Continuing innovation is essential to bring ever better solutions to market, improving productivity, health and wealth. Mass production of consumer products (such as iPhones & white goods) and the development of complex systems (such as aircraft) have become common, with a corresponding need for better design and manufacturing systems to ensure continued innovation and improvement. The diversity and shear number of products has resulted in a corresponding range of design systems and frameworks allowing rapid and consistent product development. These include set based design, triz, axiomatic design, and generative systems all of which can enhance design innovation.

In large corporations, this is exemplified by the systems engineering process which has enabled the creation of some of the most sophisticated machines in human history. These systems processes allow control, predictability and traceability of decisions to help the engineering enterprise succeed in what can appear to be an impossible scenario. A key problem is that because these design processes are top down, so-called innovations are often merely perturbations on existing solutions. It is difficult to introduce, or take advantage of, new technologies and processes as they may require a product revolution, rather than evolution, to succeed.

One of the great challenges facing systems engineers is the control of the demon ‘emergent behaviour’, which is inherently unpredictable and can result in failure to meet requirements. The result is a reliable, consistent approach, but one which limits the very innovation needed, and which strives to avoid one of the best tools for innovation: emergence.

One way to avoid predicated solutions is to remove constraints and allow the design to emerge and grow to meet the requirements within a given environment. In the same way trees grow in response to stimuli, shape and size are not forced, rather they follow a set of elementary rules in deciding when a cell splits to grow, for example a root, branch or leaf.

Our technical challenges

Obtaining a working set of growth rules for component seeds to allow components to emerge from the activity.

Does the Mandelbrot effect arise where some order appears from blind actions?

Defining stimuli that will make the component seeds grow and establishing if that growth can be controlled via the stimuli.

Capturing emergent behaviour into a working set of parameters that can interact with existing design & manufacturing systems.

Is there a set of parameters which will define a CAD model?

Developing fast, scalable, event-triggered systems to enable real time creation of complex designs.