关于不平衡计算的MALAB程序
当下可以用于工程计算的软件很多,目前我接触的是MATLAB,不知这款软件用于工程计算是否得当,相对与现在的许多计算类软件,这款需要自己编程,甚至与自己建模,同比与其它的分析软件效率要慢许多,可是在了解整个的分析模型上,要比其它的似乎更清晰一些。闲来无事,把机械设计手册的关于不平衡的例题编辑了一个程序,很简单的一个东西,现在和大家分享,也想听听关于各类计算软件中,对于一些模型分析的事例。F:\Mtlab 大侠,程序要如何打开
弄个ui界面更好 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
你的结果?
你的程序没有输入变量,那有什么用?
另外平衡等级也是要输入的变量,你不可能全用一个标准。 修改后
matlab这软件几乎无所不能,是科研人员必备利器。不会matlab的研究生不算合格的研究生。 好熟悉的界面,好久没摸了
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