基于spa技术的高速切削加工.doc
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基于spa技术的高速切削加工,摘要随着全球经济的复苏，制造加工业的需求日益增加，而作为现代机械制造业主要生产方式之一的高速切削，具有成本低、切削效率高以及加工件表面质量好等优点，正广泛应用于多个领域。但目前高速切削加工技术仍存在诸多问题，例如：切削研究多是针对特定的工艺条件，研究成果的通用性较差；高速切削是个复杂的物理化学过程，而现有研究针对其物理...
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摘 要
随着全球经济的复苏，制造加工业的需求日益增加，而作为现代机械制造业主要生产方式之一的高速切削，具有成本低、切削效率高以及加工件表面质量好等优点，正广泛应用于多个领域。但目前高速切削加工技术仍存在诸多问题，例如：切削研究多是针对特定的工艺条件，研究成果的通用性较差；高速切削是个复杂的物理化学过程，而现有研究针对其物理化学机理的探索方面已到达瓶颈状态；控制加工质量和加工成本始终是高速切削研究的永恒目标，而以往的研究大多都浪费在了多次重复检查错误中，工作效率低下。针对高速切削加工的以上难题，本文主要研究内容如下：
（1）从实际需求出发设计可行的实验方案，提出了均匀设计和正交设计逐步数据采集方法，在获得实验数据的同时，尽量减少试验次数，有效的控制了实验成本。
（2）采用MATLAB的数学模型处理功能，设计了一种数据处理方案，用多种常用的数学模型同时对采集到的数据进行拟合、预测，通过设定适当的参数指标来比对所有模型的一致性，以获得最优模型，解决了以往研究中数学模型通用性差的问题。
（3）针对前阶段优化得到的最优加工工艺，将统计过程控制理论（SPC）运用到高速切削加工工艺的监测中，提出了一种高速切削加工在线监控的思路。首先分析控制图判断过程是否可控，再进行过程能力评价，最后利用控制图对切削过程进行监控，若有异常则作出快速判断，避免了实际生产中靠事后检查去发现问题的被动局面，使整个切削加工过程处于事先预防的状态。
（4）在上一阶段SPC控制的基础上，运用神经网络对SPC判断的异常情形进行故障诊断，使得统计过程调整（SPA）得以实现。对高速切削加工故障类型进行条件简化，选用合理的故障类型和系统输入特征，建立概率神经网络，并对数据进行训练、分类，使得复杂的切削故障类型得到较好的诊断。
（5）采用Matlab软件作为开发平台，利用其矩阵处理功能对实验数据进行处理，并完成SPC控制图的绘制，运用神经网络功能模块完成故障诊断的开发。
本课题通过理论分析、模型处理以及平台开发，对于高速切削加工有以下改进效果：有效解决了高速切削数据分析通用性差的难题；同时采用统计学数据处理避开了物理化学机理研究的不精确性；运用统计过程控制及神经网络故障诊断来预防切削加工事后的被动检查。
关键词：高速切削加工； 多数学模型拟合； 统计过程控制；神经网络故障诊断
Abstract
With the global economic recovery, the demand of manufacturing industries gradually increasing. High-speed cutting as one of the main mode of production in modern manufacturing industries, which has become an important trend. However, there are still many problems in High-speed cutting field. Firstly, researches on cutting are most focus on special process conditions, which lead to the conclusion of the research cannot be widely used. Secondly, High-speed cutting is a complex physical-chemical process, at the same time the research on the mechanism of physical-chemical has reached a bottleneck. Finally, control processing quality and costs is always the target of High-speed cutting, however, previous studies are main rely on check and recheck to find error, which is inefficient and uneconomic. Besides, during recheck, a lot of resources are wasted on defective products. In order to solve these problems, main research contents of this thesis are as follows:
(1) Feasible experimental program is designed based on the characteristic of issue. Obtained experimental data at the maximum, while minimizing the number of tests. A data acquisition experimental program based on uniform design and orthogonal design is proposed, which effectively control the experiment cost.
(2) Combine with Mathematical model processing function of Matlab, a data processing program is designed. Several Mathematical models processing, fitting and forecasting the collected data at the same time. In order to obtain the optimal model, compare the consistency of all the models by setting certain parameters indicators, which solve the mathematical model cannot be used widely in previous studies.
(3) Based on the optimal model obtained above, SPC theory is applied to high-speed machining process monitoring. The idea of online monitoring for high-speed machining process is proposed. Firstly, analysis control charts are used to determine whether the process can be controlled or not. Then conducts a process capability eva luation. Finally, control charts are used to monitoring cutting process, judgments will be made if there are exceptions. This method solve the problem mentioned above, and put the cutting process in a precaution status.
(4) Based on SPC control mentioned above, Neural Networks is used to diagnose the exceptions judged by SPC, which realized SPA. By simplifying fault type of high-speed machining process and choosing reasonable fault type and system input characteristics to build up probabilistic neural network to train and classify data, which well diagnose the complex fault type.
(5) Matlab software is chosen as the develop platform and its matrix processing function is used to processing experimental data, then SPC control charts are completed, and finally neural network mode is used to develop fault diagnosis.
Based on the research mentioned above, the problem that mathematical model cannot be used widely is effectively solved. At the same time, use statistical data proces..
随着全球经济的复苏，制造加工业的需求日益增加，而作为现代机械制造业主要生产方式之一的高速切削，具有成本低、切削效率高以及加工件表面质量好等优点，正广泛应用于多个领域。但目前高速切削加工技术仍存在诸多问题，例如：切削研究多是针对特定的工艺条件，研究成果的通用性较差；高速切削是个复杂的物理化学过程，而现有研究针对其物理化学机理的探索方面已到达瓶颈状态；控制加工质量和加工成本始终是高速切削研究的永恒目标，而以往的研究大多都浪费在了多次重复检查错误中，工作效率低下。针对高速切削加工的以上难题，本文主要研究内容如下：
（1）从实际需求出发设计可行的实验方案，提出了均匀设计和正交设计逐步数据采集方法，在获得实验数据的同时，尽量减少试验次数，有效的控制了实验成本。
（2）采用MATLAB的数学模型处理功能，设计了一种数据处理方案，用多种常用的数学模型同时对采集到的数据进行拟合、预测，通过设定适当的参数指标来比对所有模型的一致性，以获得最优模型，解决了以往研究中数学模型通用性差的问题。
（3）针对前阶段优化得到的最优加工工艺，将统计过程控制理论（SPC）运用到高速切削加工工艺的监测中，提出了一种高速切削加工在线监控的思路。首先分析控制图判断过程是否可控，再进行过程能力评价，最后利用控制图对切削过程进行监控，若有异常则作出快速判断，避免了实际生产中靠事后检查去发现问题的被动局面，使整个切削加工过程处于事先预防的状态。
（4）在上一阶段SPC控制的基础上，运用神经网络对SPC判断的异常情形进行故障诊断，使得统计过程调整（SPA）得以实现。对高速切削加工故障类型进行条件简化，选用合理的故障类型和系统输入特征，建立概率神经网络，并对数据进行训练、分类，使得复杂的切削故障类型得到较好的诊断。
（5）采用Matlab软件作为开发平台，利用其矩阵处理功能对实验数据进行处理，并完成SPC控制图的绘制，运用神经网络功能模块完成故障诊断的开发。
本课题通过理论分析、模型处理以及平台开发，对于高速切削加工有以下改进效果：有效解决了高速切削数据分析通用性差的难题；同时采用统计学数据处理避开了物理化学机理研究的不精确性；运用统计过程控制及神经网络故障诊断来预防切削加工事后的被动检查。
关键词：高速切削加工； 多数学模型拟合； 统计过程控制；神经网络故障诊断
Abstract
With the global economic recovery, the demand of manufacturing industries gradually increasing. High-speed cutting as one of the main mode of production in modern manufacturing industries, which has become an important trend. However, there are still many problems in High-speed cutting field. Firstly, researches on cutting are most focus on special process conditions, which lead to the conclusion of the research cannot be widely used. Secondly, High-speed cutting is a complex physical-chemical process, at the same time the research on the mechanism of physical-chemical has reached a bottleneck. Finally, control processing quality and costs is always the target of High-speed cutting, however, previous studies are main rely on check and recheck to find error, which is inefficient and uneconomic. Besides, during recheck, a lot of resources are wasted on defective products. In order to solve these problems, main research contents of this thesis are as follows:
(1) Feasible experimental program is designed based on the characteristic of issue. Obtained experimental data at the maximum, while minimizing the number of tests. A data acquisition experimental program based on uniform design and orthogonal design is proposed, which effectively control the experiment cost.
(2) Combine with Mathematical model processing function of Matlab, a data processing program is designed. Several Mathematical models processing, fitting and forecasting the collected data at the same time. In order to obtain the optimal model, compare the consistency of all the models by setting certain parameters indicators, which solve the mathematical model cannot be used widely in previous studies.
(3) Based on the optimal model obtained above, SPC theory is applied to high-speed machining process monitoring. The idea of online monitoring for high-speed machining process is proposed. Firstly, analysis control charts are used to determine whether the process can be controlled or not. Then conducts a process capability eva luation. Finally, control charts are used to monitoring cutting process, judgments will be made if there are exceptions. This method solve the problem mentioned above, and put the cutting process in a precaution status.
(4) Based on SPC control mentioned above, Neural Networks is used to diagnose the exceptions judged by SPC, which realized SPA. By simplifying fault type of high-speed machining process and choosing reasonable fault type and system input characteristics to build up probabilistic neural network to train and classify data, which well diagnose the complex fault type.
(5) Matlab software is chosen as the develop platform and its matrix processing function is used to processing experimental data, then SPC control charts are completed, and finally neural network mode is used to develop fault diagnosis.
Based on the research mentioned above, the problem that mathematical model cannot be used widely is effectively solved. At the same time, use statistical data proces..
