Product conceptual design improvement model through reverse reasoning process: an integrated functional structure and bond graph model

Abstract

Functional structure modeling is a crucial stage in the conceptual design process that determines the innovation and feasibility of the final design. Previous functional modeling processes relied on designers’ experience and qualitative analysis, and the resulting functional-structural models were inherently subjective, ignoring the advantage of causal relationships between physical variables that could serve as paths for function-structure mapping, leading to unsustainable design improvements. To date, few models are able to construct functional structures using such involved physical variables required for simulation modelling. To fill these gaps, a conceptual design improvement model is proposed through reverse reasoning: an integrated functional structure and bond graph (BG) model. First, a functional structure for fusing physical variables is constructed using the mapping rule between BG elements and physical variables, which can support the construction of polychromatic sets of variables. Second, causalities among variables are obtained by dimensional analysis, improving the polychromatic causal directed graph (PCDG). Third, using PCDG as the basis of reverse analysis, the target variables are determined to reverse-find the key variables on PCDG and screen out the more valuable variables to improve the schemes that fit the variable change requirements. Finally, a tree climbing trimmer machine case study is presented to demonstrate the proposed model’s effectiveness, and the new design’s performance is verified using AMESim software.