基于支持向量机的蠕墨铸铁表面粗糙度预测

目的准确预测蠕墨铸铁加工过程中的表面质量,指导加工参数调整,保证加工过程中加工质量的稳定,运用差分进化算法优化的SVM模型(DE-SVM)构建蠕墨铸铁表面粗糙度(Ra)预测模型和加工参数选择方法。方法采用DE-SVM提高支持向量机回归模型的预测精度,建立针对实际加工材料的表面粗糙度预测模型,基于构建的预测模型,挖掘表面粗糙度与加工参数之间的关系,从而获得较优的加工参数。结果结合蠕墨铸铁的铣削加工实验数据,对比DE-SVM与常用优化算法(粒子群优化算法(PSO)和遗传算法(GA))优化的SVM模型,DE-SVM模型获得的MAPE(0.122)和R2(0.9559)值均优于粒子群和遗传算法优化的支持向量模型获得MAPE和R2值。在给定的加工参数范围内,切削速度和进给速度对表面粗糙度的影响较大,且表面粗糙度与切削速度成正比关系,与进给速度成反比,而切削深度对表面粗糙度影响不显著。结论由实验的对比结果可知,采用DE-SVM模型建立的蠕墨铸铁表面粗糙度模型具有更高的预测精度,基于DE-SVM获得的加工参数对表面粗糙度的影响,可有效指导加工参数的选择与调整,对保持蠕墨铸铁优良的加工质量具有较好的指导意义。

Prediction of Surface Roughness for Compacted Graphite Cast Iron Based on Support Vector Machine

The paper aims to accurately predict the surface quality of the compacted graphite cast iron during machining and effectively guide the adjustment of machining parameters to ensure stable machining quality, applies a support vector machine model based on differential evolution algorithm optimization (DE-SVM), so as to establish a prediction model of surface roughness of compacted graphite cast iron and a selection method of machining parameters. DE-SVM was used to improve the prediction accuracy of support vector machine regression model, and a prediction model of surface roughness (Ra) for specific machining materials was established. On this basis, the relationship between surface roughness and machining parameters was explored to obtain more suitable machining parameters. Combining the milling experiment data of compacted graphite cast iron, the comparison was carried out between DE-SVM and the SVM model optimized by the commonly used optimization algorithms (particle swarm optimization algorithm (PSO) and genetic algorithm (GA)). The values of MAPE (0.1221) and R2 (0.9559) obtained by DE-SVM model were superior to those of the support vector machine model optimized by particle swarm optimization algorithm and genetic algorithm. Within the given machining parameters, the cutting speed and the feed rate had a great influence on Ra, which was directly proportional to cutting speed and inversely proportional to feed rate; and the depth of cut had no significant effect on Ra. The experimental results indicate that the surface roughness model of compacted graphite cast iron based on DE-SVM model has higher prediction accuracy. The influence of machining parameters on surface roughness obtained by DE-SVM can effectively guide the selection and adjustment of machining parameters. It has good guiding significance for maintaining the excellent machining quality of compacted graphite cast iron.