Machining chatter intelligent suppression method based on chatter frequency

Abstract

The invention discloses a machining chatter intelligent suppression method based on chatter frequency. In the machining process, chatters can be identified in real time in the chatter inoculation phase through a detection algorithm. When the chatters are identified, the main frequency of the chatters is accurately and rapidly estimated by utilizing a frequency estimation algorithm. The optimal values of the amplitude and the frequency of a variable spindle rotating speed chatter suppression algorithm are obtained according to the main frequency of the chatters. A variable spindle rotating speed chatter suppression instruction is transmitted to a machine tool according to the optimal amplitude and the optimal frequency so that rotating speed is enabled to cyclically change, and thus the chatters can be suppressed in the chatter inoculation phase. With application of the no-chatter machining method without reducing machining efficiency, damage of the chatters to the components of the machine tool and machined workpieces can be greatly reduced.

Description

technical field [0001] The invention relates to the field of mechanical processing, in particular to an intelligent suppression method for machining chatter vibration based on chatter frequency. Background technique [0002] Machining chatter is an unstable phenomenon that occurs in almost all cutting processes and manifests as severe vibration between the tool and the workpiece. The occurrence of chatter will affect production efficiency and processing quality, and it can also cause excessive noise, tool damage, etc. The harm to product quality, tool and machine tool equipment is beyond doubt. [0003] The ever-growing manufacturing industry puts forward higher requirements on processing efficiency, processing quality and processing cost. In order to minimize the adverse effects caused by chatter, chatter must be detected early in the incubation stage of chatter, and then Choose stable cutting parameters, or adopt advanced control strategies to avoid damage to workpieces a...

AI Technical Summary

Problems solved by technology

S-function transform and Hilbert-Huang transform are computationally intensive and cannot be applied to online flutter detection

The second category is the pattern recognition method, which mainly includes artificial neural network, support vector machine, case reasoning, fuzzy logic table, etc., but it needs to do a lot of experiments to train the model in the early stage

However, it is often difficult to determine the frequency and amplitude of the variable spindle speed. If the selection is not appropriate, the chatter suppression effect will not be achieved.

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