Adaptive machine tool control method based on GA-BP neural network algorithm

A neural network algorithm, GA-BP technology, applied in the field of machine tool self-adaptive control based on GA-BP neural network algorithm, can solve the problems of difficulty in establishing fuzzy control theory, ineffective control of complex systems, and lack of systematicness, achieving the realization of The effect of self-adaptive real-time control, improving the level of intelligence and reducing technical requirements

A neural network algorithm, GA-BP technology, applied in the field of machine tool self-adaptive control based on GA-BP neural network algorithm, can solve the problems of difficulty in establishing fuzzy control theory, ineffective control of complex systems, and lack of systematicness, achieving the realization of The effect of self-adaptive real-time control, improving the level of intelligence and reducing technical requirements

CN110488754AActive Publication Date: 2019-11-22DALIAN UNIV OF TECH
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  • Adaptive machine tool control method based on GA-BP neural network algorithm
  • Adaptive machine tool control method based on GA-BP neural network algorithm
  • Adaptive machine tool control method based on GA-BP neural network algorithm

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Embodiment Construction

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings, taking a certain type of vertical milling machine as an example to describe the implementation of the present invention in detail.

[0051] The first step is to collect the machining state data with variable cutting parameters

[0052] Such as figure 1 As shown, the power sensor is arranged on the spindle motor (2), and its three coils are respectively set on the three power lines of the spindle motor UVW; the acceleration sensor is arranged at the lower end of the outer edge of the spindle (1), close to the tool side.

[0053] The variable parameter processing process is:

[0054] First choose a domestic milling cutter with a diameter of 10mm and a 45 steel workpiece with a length and width of 200mm and 100mm respectively, and then combine and match a total of 600 sets o...

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Abstract

The invention discloses a machine tool self-adaptive control method based on a GA-BP neural network algorithm, and belongs to the technical field of numerical control machining. The method comprises the steps that a spindle motor power signal and a spindle vibration signal in the workpiece numerical control machining process are monitored in real time, and the feeding speed and the spindle rotating speed are optimized in real time and adjusted adaptively based on the spindle power signal and the spindle vibration signal; and the whole signal acquisition process does not influence normal processing. The machine tool self-adaptive control method based on the neural network algorithm can be used to effectively improve the machining efficiency and the machining quality, prolong the service life of a cutter and a machine tool and reduce the cost.

Description

technical field [0001] The invention belongs to the technical field of numerical control processing, and in particular relates to a machine tool self-adaptive control method based on GA-BP neural network algorithm. Background technique [0002] The principle of adaptive control in NC machining process is to maximize the machining efficiency on the premise of ensuring the machining quality of parts, and realize the requirement of ensuring quantity. [0003] The traditional CNC machining method has already determined the machining parameters in the programming stage before the machining starts, and these machining parameters are often set by the experience of the operator, which is not the best or optimal cutting parameters, so it cannot be guaranteed to obtain the best cutting parameters. Excellent processing quality and processing efficiency; in the actual processing process, especially in the rough processing process, due to impact vibration, uneven machining allowance, too...

Claims

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Application Information

Patent Timeline
22 Nov 2019
Publication
CN110488754A
IPC
G05B19/408; G06N3/08
CPC
G05B19/408; G06N3/084; G06N3/086
Inventors
刘阔; 黄任杰