姚姚zxd 发表于 2025-6-20 13:35:09

megafab 发表于 2025-6-20 11:26
若你测累计误差数据时,是根据执行机构的位置误差来判定的,那就不是测步进电机的精度了......

而且尽量不 ...

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

大概是这样的结构,往复运动1000次然后上面相机拍照看探针偏了多少像素
我觉得这样的结构测出的偏差也不能说明一定电机的问题,有可能是丝杆精度的问题

寰宇无人 发表于 2025-6-20 17:31:53

换向误差一般是丝杆的误差,
电机的误差往往是由于负载过大或速度过快导致的丢步

受不了了 发表于 2025-6-20 20:22:00

姚姚zxd 发表于 2025-6-20 13:08
公司让我拿便宜闭环和之前的开环比看能不能省钱,结果这个闭环误差比开环的误差还大,便宜没好货
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步进电机最最重要的是加速度,同样的电机有的人就是加速度更快最高速更高,以前的PLC控制步进这方面不行,这些年倒是进步大的
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查看完整版本: 步进电机在换向时为什么会出现累积误差