Comprehensive performance evaluation method for CNC machine tools based on improved pull-off grade method

Patent metadata

Field	Value
Publication number	US-10814448-B2
Application number	US-201816483256-A
Country	US
Kind code	B2
Filing date	Aug 28, 2018
Priority date	Aug 28, 2018
Publication date	Oct 27, 2020
Grant date	Oct 27, 2020

Abstract

A comprehensive performance evaluation method for the CNC machine tools based on an improved pull-off method belongs to the technical field of performance evaluation of CNC machine tools. A linear proportional method is used to standardize the performance index data of machine tool. The entropy weight method and mean variance method are used to determine the two objective weights of each level of indicator. Based on the principle of vector A comprehensive evaluation of three-level index is obtained from the linear weighted evaluation function. Finally, a similar method was used to calculate the comprehensive evaluation of a large system layer by layer. The present invention is used for the comprehensive performance evaluation of various CNC machine tools and also for a lateral comparison of specific performance of different machine tools, providing a scientific and possible evaluation method and process for the comprehensive performance evaluation of machine tools.

First claim

The invention claimed is:

1. A comprehensive performance evaluation for CNC machine tools based on improved pull-off grade method, wherein the steps are as follows:
(I) establishment of a key performance index system for CNC machine tools
the key performance indicators of CNC machine tools are summarized as four aspects: accuracy, efficiency, reliability, and energy consumption; effect of different factors on the comprehensive performance of CNC machine tools is quite different; types of indicators are divided, and subordinate relationships are analyzed; multilevel indicator sets are established, and an evaluation system is built step by step; the key performance indicators of CNC machine tools, the following classification is made:
a first level: accuracy, efficiency, reliability, and energy consumption of CNC machine tools;
a second level: three sublevel indicators of geometric error, thermal error, and composite motion error, and specific indicators of an other three first-level indicators;
when a machine tool works in a usual manner, an error forms and sizes in different axial directions and planes are different; therefore, a supplementary classification is as follows;
a third level: the second level of accuracy indicators are reflected in the errors of different axes and planes, including X-axis geometric error, perpendicularity error, X-feed axis thermal error, and X-Y plane roundness error;
a form and size of deviation of relative ideal position of the machine tool moving parts along different axes are different; therefore, the three-level index of geometric error can be classified as follows:
a fourth level: three movement errors and rotation angle errors of each axis and a perpendicularity error among the three axes;
an affiliation relationship between the evaluation indicators at all levels is determined; according to a general process of multilevel comprehensive evaluation, the key performance index system of CNC machine tools with four-level structure is finally determined;
(II) obtaining raw data for evaluation indicators
the machine tool performance data are obtained using a laser interferometer, ball bar apparatus, and other devices; n performance indexes of m CNC machine tools are evaluated, and the original data matrix of m×n order evaluation index is constructed;
(III) evaluation index pretreatment
first, all the indicators are unified into a maximal type, and linear proportional method m j , which can make a difference of evaluation values larger, a stability optimal, and retain an information about data variation to the maximum, is selected as a dimensionless method:

x
ij
*

=

x
ij

x
j
′

(
1
)

where x′ j is taken as a minimum value of j-th evaluation index in a sample to be evaluated;
(IV) order relationship analysis method to determine subjective weight
{circle around (1)} determine the order relationship

first, select the index with a highest importance of evaluation target in the original evaluation index set as x 1 and then continue to screen in the remaining m−1 indicators; based on this principle, an order relationship of evaluation indicator set is finally determined:
x 1 >x 2 > . . . >x m   (2)

{circle around (2)} evaluate an importance degree of adjacent indicators let a rational judgment of a ratio of importance degree between the adjacent evaluation indicators x k-1 and x k be

r
k

=

w

k
-
1

w
k

,

k
=
m

,

m
-
1

,
…
⁢

,
2

(
3
)

where values of r k are taken as 1.0, 1.2, 1.4, 1.6, and 1.8, respectively representing that an index x k-1 is equally important, slightly important, obviously important, strongly important, and extremely important than x k ;
{circle around (3)} weight coefficient calculation according to the evaluation of degree of importance, a subjective weighting coefficient of the index is obtained using the following formula:

w
m

=

(

1
+

∑

k
=
2

m

⁢

∏

i
=
k

m

⁢

r
i

)

-
1

(
4
)

(V) entropy method to determine an objective weight coefficient
{circle around (1)} normalize a standardized data

Assignees

• Univ Dalian Tech

Inventors

• Liu Kuo
• Liu Haibo
• Li Te
• Chu Heng
• Wang Yongqing
• Jia Zhenyuan

Classifications

• G05B19/4063 Primary

  Monitoring general control system (G05B19/4062 takes precedence) · CPC title

• B23Q17/0971 Primary

  by measuring mechanical vibrations of parts of the machine (arrangements for measuring vibrations B23Q17/12) · CPC title

• G05B19/4145

  characterised by using same processor to execute programmable controller and numerical controller function [CNC] and PC controlled NC [PCNC] · CPC title

• G05B2219/33099

  Computer numerical control [CNC]; Software control [SWC] · CPC title

• B23Q15/12

  Adaptive control, i.e. adjusting itself to have a performance which is optimum according to a preassigned criterion · CPC title