Computational optimization of a steel A-36 monolithic mechanism by bonobo algorithm and intelligent model for precision machining application

Monolithic mechanism is a potential mechanism for used in precision machining applications. However, it exhibits the limited problems of the resonant frequency and the capacity of load. This paper proposes a design optimization for a new monolithic mechanism. This mechanism is built in a symmetric configuration to generate a translation movement along the vertical axis. Basically, there is a complex relationship among the performances of the mechanism and its geometrical factors. Firstly, a mechanical model of the proposed MM was built, and the numerical data are collected through the finite element simulations. Next, the intelligent surrogate model based on the adaptive-network-based fuzzy inference system with three Genfis types is built to form the fitness function for both the resonant frequency and the force. The modeling results indicated that the Genfis2 and Genfis3 are the best modeler for the resonant frequency and the force, respectively. Lastly, the bonobo optimizer is utilized to maximize the resonant frequency and the force. For case study 1, the results found that the optimal frequency is found about 1013 Hz. For case study 2, the optimal force is found approximately 202.0154 N. Lastly, the errors between the predicted and verified values for two case studies are lower than 4%. The proposed method is a potential optimizer for other monolithic mechanisms and related engineering designs.