Abstract: To enhance the efficiency and accuracy of the lower rocker arm optimization in vertical roller mills,an advanced optimization approach based on the Improved Northern Goshawk Optimization (INGO) algorithm and the BP Neural Network (BPNN) model was proposed. Initially,a finite element analysis of the lower rocker arm was performed.Subsequently,to overcome the issues of local extrema and slow convergence with the Northern Goshawk Optimization (NGO) algorithm,improvements were introduced through the Latin hypercube sampling method,the sine cosine algorithm,and Cauchy mutation for NGO. Following this,INGO-BP prediction models were constructed to capture the relationships between different structural parameters and the mass,maximum equivalent stress,and maximum deformation of the lower rocker arm.Ultimately,a mathematical optimization model for the lower rocker arm was established and solved,yielding a set of optimal design variables. Experimental results revealed that the mass reduction in the optimized lower rocker arm reached 16.4 %,while the maximum equivalent stress and maximum deformation remained within safe limits.In comparison to other algorithms,the INGO has the advantages of fast convergence and strong optimization ability. The INGO-BP model demonstrated high accuracy and stability in predicting the mechanical properties of the lower rocker arm,providing reference for applying optimization algorithms in structural optimization.

Key words: lower rocker arm, Improved Northern Goshawk Optimization(INGO), BP neural network, structural optimization