基于模糊信息粒化和优化支持向量机的氧化铝陶瓷超声磨削力趋势预测

为实现超声磨削氧化铝陶瓷中磨削力变化趋势的预测,提出了一种基于模糊信息粒化和支持向量机相结合的方法。首先进行氧化铝陶瓷超声磨削试验,然后利用模糊信息粒化方法对试验获得的磨削力进行粒化处理,并将人工免疫系统算法和粒子群算法进行并联混编构成人工免疫系统粒子群算法(AISPSO),接着建立非线性回归支持向量机预测模型并对模糊粒子进行预测,并通过AISPSO算法优化支持向量机预测模型,最后获得超声磨削氧化铝陶瓷中磨削力的变化趋势和变化范围。结果表明:该方法可以有效实现超声磨削中磨削力的变化趋势及变化范围预测,且预测未来5组数据变化范围的误差在10%以内,这为通过磨削力变化调整工艺参数以获得更好的加工表面提供了新的思路。

Trend Prediction of Ultrasonic Grinding Force of Alumina Ceramics Based on Fuzzy Information Granulation and Optimized Support Vector Machine

In order to predict the changing trend of grinding force in ultrasonic grinding of alumina ceramics,a method based on the combination of fuzzy information granulation and support vector machine was proposed.Firstly,the ultrasonic grinding test of alumina ceramics was carried out.The grinding force obtained by the experiment was granulated by fuzzy information granulation method.The artificial immune system algorithm and the particle swarm algorithm were mixed in parallel to form the artificial immune system particle swarm algorithm (AISPSO).Secondly,a nonlinear regression support vector machine prediction model was established and fuzzy particles were predicted.The support vector machine prediction model was optimized through the AISPSO algorithm.Finally,the change trend and range of grinding force in ultrasonic grinding of alumina ceramics were obtained.The results show that this method can effectively realize the prediction of the change trend and range in ultrasonic grinding.And the error of prediction of the change range of the next 5 sets of data are less than 10%.A new idea for adjusting the process parameters by changing the grinding force to obtain a better-processed surface is provided.