基于神经网络的机床质量分类与评估方法研究

Research on classification and evaluation method of machine tools′quality based on neural network

提出了一种基于神经网络的直接分析法。以加工中心为研究对象,通过大数据收集国内外产品的主轴功率、主轴最大转速、定位精度、重复定位精度及快速移动速度等主要性能参数数据。利用Python框架下的神经网络模型探究各参数对机床质量与性能的影响权重。同时利用神经网络的分类、预测功能提出了机床质量分类与评估方法,从而解决了机床质量难以合理量化的问题。分析结果表明,该方法可以分析出影响机床质量的关键因素,同时也可以较好地对机床质量进行初步分类,对机床性能的评估具有指导意义。

A more direct analysis method based on neural network is proposed.Taking the machining centers as the research objects,the main performance parameters such as the spindle power,maximum rotating speed,positioning accuracy,repeat positioning accuracy,and fast moving speed of domestic and foreign products are collected through big data.Using the neural network model under Python framework to explore the weight of the influence of each parameter on the quality and performance of the machine tools.At the same time,a classification and evaluation method of machine tools'quality is proposed by using the classification and prediction functions of neural networks,thereby solving the problem that it is difficult to reasonably quantify the quality of machine tools.The analysis results show that this method can analyze the key factors that affect the quality of machine tools,and can also perform a preliminary classification of machine tool quality,which has guiding significance for the evaluation of machine tool performance.