模具钢干铣削试验及参数优化研究

为了对铣削力做进一步的研究，以及预测铣削参数的改变对铣削力变化的影响，文章建立了铣削力预测模型，引 入了PSO优化算法。试验采用正交设计方法，干式铣削SKD61模具钢；KISTLER测力仪测量铣削力；HRsoft_DW数采软 件采集试验数据，并对数据进行极差分析。研究结果表明每齿进给量是铣削参数中影响铣削力最为主要的因素。研究 验证了PSO算法对铣削参数优化问题具有有效性。

Dry Milling Experiment of Die Steel and Parameter Optimization

ln order to do further research on the milling force and forecast the impact of changing milling parameters on milling force, the milling force prediction model was established, and the PSO algorithm was introduced. The experiment adopted orthogonal design method and milling SKD61 die steel by dry milling method. The milling force was measured by KISRLER dynamometer, collected and processed experiment data by HRsoft_DW data acquisition software. It is concluded that the feed rate per tooth is the most important factor to affect the milling force in milling parameters, and verified the effectiveness of particle swarm optimization ( PSO) algorithm for milling parameters optimization problem.