A new methodology in computer aided design to form find tensegrity systems
The following project is intended to frame and expand the theory beyond my first publication on the subject about software to design freeform tensegrity systems. [Tensegrity CAD: A new methodology in computer aided design to form find tensegrity system. International Symposium of the International Association for Shell and Spatial Structures (IASS) Shanghai, China November 8-12, 2010 Conference Proceedings nov 2010]. It has been a long postponed intent to complete such publication, but only now with a certain degree of closure, I can share some of the driving ideas I have been following and put into practice. With this essay and the given time – far from being quickly completed – I hope to reasonably complete the job . Unfortunately for those who have some knowledge about the subject, I restructured all the theory behind, looking for an answer to my specific task: design tensegrity system. Commonly known notions have been respected at the best, while others have been reformulated (and changing their meaning) more aligned with a more precise interpretation of the “natural principle” behind. To make it more difficult, a storical complex overlapping of disciplines – that over the years have been selectively developed and fuzzle a generally accepted vision on the subject- will likely puzzle more experts than newbies. I will try my best to offer missing notions, proposing a self contained body of work. Thanks for your patience.
Drafting new strategies and frontiers
Design topology and multi-objective optimization in form-finding of freeform tensegrity.
Dedicated to the work of my parents and to the dreams of my children. A self published essay of Alberto Campesato.
Numerical models based on dynamic relaxation and force density methods are widely used within the resolution of form finding problems. Alternatively other methodologies have been developed based on stochastic search methods (simulated annealing combined with dynamic relaxation) or genetic algorithms for design-topology problems. Nonetheless, limited results have addressed the jointed problem of design-topology and design-optimization, particularly in tensegrity systems design. We believe innovative resolutions of form-finding problems are central in designing and manufacturing future lightweight systems.
We argue that the lack of a stronger software toolset aiding the design of tensegrity has disrupted and cornered their use in the current market of lightweight structures. We draft foundations of a new approach in designing and manufacturing tensegrity systems, based on Optimization algorithms (PSO) nested into Particle Spring Model (PSM) and implemented in a tailored software toolset. The paper outlines an alternative approach and fundamental concepts employed to form-finding methods of multi-objective optimization problems. Comparatively, Evolutionary Strategies are discussed, with reference to the current understanding of biological systems and their implementation within our approach .
Aiming to design freeform tensegrity systems, a series of selected prototypes are presented in terms of output for direct fabrication and analysis, speculating on the applicability of both the approach and the system. Differently from other methodologies, improvements are relevant in terms of freeform choice, direct assembling, controlling and tuning of the final solution, tailoring the application to controlled actuated kinetic systems. Concluding, we address further works focusing in the development of automated advanced manufacturing processes to specific applications in lightweight robotics.
The methodology proposed facilitates the use of tensegrity systems in future market products, simplifying tedious procedures both in design and ultimately fabrication, opening new frontiers for intelligent structural skin.
References:  Zhang Y., “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications”, Mathematical Problems in Engineering, vol. 2015. Nikos Vlassis. “A Concise Introduction to Multiagent System and Distributed AI”, Intelligent Autonomous System Informatics, Institute University of Amsterdam, 2003.
©️ 2010-2020 Alberto Campesato