CN107390647A - A kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing - Google Patents
A kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 33
- 238000005259 measurement Methods 0.000 title claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 80
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 239000011159 matrix material Substances 0.000 claims abstract description 9
- 238000009826 distribution Methods 0.000 claims description 23
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000005315 distribution function Methods 0.000 claims description 4
- 238000003908 quality control method Methods 0.000 claims description 4
- 238000012790 confirmation Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012937 correction Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 claims description 2
- 238000003754 machining Methods 0.000 claims description 2
- 230000009897 systematic effect Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 4
- 238000012631 diagnostic technique Methods 0.000 abstract description 3
- 238000007619 statistical method Methods 0.000 abstract 1
- 238000011161 development Methods 0.000 description 4
- 238000003070 Statistical process control Methods 0.000 description 2
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
A kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing, collect the initial data of mass property in manufacturing process, carry out data prediction, on the basis of algorithm before, covariance matrix characterizes correlation between multivariate quality, the ratio between application specification amendment volume and process volume characterize procedure quality capacity factor, other applied statistical method characterizes procedure quality capacity factor, the method of summary two, obtain most suitable sign procedure quality capacity factor function, the data recorded according to control figure are sentenced steady and whether anomaly occur, find out process exception source.Process of the present invention capacity factor condition is rigorous, decision state is accurate, algorithm complex is low, the time of processing is fast, parameter processing is more rigorous, the correlation between multivariate quality is combined again, judges the factor, principal component analytical method by accident, then characterize the process capability function degree of accuracy more preferably to be lifted, also more tally with the actual situation, preferable basis has been established for subsequent manufacturing processes diagnostic techniques.
Description
Technical field
The present invention relates to Mechanical Product's Machining manufacturing process Quality Control Technology field, and in particular to a kind of measurement of mixing
The algorithm of manufacturing process multivariate quality ability.
Background technology
21 century, along with the development of global economic integration, the competition of international market, with time and cost
Equally, quality oneself turn into enterprise's survival and development main factors concerned.Extensively using domestic and international advanced quality method and matter
Amount technology is significant for Enhancing The Product Quality In Enterprises, raising product competitiveness.Good quality is inexpensive, efficient
Rate, low-loss, the guarantee of high yield are also to win customer loyalty for a long time, and enterprise obtains the foundation stone of sustainable development.In although
The nearest focus of state's business circles seems to concentrate on merger, capital management, the market expansion, diversification etc., but in fact, to appointing
The control of management, the production procedure of quality, it is the mostly important " interior of enterprise development for He Yijia manufactures enterprise
One of work("." internal strength " how is perfected, not only needs thought, the ways and means of quality management, with greater need for there is quality engineering skill
The support of art.How quality engineering technology is utilized, design and produce the production in inexpensive, short cycle, high quality, high reliability
Product, it is derived from striving advantage unexpectedly, the problem of oneself turns into domestic and international vast theoretical research person and working people extensive concern.And carry
One technical way of high quality is exactly to carry out effective process monitoring.Because product quality is important in modern industry
Status, statistical Process Control (SPC) achieve very ten-strike in machinery, weaving, electronic product, auto lamp discrete manufacturing business,
And gradually permeated to the industry of the interval such as papermaking, oil refining, chemical industry, food and continuous manufacturing industry.Because traditional process capability is analyzed
It is to be directed to unitary production process mostly, does not consider the situation of Multivariate Quality Characteristics in production process typically, and is produced in reality
During that multiple quality characteristic values are much be present, the analysis of traditional unitary process capability has been unsuitable for such more
First mass property production process, therefore, Multivariate Process Capability Indices how are measured and calculated, evaluated by single number polynary
Process capability, process capability analysis is carried out, and then predict that process production capacity has important theory significance and real value.Base
In the demand, the invention provides a kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing.
The content of the invention
For the problem of quality control aspect is present between traditional vehicle, the invention provides a kind of measurement manufacturing process of mixing
The algorithm of multivariate quality ability.
In order to solve the above problems, the present invention is achieved by the following technical solutions：
Step 1：The initial data of mass property in manufacturing process is collected, and necessary arrangement, simplification are carried out to the data
And calculate.
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process；
Step 3：The data observed recorded in oneself control figure through finishing control limit, according to sentencing steady rule judgment mistake
Whether journey there is anomaly；
Step 4：According to recognition result, process exception source place is found out；
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, manufacturing process is monitored if continuing with control figure without if.
Present invention has the advantages that：
1st, process capability coefficient condition is more rigorous, and decision state result is more accurate.
2nd, algorithm complex is low, and the time of processing is short, has obtained preferable result precision.
3rd, preferable basis has been established for subsequent manufacturing processes diagnostic techniques.
4th, the polynary characteristic between quality is considered, algorithm adaptability is stronger, more meets actual application.
5th, the more normative and reasonable of parameter factors processing, obtained value more meet the result of experience judgement.
6th, consider the erroneous judgement factor and combine principal component analytical method, the further lifting that result precision obtains again.
Brief description of the drawings
The structure flow chart of Fig. 1 manufacture process controls and diagnostic techniques
Fig. 2 workshop data acquisition scheme figures of the present invention
The specification region of Fig. 3 two-dimensional process amendments and actual distribution example region figure
Embodiment
In order to solve the problems, such as that quality control aspect is present between traditional vehicle, the present invention has been carried out in detail with reference to Fig. 1-Fig. 3
Illustrate, its specific implementation step is as follows：
Step 1：The initial data of mass property in manufacturing process is collected, and necessary arrangement, simplification are carried out to the data
And calculate.
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process, its specific calculating process is as follows：
In process of production, when Systematic Errors are not present in process, the quality characteristic value X of product meets normal distribution, X
∈ N (μ, σ2), wherein X is quality characteristic value, and μ is population mean, σ2It is population variance.When quality characteristic value Normal Distribution
When, its averageAlso Normal Distribution, wherein, n is sample size.According to the characteristic of normal distribution, then
P (σ of μ -3 σ ＜ X ＜ μ+3)=99.73%
That is, no matter what value μ and σ takes, and the probability that X falls between is 99.73%, that is to say, that is fallen in this distribution
Outside probability there was only 0.27%.
Due to influenceing the polynary characteristic of quality, that should be carried out polynary characteristic than reassigning；
Proportion is calculated as follows：
Assuming that t ties up normal distribution Nt(μ, ∑), i.e. Xt~Nt(μ, ∑), wherein μ are population mean vector, and ∑ is covariance
Matrix, due to ∑t×tFor symmetrical matrix, therefore symmetrical matrix P be present so that
Wherein λ1, λ2..., λtFor the characteristic value of covariance matrix, it meets (λ1, λ2..., λt) the polynary matter of ＞ 0, i.e. t dimension
The weight distribution of amount can be expressed as following formula：
Specification region for process amendment is a spheroid, and its volume calculation formula is：
Ui、LiThe bound of i-th yuan of quality factor respectively in control figure.
Complex process spheroid in actual distribution region under (1- α) confidence level is：
| Σ | it is the covariance determinant of the multivariate quality factor.
If its correction factor is k；
ε=[(M1-μ1)2+(M2-μ2)2+…+(Mt*μt)2]1/2
Mi、μiRespectively specification figure and the mean location of real process, ε are that t ties up average difference.
Another factor of influence is(Uj、Lj) be specification bound intersection point.
I.e.
In summary, it is as follows to characterize process capability function：
In order to improve the result of above formula, following method is integrated here, and detailed process is as follows：
The probability of misjudgement error is divided into two classes, first, slave mode is judged to runaway condition, probability is P1, second, shape out of control
State is judged to slave mode, and probability is P2。
Sample X, when in slave mode.If it is distributed as normal distribution X ∈ N (μ, σ2)；Process is in runaway condition
When, its distribution is changed, and the distribution function after change is F (x).
The upper and lower control limit for remembering control figure is respectively U, L；
P1=2 (1- Φ (λ))
P2=F (U)-F (L)
Overall error probability is P1+P2
Above formula Φ (λ) is value of the distribution function of standardized normal distribution at point λ, and λ is actual parameter in control figure, this
Concrete condition can be determined specifically.
Unitary characterizes process capability function CP：
CP=min (CPU, CPL)
Multivariate table sign process capability function MC 'P：
In summary, complete multivariate table sign process capability function WMC ' is obtainedP：
WMC ' herePThe C of unitary process can be analogized top, namely one more than 1 numerical value mean process relative to
The fluctuation of specification limit is smaller, means to fluctuate less than 1 larger.Equally, (1-k) measured Process Mean and desired value close to journey
Degree, larger (1-k) means Process Mean closer to desired value.
Step 3：The data observed recorded in oneself control figure through finishing control limit, according to sentencing steady rule judgment mistake
Whether journey there is anomaly, and it is described in detail below：
The control figure established when if process is in non-statistical controlled process state with sample point controls follow-up production
Journey, good control effect is not had not only, can bring the forecast of mistake to enterprise on the contrary, be caused damage to enterprise.
Step 4：According to recognition result, process exception source place is found out；
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation；
Step 6：After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has
It is abnormal, return and asked to (3) if having, manufacturing process is monitored if continuing with control figure without if.
Claims (2)
1. a kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing, the present invention relates to Mechanical Product's Machining to manufacture
Journey Quality Control Technology field, and in particular to a kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing, it is characterized in that,
Comprise the following steps：
Step 1：The initial data of mass property in manufacturing process is collected, and the data are carried out with necessary arrangement, simplifies and counts
Calculate
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process；
Step 3：The data observed recorded in oneself control figure through finishing control limit, be according to steady rule judgment process is sentenced
No anomaly occur, it is described in detail below：
The control figure established when if process is in non-statistical controlled process state with sample point controls follow-up production process, no
Good control effect is not only had, the forecast of mistake can be brought to enterprise on the contrary, is caused damage to enterprise
Step 4：According to recognition result, process exception source place is found out；
Step 5：Related personnel proposes and implemented improved measure for quality problems, solves process exception situation； steps 6：
After implementation is improved, dimension is continuous to carry out checking confirmation to procedure quality using control figure, and whether observation still has exception, return and ask if having
Extremely（3）, manufacturing process is monitored if continuing with control figure without if.
2. according to a kind of algorithm of the measurement manufacturing process multivariate quality ability of mixing described in claim 1, it is characterized in that,
Specific calculating process in step 2 described above is as follows：
Step 2：Process analysis procedure analysis is carried out to the Multivariate Quality Characteristics of critical process, its specific calculating process is as follows：
In process of production, when Systematic Errors are not present in process, the quality characteristic value of productMeet normal distribution,, whereinIt is quality characteristic value,It is population mean,It is population variance, when quality characteristic value takes
From during normal distribution, its averageAlso Normal Distribution, wherein, n is sample size, according to the characteristic of normal distribution, then
I.e., no matterWithWhat value is taken,The probability fallen between is, that is to say, that fall in this distribution model
Probability outside enclosing only has
Due to influenceing the polynary characteristic of quality, that should be carried out polynary characteristic than reassigning；
Proportion is calculated as follows：
Assuming thatTie up normal distribution, i.e.,, whereinIt is vectorial for population mean,For association side
Poor matrix, due toFor symmetrical matrix, therefore symmetrical matrix be presentSo that
WhereinFor the characteristic value of covariance matrix, it meets, i.e.,Dimension is more
The weight distribution of first quality can be expressed as following formula：
Specification region for process amendment is a spheroid, and its volume calculation formula is：
、Respectively in control figureThe bound of first quality factor
Complex process existsThe spheroid in actual distribution region is under confidence level：
For the covariance determinant of the multivariate quality factor
If its correction factor is；
、Respectively specification figure and the mean location of real process,Average difference is tieed up for t
Another factor of influence is,For the intersection point of specification bound
I.e.
In summary, it is as follows to characterize process capability function：
In order to improve the result of above formula, following method is integrated here, and detailed process is as follows：
The probability of misjudgement error is divided into two classes, first, slave mode is judged to runaway condition, probability is, second, runaway condition
Slave mode is judged to, probability is
Sample, when in slave mode, if it is distributed as normal distribution；Process is in shape out of control
During state, its distribution is changed, and the distribution function after change is
The upper and lower control of note control figure, which limits, is respectively 、；
Overall error probability is
Above formulaFor standardized normal distribution distribution function in pointThe value at place,For actual parameter in control figure, this
Concrete condition can be determined specifically
Unitary characterizes process capability function：
Multivariate table levies process capability function：
In summary, complete multivariate table sign process capability function is obtained：
HereUnitary process can be analogized to, namely one more than 1 numerical value mean process relative to
The fluctuation of specification limit is smaller, mean to fluctuate less than 1 it is larger, equally,Connecing for Process Mean and desired value is measured
Short range degree, it is largerMean Process Mean closer to desired value.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101639687A (en) * | 2009-09-08 | 2010-02-03 | 西安交通大学 | Integrated technology quality control system and realization method thereof |
| CN101794145A (en) * | 2009-01-29 | 2010-08-04 | 西门子公司 | Method and system for modeling a manufacturing process |
| WO2012120975A1 (en) * | 2011-03-07 | 2012-09-13 | ジヤトコ株式会社 | Torque converter |
| CN102830618A (en) * | 2012-08-16 | 2012-12-19 | 西北工业大学 | Quality-fluctuation prediction method for multi-procedure processing process |
| CN103268517A (en) * | 2013-04-23 | 2013-08-28 | 重庆科技学院 | Multivariate Quality Process Out-of-Control Signal Diagnosis Method Based on Support Vector Machine and Genetic Optimization |
| CN106054833A (en) * | 2016-06-08 | 2016-10-26 | 杭州威衡科技有限公司 | Quality control system and method in motor manufacturing process |
-
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- 2017-05-27 CN CN201710397927.7A patent/CN107390647A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101794145A (en) * | 2009-01-29 | 2010-08-04 | 西门子公司 | Method and system for modeling a manufacturing process |
| CN101794145B (en) * | 2009-01-29 | 2014-12-17 | 西门子公司 | Method and system for modeling a manufacturing process |
| CN101639687A (en) * | 2009-09-08 | 2010-02-03 | 西安交通大学 | Integrated technology quality control system and realization method thereof |
| CN101639687B (en) * | 2009-09-08 | 2012-03-28 | 西安交通大学 | An integrated process quality control system and its realization method |
| WO2012120975A1 (en) * | 2011-03-07 | 2012-09-13 | ジヤトコ株式会社 | Torque converter |
| CN102830618A (en) * | 2012-08-16 | 2012-12-19 | 西北工业大学 | Quality-fluctuation prediction method for multi-procedure processing process |
| CN103268517A (en) * | 2013-04-23 | 2013-08-28 | 重庆科技学院 | Multivariate Quality Process Out-of-Control Signal Diagnosis Method Based on Support Vector Machine and Genetic Optimization |
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Application publication date: 20171124 |