小样本下基于比能值和ELM-AdaBoost的数控铣削能效优化模型构建方法OA

Aiming at the problems of high cost of energy efficiency data acquisition in CNC milling processes and low prediction accuracy of traditional CNC milling energy efficiency model under small sample data,an energy efficiency optimization model was proposed based on specific energy values and ex-treme learning machine(ELM)-adaptive enhancement algorithm(AdaBoost).The experimental data were obtained through orthogonal experimental design,a mechanism model was constructed based on specific en-ergy value,combined ELM and AdaBoost to form ELM-AdaBoost data model,and finally integrated the energy efficiency prediction model,which might guarantee the prediction accuracy while effectively reduce the model·s demands for data volume.The energy efficiency optimization models were established with the objectives of minimum specific energy value and minimum machining costs,and the optimal processing pa-rameters were solved and optimized by non-dominated sorting genetic algorithm Ⅱ and entropy weight-TOPSIS,and the machining experiments were conducted to verify the feasibility of the proposed method.