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To reduce energy consumption and improve service stability and security, various design methods of cutterhead based on lightweight, modal optimization and life prediction were adopted. In the present work, a multi-disciplinary design optimization (MDO) methodology is presented and subsequently applied to the development of cutterhead. According to the requirements of the cutterhead process and the results of sensitivity analysis, the design parameters of each discipline were determined. To alleviate the computational cost caused by calling the results of finite element method (FEM) in design optimization, the Kriging model was employed to approximate the highly nonlinear implicit relationship between the mode frequency, fatigue life and the design parameters. Then, the system-level optimization model and the subsystem-level optimization model of mass, mode frequency and fatigue life were established based on collaborative optimization (CO). Finally, genetic algorithm (GA) was used for iterative optimization between the system-level and subsystem-level model to obtain the overall optimization results. The results showed that the mass of the cutterhead was finally reduced from 90.4 t to 88.5 t, the first-order modal frequency raised from 32 HZ to 37 HZ, and the fatigue life raised from 3.0E5 cycles to 5.5E5 cycles. [ABSTRACT FROM AUTHOR]

To balance the cost and accuracy in the optimization of EDM process parameters, the variable-fidelity surrogate model (VFM) was introduced in this paper to replace the implicit relationship between the EDM process parameters (peak current, duty cycle, pulse period) and performance functions (material remove rate, MRR, and surface roughness, Ra). Low-fidelity (LF) samples were obtained by using the low-cost EDM heat flow simulation, and high-fidelity (HF) samples were obtained by using the EDM machining experiments. By fusing the LF and HF samples, it is possible to establish an accurate expression of the implicit relationship between the EDM process parameters and performance functions in the variable-fidelity framework. Finally, the EDM process parameter model was established, and sequential quadratic programming (SQP) was used to obtain the optimal solution by calling the VFM. Through the verification experiments, the results showed that the parameter combination obtained by VFM is 15.1% higher than the MRR of the low-fidelity model (LFM) and 13.0% higher than the high-fidelity model (HFM), which reaches 88.285 mg/min, while the Ra has slightly decreased and is within the constraint range. From the above research, it can be concluded that the proposed technology has significant advantages and application potential in the field of EDM machining optimization. [ABSTRACT FROM AUTHOR]