基于最小二乘支持向量回归的划片刀刃长控制优化

划片刀的刀刃长度影响划片刀的使用性能,而刀刃出露是控制刀刃长度的关键工序。在连续生产中一次腐蚀多片,刀刃长度存在波动。针对此问题,以子组划片刀刃长极差为响应,以溶液温度、溶液浓度、工件旋转速度为影响因子,选择正交试验设计方式获取试验点并得到样本集,再用最小二乘支持向量回归法建立模型,最后用粒子群算法对所建模型进行寻优,获得优化后的工艺参数。试验表明:该方法对降低划片刀生产中刃长波动性有显著效果,试验结果与建模寻优结果的划片刀刃长极差仅相差2.1μm。

Optimization of blade length control based on least squares support vector regression

The length of the dicing blade affects its performance, while the blade exposure is the key process to control the dicing blade’s length. In the continuous production, the dicing blade’s exposure fluctuates due to the corrosion of multiple blades at one time. To solve this problem, the extreme differences of the sub-set dicing blade’s length were taken as response, with solution temperature, solution concentration and workpiece rotation speed as influence factors. An orthogonal experimental design method was selected to get the test points and then a sample set. Then the least square support vector regression method was used to build a model. Finally, a particle swarm optimization algorithm was used to optimize the model and obtain the optimized process parameters. The experimental results show that this method is effective to reduce the dicing blade’s exposure fluctuation.The difference between the experimental results and the modeling results is only 2.1 μm.