Turning force prediction method based on cutting-tool angle and cutting amount parametric variation

Abstract

The invention discloses a method for predicting a turning force based on cutter angle and parameter variation of cutting data. The method comprises the following steps: using a dynamometer to measure a cutting force distributed along a coordinate axis of a coordinate system in one-time turning, calculating a three-way turning force coefficient of the cutting force in a converted coordinate system through converting the coordinate system, and calculating and obtaining the predicted turning force according to the factor of the cutting force, so as to achieve prediction of a three-way turning force for processing work pieces made from the same material and having the same heat treating state under the same cutting and lubricating conditions of the cutters made from the same material at any cutter angle parameter and cutting data parameter. The method for predicting the turning force needs little workload for cutting experiments, and the obtained cutting force models have wider application application, have predicting precision meeting actual demand, and are convenient to popularize and apply.

Description

technical field [0001] The invention belongs to the technical field of mechanical manufacturing, and relates to a method for predicting turning force, in particular to a method for predicting turning force based on changes in tool angle and cutting parameters. Background technique [0002] In the cutting process of parts, the cutting force not only has a great influence on the wear and durability of the tool and the surface quality of the machined parts, but also affects the performance and cutting efficiency of the machine tool. Therefore, the estimation of cutting force is of great significance for tool sharpening, reasonable selection of cutting parameters and machine tool design. [0003] Turning force is affected by many factors, such as tool angle, tool material, cutting amount conditions, cutting fluid, material to be processed and heat treatment conditions, etc. Among these influencing factors, some factors can be expressed quantitatively, and some factors can only b...

AI Technical Summary

Problems solved by technology

For the neural network model, the effectiveness of the model also depends on the topology of the neural network, and the higher the versatility, the lower the efficiency of training the neural network model, and it takes a lot of training time

[0009] To sum up, the existing turning force prediction methods require a large number of cutting experiments to obtain a turning force prediction model used within a certain range; The greater the amount of cutting experiments, the more difficult it is to establish the prediction model

In addition, the existing turning force prediction model is mainly applicable to the situation where the cutting parameters change

As we all know, cutting experiments require a lot of manpower and material resources, especially for some workpieces made of precious and rare materials, it is not allowed to do a large number of cutting experiments to predict their cutting force, so it is almost difficult to achieve by conventional methods

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