Abstract
The main purpose of this paper is to show to what extent data used in design optimization process can be used to provide innovation directions and inputs to TRIZ methods. When looking for a new design, it is common to first try to optimize existing systems by experimental and numerical means. This approach requires building a model linking on the one hand, a set of Action Parameters and their range of possible values; and on the other hand, Evaluation Parameters that allow measuring the quality of a solution. Next, to evaluate the best potential solutions, the concept of dominance can be used to define the Pareto frontier, somehow the limits of the performances that can be reached with the built model of system. When none of the dominant points satisfies the objectives, it means that a redesign of the system is required and directions towards this new design needs to be elicited. Our hypothesis in this paper is that some directions can be formulated out of the analysis of experimental or simulation data, either by interpreting the influence of each parameter towards the reaching of the objectives, which is the classical routine way to do, or by identifying systems of contradictions from the data and thus propose another way to overcome the Pareto frontier.
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Dubois, S., Chibane, H., De Guio, R., Rasovska, I. (2018). From Simulation to Contradictions, Different Ways to Formulate Innovation Directions. In: Koziołek, S., Chechurin, L., Collan, M. (eds) Advances and Impacts of the Theory of Inventive Problem Solving . Springer, Cham. https://doi.org/10.1007/978-3-319-96532-1_8
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