Evaluation Method of CNC Machine's Green Degree Based on Clustering and ANFIS

Abstract

Abstract: To realize the intelligent evaluation and improve the prediction accuracy of CNC machine's green degree, an evaluation method of CNC machine's green degree was proposed based on clustering and ANFIS. Clustering algorithm based on FCM and improved particle swarm optimization were used to accomplish adaptive classification of samples and generate a training sample set for ANFIS learning. The fuzzy rules were automatically generated based on training results in order to eliminate effects of the fuzziness and randomness of the indicators. After training, the model might make inference about evaluation samples. Finally, with the experiments of the CNC machine's green degree evaluation, the effectiveness of the proposed method was demonstrated.

Key words: CNC machine, green degree, particle swarm, fuzzy C-means（FCM）, adaptive neural-fuzzy inference system（ANFIS）

摘要： 为了实现对数控机床绿色度的智能评价，提高数控机床绿色度预测精度，提出了一种基于聚类和自适应神经模糊推理系统（ANFIS）的评价方法。采用改进的粒子群优化模糊C均值聚类算法实现样本的自适应分类，生成辅助ANFIS学习的训练样本集；建立基于ANFIS的评价模型，通过对训练样本集的学习自动生成模糊规则，消除评价指标的模糊性和随机性对评价结果的影响；训练后的评价模型可用于评价样本的自适应推理。最后利用提出的评价方法对数控机床绿色度进行实例分析，验证了该评价方法的有效性。

关键词: 数控机床, 绿色度, 粒子群, 模糊C均值, 自适应神经模糊推理系统

CLC Number:

TH162

WANG Yugang1;IU Shichao2. Evaluation Method of CNC Machine's Green Degree Based on Clustering and ANFIS[J]. China Mechanical Engineering.

王宇钢1;修世超2. 基于聚类和自适应神经模糊推理系统的数控机床绿色度评价方法[J]. 中国机械工程.

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