Set-based design of mechanical systems with design robustness integrated

This paper presents a method for parameter design of mechanical products based on a set-based approach. Set-based concurrent engineering emphasises on designing in a multi-stakeholder environment with concurrent involvement of the stakeholders in the design process. It also encourages flexibility in design through communication in terms of ranges instead of fixed point values and subsequent alternative solutions resulting from intersection of these ranges. These alternative solutions can then be refined and selected according to the designers’ preferences and clients’ needs. This paper presents a model and tools for integrated flexible design that take into account the manufacturing variations as well as the design objectives for finding inherently robust solutions using QCSP transformation through interval analysis. In order to demonstrate the approach, an example of design of rigid flange coupling with a variable number of bolts and a choice of bolts from ISO M standard has been resolved and demonstrated

Design space reduction in optimization using generative topographic mapping

Dimension reduction in design optimization is an extensively researched area. The need arises in design problems dealing with very high dimensions, which increase the computational burden of the design process because the sample space required for the design search varies exponentially with the dimensions. This work describes the application of a latent variable method called Generative Topographic Mapping (GTM) in dimension reduction of a data set by transformation into a low-dimensional latent space. The attraction it presents is that the variables are not removed, but only transformed and hence there is no risk of missing out on information relating to all the variables. The method has been tested on the Branin test function initially and then on an aircraft wing weight problem. Ongoing work involves finding a suitable update strategy for adding infill points to the trained GTM in order to converge to the global optimum effectively. Three update methods tested on GTM so far are discussed