets is available.

Keywords

Computer Aided Design (CAD)New product developmentDesign for X (DfX)Fuzzy modeling
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 263 - 272
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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