102,365 research outputs found
Integrated Computer-Aided Innovation: The PROSIT approach
No full textThe paper presents a methodology aimed at the improvement of the product development cycle through
the integration of Computer-Aided Innovation (CAI) with Optimization and PLM systems. The
interoperability of these tools is obtained through the adoption of Optimization systems as a bridging
element between CAI and PLM systems. This methodology was developed within the PROSIT project
(http://www.kaemart.it/prosit).
The paper describes the main issues related to the integration of these complementary instruments
and the solutions proposed by the authors. More specifically, the main idea of the PROSIT project to link
CAI and Optimization systems is the adoption of the latter tools not just to generate optimized solutions,
but also as a design analysis tool, capable to outline critical aspects of a mechanical component in terms
of conflicting design requirements or parameters. CAI systems are then applied to overcome the
contradictory requirements. The second step, i.e. the integration between Optimization and PLM
systems, has been obtained through the development of Knowledge-Based (KB) tools to support
designer’s activities. More in details, they provide means to analyze and extrapolate useful geometrical
information from the results provided by the optimizer, as well as semi-automatic modelling features for
some specific geometries. A detailed example related to the design of a plastic wheel for light motoscooters
clarifies the whole procedure. The paper integrates, extends and updates topics presented in
Cugini et al., Barbieri et al. and Cascini et al. [U. Cugini, G. Cascini, M. Ugolotti, Enhancing interoperability
in the design process—the PROSIT approach, in: Proceedings of the 2nd IFIP Working Conference on
Computer-Aided Innovation, Brighton (MI), USA, October 8–9, 2007, published on Trends in Computer-
Aided Innovation, Springer, ISBN 978-0-387-75455-0, pp. 189–200; L. Barbieri, F. Bruno, M.
Muzzupappa, U. Cugini, Design automation tools as a support for knowledge management in topology
optimization, in: Proceedings of the ASME 2008 International Design Engineering Technical Conferences
& Computers and Information in Engineering Conference (IDETC/CIE 2008), Brooklyn, New York, USA,
August 3–6, 2008; L. Barbieri, F. Bruno, M. Muzzupappa, U. Cugini, Guidelines for an efficient integration
of topological optimization tools in the product development process, in: Third International Conference
on Design Computing and Cognition, Atlanta, USA, June 23–25, 2008; G. Cascini, P. Rissone, F. Rotini,
From design optimization systems to geometrical contradictions, in: Proceedings of the 7th ETRIA TRIZ
Future Conference, Frankfurt, Germany, November 6–8, 2007]
TESTING DESIGN STIMULI FOR DESIGN-BY-ANALOGY ON A LARGE SET OF DESIGNERS
No full textThis paper presents evidence supporting the hypothesis that, for designers not specifically trained in designing-by-analogy, the sources of inspiration that share the same (sub-functions) and context of the target system lead to ideas having higher novelty and quality. The exploration of the design space gets positively affected as well. These evidence emerge after the statistical analysis of the results of an experiment that involved 84 graduate
students in Mechanical Engineering, with typical competencies on engineering design, but without any specific skill on analogy-based idea generation
Metodo per il monitoraggio della deformazione meccanica di una superficie e fascetta comprendente un elemento per monitorare la deformazione meccanica di detta fascetta
No full textDesign computing and cognition
No full textDesign Computing and Cognition has been through the years a recurring topic of AI EDAM special issues. A regular stream of articles is constituted by updated and extended versions of papers presented at the homonymous conference, the International Conference on Design Computing and Cognition (DCC). This special issue embeds a selection of suitably extended and updated papers that were presented at DCC’18 and are complemented by further contributions aimed to depict the latest research and some relevant trends in this domain. The growth of awareness about the fundamental importance of ‘designing’ changes in all dimensions of society is accompanied by the increased rigor of research in Design, along with the differentiation of research motivations and objectives. Similarly, in the computational domain, design research can be carried out by conjecturing design processes, constructing computational models of those processes, and then examining the behaviors of the resulting computational systems in simulated experiments
AN EXPERIMENT-DRIVEN MASS-PERSONALISATION MODEL: APPLICATION TO SAXOPHONE MOUTHPIECE PRODUCTION
No full textMass-personalization (MP) presents an opportunity to meet diversifying customer needs in consumer products market with a near mass-production efficiency. Traditional product development methodologies fall short to guide design for MP and a dedicated systematic methodology is essential. The proposed approach bases on a dynamic product template that automatically adapts with user input and produces a reliable output. This paper presents the workflow towards mass-personalization of saxophone mouthpieces with focus on design automation
Design for innovation - A methodology to engineer the innovation diffusion into the development process
No full textIn its hyper-inflated usage, innovation simply means ‘‘something new'', and is applied to any technical novelty. In its true meaning, innovating means designing something that will not only work under a technical point of view, but will also make business sense. ‘‘Design for Innovation'' means considering that design cannot simply focus on a narrow meaning of ‘‘product use'', because this could severely limit the diffusion of innovative products. The paper proposes an original model for representing what we call ‘‘beyond-use situations'' and the influences among the actors involved in the innovation diffusion process. Taking inspiration from social influence network models and from the Multi-issue Actor Strategy Analysis Model (MASAM), the paper presents an operational methodology to assess the influence of different actors on the decision to adopt a new product. In turn, such methodology should support design teams to conceive novel solutions more likely to become factual innovations. The paper also describes a computer-implementable technique, loosely derived from Quality Function Deployment, to practically apply the proposed methodology. An industrial case study from the medical-care sector illustrates its logic and operational step
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