Automated Optimal Motion Sequence of a 9-Bar Linkage

Automated optimization-driven analyses may lead to an emersion of innovative design approaches, while investigating the potentials in design, analysis and control of high-performance engineering structures, and adaptive architecture. In the present study, an optimum motion sequence for the reconfiguration of a 9-bar linkage is succeeded through a robust automated optimization-driven approach of the metaheuristic algorithm, Pity Beetle Algorithm (PBA). The project explores the load-bearing behavior of the planar structure in different states of the “effective 4-bar” reconfiguration sequences considering its self-weight and a uniform distributed vertical load. Each motion step of the 9-bar linkage uses only one degree-of-freedom (DOF), in which four joints of the primary members are unlocked and the remaining joints are controlled by the brakes. The system is actuated by only one geared electrical motor detached from the structure and positioned on the ground. Different intermediate configurations of the 9-bar linkage are selected through the automated optimization-driven analysis, in such a way that all the joints are adjusted to the desired values and therefore to the target position. Hence, the objective of the optimization process focused at minimizing the brake torques in the locked joints of the structure through all the motion steps. The work-minimization problem has been solved by the metaheuristic algorithm, PBA, while the algorithm has the ability to search into large spaces for possible solutions regardless of the scale. Based on the aggregation behavior of the beetle Pityogenes chalcographus, the metaheuristic algorithm is able to find the global optimum solution among the plethora of possible solutions. The numerical studies of different possible motion sequences have been conducted with the software MATLAB and Simulink for a Model Based-Design. The obtained results demonstrate that the design of reconfigurable engineering structures and adaptive architecture can benefit from an automated optimization-driven analysis of the reconfiguration determination, in view of achieving improved performance and energy efficiency