A novel methodology for stability analysis of sandwich structures used in mechanical automation systems

This paper develops a new method for studying the stability of sandwich doubly-curved structures used in advanced mechanical automation systems. The structure analysed has a core made from a functionally graded graphene origami-enabled auxetic metamaterial (FG-GOEAM) with actuation and sensing face sheets to create an intelligent auxetic sandwich structure that can adapt dynamically to changes in load. To achieve this, a comprehensive coupled-field model based on integrating electromechanical constitutive relationships and first-order shear deformation theory (FSDT) that can represent how the system will react under nonlinear loading when operated automatically was created. Additionally, active control algorithms and controllers were also developed that enhance vibration suppression, increase stability margins, and optimize the real-time structural performance of the intelligent auxetic sandwich structure in an automated manufacturing environment. Numerical simulations explain how graphene dispersed within auxetic geometrical parameters (length, width, thickness), degree of curvature, and feedback control gains affect the dynamic stability of the suggested system. A deep neural network (DNN) is introduced for validating the mathematical results from datasets derived from the previous simulations. Comparison shows the suggested system has improved computational accuracy (by an order of magnitude), required fewer resources for construction (due to better structural adaptability), more effectively controlled process flows (due to enhanced intelligent controls), and increased operational reliability for future generations of automated systems. The suggested system has been designed to reduce computational complexity, provide support for predictive maintenance strategies, and efficiently implement scaling up to autonomous robotic platforms.