CN113094827A - QFD decomposition and RPN value expansion based product manufacturing reliability root cause identification method - Google Patents

QFD decomposition and RPN value expansion based product manufacturing reliability root cause identification method Download PDF

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CN113094827A
CN113094827A CN202110355384.9A CN202110355384A CN113094827A CN 113094827 A CN113094827 A CN 113094827A CN 202110355384 A CN202110355384 A CN 202110355384A CN 113094827 A CN113094827 A CN 113094827A
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characteristic
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equipment
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CN113094827B (en
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何益海
张安琪
解宇轩
张吉山
杨秀珍
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Beihang University
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Abstract

The invention provides a method for identifying a product manufacturing reliability degradation root cause based on QFD decomposition and RPN value expansion, which comprises the following specific steps: firstly, determining and decomposing key reliability characteristics of a product; secondly, extracting the part characteristics of the product; thirdly, mapping and decomposing the characteristics of the product parts to a production line; fourthly, acquiring the characteristics of the process units on the production line and eliminating the redundant characteristics; calculating the importance of the characteristics to the reliability characteristics of the product as a first risk factor; sixthly, calculating the probability of the characteristic deviation as a second risk factor; seventhly, calculating the undetectable degree of characteristic deviation under the current control condition to serve as a third risk factor; calculating a characteristic integration RPN value so as to determine root causes causing the degradation of the manufacturing reliability of the product; the method makes up the defect that the traditional method ignores the interaction mechanism of the manufacturing process and the product reliability, and has important reference value for guiding the control with pertinence and emphasis on the production process.

Description

QFD decomposition and RPN value expansion based product manufacturing reliability root cause identification method
Technical Field
The invention provides a method for identifying a product manufacturing reliability degradation root cause based on QFD decomposition and RPN value expansion, belonging to the field of reliability.
Background
With the rapid development of various industries and the influence of global integration of economy on national economy, the manufacturing level becomes an important mark for measuring the comprehensive national strength of a country and is also an important embodiment of the international competitiveness of the country. With the advent of the intelligence and big data era, the manufacturing industry of China has been developed unprecedentedly, and the development of the manufacturing industry promotes the production of a series of products with complex structures and integrated functions. However, uncertain factors such as variable production environments, complex manufacturing processes and the like are endless, the manufacturing process needs to ensure the reliability of the product under such a complex and variable dynamic background to face a great challenge, and the reliability of the manufactured product often cannot reach the design reliability target. Therefore, identifying the root cause of product reliability degradation during manufacturing has become an urgent issue for the current manufacturing industry. In the past, the research on the cause of the product reliability degradation is focused on product fault diagnosis, the product fault data fitting and the physical structure modeling are considered, the interaction among the manufacturing process, the manufacturing system and the product reliability is ignored, and the product reliability degradation cannot be prevented fundamentally.
In the manufacturing process of the product, raw materials enter a workshop in the manufacturing process of the product, and an operator changes parameters such as the shape, the size, the surface characteristics and the like of the raw materials according to a certain processing mode and a certain technological procedure to finally form a finished product meeting the quality requirement. However, the increasing complexity of the product complicates the manufacturing process, the processing route, the process schedule, the assembly process, and the layout of the workshop, and secondly, the complexity of the manufacturing process is increased by the operator, the processing environment, and other factors. Product reliability originates at the design stage, forming at the manufacturing stage. The reliability of the manufacturing process has a direct impact on the reliability of the output product. In order to meet the reliability requirement of the product in the design stage, i.e. to make the reliability of the product output in the manufacturing stage as close to the design requirement as possible, the manufacturing process becomes the focus of the manufacturer as the key stage of the product reliability formation. However, in the past, in the research on the process reliability in the manufacturing process, the optimization of the process parameters is realized by focusing on the control of the equipment performance state and the key process in the manufacturing system, and the research on the manufacturing reliability guarantee developed by taking the product as the core is mostly neglected from the viewpoint of the equipment and the process. From the perspective of system engineering, there are inseparable interaction relationships among manufacturing process equipment, manufacturing processes, and output products. Therefore, how to directly identify the cause of product reliability degradation in the manufacturing process for the product has become a significant engineering problem facing the manufacturing field and the reliability field.
However, in the existing research, when the product is frequently failed in the initial use stage and the reliability is reduced, the existing research mostly depends on the accurate physical structure of the product, and considering that the product is the direct output of the manufacturing process, the variable in the manufacturing process is an important factor influencing the reliability of the product, and the existing research is still lack of further research on how to establish a relation model between the reliability of the product and the variable in the manufacturing process by using big data in the manufacturing process. In addition, the manufacturing process guarantee is an effective way for improving the reliability of products, but most of the existing researches are started from equipment and processes, the interaction between a manufacturing system and the reliability of the products is ignored, and the manufacturing process variables influencing the reliability of the products are not directly identified for the products in a targeted manner. In view of this phenomenon, the patent develops a product manufacturing reliability root cause identification method based on Quality Function Deployment (QFD) and extended Risk Priority (RPN). Based on the operation process of the manufacturing system, the product manufacturing reliability connotation is provided, and the degradation mechanism of the product manufacturing reliability is combed. Further, by means of waterfall decomposition of QFD, a root cause association tree of product manufacturing reliability degradation is constructed from top to bottom by taking a product as a core from the perspective of system engineering, and therefore an initial set of root causes is determined. Integrating a risk idea, comprehensively analyzing the risk of the variables in the initial root cause set to the product reliability based on the expanded RPN value, comprehensively evaluating the importance of the variables, and finally determining the root cause causing the product manufacturing reliability degradation. The method is suitable for identifying the root cause influencing the reliability of the product in the manufacturing process, and can provide reference for the reliability control of a manufacturer in the production process.
Disclosure of Invention
(1) The purpose of the invention is as follows:
in order to solve the problem that the characteristics influencing the product reliability in the process of identifying the manufacturing are not directly developed by taking a product as a core in the existing research, the invention provides a systematic method for identifying the root cause of product manufacturing reliability degradation, namely a method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and RPN value expansion. On the basis of the clear product manufacturing reliability connotation, a product manufacturing reliability degradation mechanism is described. And taking the early failure rate of the product as a top event, and decomposing the product characteristics into part characteristics, production lines and process parameters layer by layer from top to bottom, thereby constructing a correlation tree of root causes and determining an initial set of the root causes. And on the basis of three risk factors of the importance degree of the characteristics to the reliability characteristics of the product, the probability of characteristic deviation and the undetectable degree of the characteristic deviation, the RPN value of each characteristic is calculated based on the hesitation fuzzy theory, and the characteristic with smaller risk consequence in the initial set of root causes is eliminated, so that the root causes which cause the reliability degradation of the product manufacturing are determined. The object of identifying the root cause that causes the degradation of the reliability of the manufacture of the product is achieved.
(2) The technical scheme is as follows:
the basic assumptions made by the present invention are as follows:
the manufacturing system is in an ideal state, and the influence of human factors on equipment is not considered when the production equipment runs;
assume 2 that equipment within a manufacturing system experiences a series of discretized states ranging from normal operation, defective operation, to complete failure; the processing capacity of the apparatus is divided into Em＝[e1,e2,e3…eM], wherein ,e1 and eMRespectively representing a normal operation state and a complete failure state of the equipment;
suppose 3 that the probability of any processing capacity state occurring in the operation process of the equipment is Pm＝[qp1,qp2,qp3…qpM]Wherein qp1 and qpMRespectively representing the probability of the occurrence of an ideal state and the probability of the occurrence of complete failure of the equipment, wherein p is a constant; any processing capacity states are independent of each other,
Figure BDA0003003518920000031
namely the probability sum of the occurrence of each processing capacity state of the equipment is 1;
suppose 4, when the equipment processing capacity state is m ═ x, the equipment can not complete the specified production task, and the probability of the state is
Figure BDA0003003518920000032
Assuming that 5, the environmental factor in the manufacturing system is the objective characteristic, the acceptable capability index is a, and when the actual capability index is lower than a, a certain measure should be taken to control the environmental factor;
based on the hypothesis, the invention provides a product manufacturing reliability degradation root cause identification method based on QFD decomposition and an extended RPN value, which comprises the following steps:
step 1, determining and decomposing key reliability characteristics of products;
step 2, extracting the part characteristics of the product;
step 3, mapping and decomposing the characteristics of the product parts to a production line;
step 4, acquiring the characteristics of the process units on the production line and eliminating redundant characteristics;
step 5, calculating the Importance of the characteristics to the reliability characteristics of the product as a first risk factor (I);
step 6, calculating the Probability of the deviation of the characteristic as a second risk factor (P);
step 7, calculating the undetectable degree of characteristic deviation under the current control condition as a third risk factor (D);
step 8, calculating a characteristic integration RPN value so as to determine a root cause causing the degradation of the manufacturing reliability of the product;
wherein, the "determining and decomposing the key reliability characteristics of the product" in step 1 means that the mechanism leading to the degradation of the manufacturing reliability of the product is clarified according to the interaction mechanism among the reliability of the manufacturing system, the quality of the manufacturing process and the manufacturing reliability of the product, as shown in fig. 1; further, according to the fault form of the product, the product-level characteristics are decomposed layer by layer to obtain a degraded part; the specific method comprises the following steps: the early failure of the product is taken as the top event of the associated tree, the characteristics of the product at the component level, the component level and the part level are sequentially decomposed according to the failure mode of the product at the initial use stage and the physical structure of the product, the inspection data related to the product, the component and the part in the production process are collected, and the degraded part is finally determined.
Wherein, the step 2 of extracting the part characteristics of the product refers to that after the degraded part is obtained, the characteristics related to the part need to be extracted; for a certain product, a series of interdependent parts are the basic units that make up the product; parts are usually composed of different families, and there are some common characteristics among parts with similar functions; for each series, common features may be machined from functionally similar production lines; in addition to common characteristics, certain parts also have specific characteristics; in a word, the part characteristic library after classification and extraction consists of common characteristics and specific characteristics; the specific method comprises the following steps: based on the identified degraded parts, according to the space structure of the manufactured parts and the manufacturing process plan, the characteristics of the parts finished on the same production line are divided into a common characteristic library, and the characteristics specific to a certain part are divided into a specific characteristic library, so that the extraction and classification of the characteristic library of the parts are realized.
The step 3 of mapping and decomposing the product part characteristics to the production line means that a production engineer decomposes the part characteristics to the corresponding production line according to a production plan; the establishment of the part characteristic library is beneficial to more planned and systematic production line decomposition; at this time, identifying a production line for which a deviation has occurred in relation to the degraded part characteristics; the specific method comprises the following steps: the production line related to the characteristic processing of the part is determined by a craftsman according to the planning of the part manufacturing process, a process flow chart, a technical document and the like, and data from the corresponding production line is collected, so that the production line with the deviation is determined.
Wherein, the step 4 of obtaining the characteristics of the process units on the production line and removing the redundancy characteristics refers to obtaining a group of control characteristics of the production line from mutual influence on the process units according to the process composition, and on the basis, using Last Absolute Secret and Selection Operator (LASSO) to regress the redundancy characteristics, thereby obtaining an initial set of root causes; the specific method comprises the following steps: searching process information of each process unit and data for controlling key nodes in the production process by referring to an enterprise resource planning system, and collecting process characteristic data on a production line, wherein in the process, the collected index data is ensured to be comprehensive and complete as much as possible; on the basis, a correlation equation between the product reliability characteristic and the process characteristic is constructed by using LASSO regression, so that the collinear process characteristic (hereinafter referred to as the characteristic) is eliminated, and a group of mutually independent characteristic sets, namely a root cause initial set, is obtained; to this end, the step 1-4 establishes a root cause correlation tree of the product manufacturing reliability degradation by using the waterfall decomposition of the QFD, and obtains mutually independent root cause initial sets, as shown in fig. 3.
Wherein the "calculation characteristics" described in step 5 are specific to product reliabilityImportance of nature "means that the contribution of the characteristics to the reliability characteristics of the product is considered as the result of the deviation of the characteristics, and the LASSO regression coefficient in step 4 is used as a first risk factor; the method comprises the following specific steps: using the regression equation of the correlation relationship established in step 4, the dependent regression coefficient can measure the correlation between the independent variable and the dependent variable, and the regression coefficient
Figure BDA0003003518920000051
For characterizing the importance of the process characteristics to the reliability characteristics of the product, wherein p represents the number of sample data, n is the number of characteristics, yiFor the ith product reliability characteristic, xijFor the jth process characteristic related to the ith product reliability characteristic, the regression coefficient is adjusted
Figure BDA0003003518920000052
The normalization results in the importance of the property, i.e. the first risk factor I.
The "calculating the probability of the characteristic deviation" in step 6 refers to the probability of the characteristic deviation within the time t as a second risk factor; the probability that a characteristic does not deviate within time t is considered in this patent to be that equipment does not fail and a production environment factor is at a normal level during this time, and therefore the probability that a characteristic deviates is
Figure BDA0003003518920000061
Determining the probability of no failure of the equipment based on the probability distribution of the processing capacity state of the equipment in the production process, and determining the probability of keeping the environmental factor at a normal level based on the capacity index of the environmental factor in the manufacturing system;
the method comprises the following specific steps: according to processing equipment related to process characteristics, fault data of the equipment in the production process and the amount of finished processing tasks are collected, environmental factors influencing the process characteristics of the manufacturing system are collected for production personnel, and process specification requirements related to the environmental factors and data collected in the actual processing process are collected;
first, the calculation is made over time tProbability that the device is not faulty; the probability distribution of the occurrence of any processing capacity state of the equipment in the time t is Pm＝[qp1,qp2,qp3…qpM]When the processing capacity state of the equipment is m ═ x, the equipment cannot complete a given production task; in conjunction with the unavailability of the equipment, on the one hand, the unavailability of the equipment may be expressed as a ratio of a loss of production capacity to an ideal state of production capacity; on the other hand, the unavailability of the equipment can be considered as the ratio of the time that the machine cannot work normally to the working time in an ideal state; unavailability of a device is expressed as
Figure BDA0003003518920000062
wherein ,
Figure BDA0003003518920000063
for the number of equipment failures in time t, γ is the down time due to the equipment failure, n and γ' represent the number of overhauls and the down time at that time, respectively, exRepresenting the state of the processing capacity of the plant, e1Is the ideal operating state of the apparatus, emRepresenting a complete failure state of the device; thus, the probability that the device will not fail is
Figure BDA0003003518920000064
Secondly, in the production process, the fluctuation of the environmental factors exceeding the normal level can directly cause the deviation of the characteristics; capability index of environmental factor
Figure BDA0003003518920000065
wherein T＝TU-TL，Δ＝|μ-M|，TU and TLRespectively representing specified upper and lower limits, mu and M are actual and standard distribution central values; collecting environmental factor data in the production process to obtain a group of environmental factor capability index sets Sc＝{Cpe1,Cpe2,…Cpen}; thus, the probability that the environmental factor remains at a normal level is
Figure BDA0003003518920000071
wherein ncSatisfies C for the ability indexpenNumber of times > a, NcIs the total number of samples;
at this point, the probability that the equipment does not fail within the time t and the probability that the environmental factor is kept at a normal level are calculated, and finally, P is calculatedvAnd is the second risk factor P.
The step 7 of calculating the undetectable degree of characteristic deviation under the current control condition refers to the capability of detecting characteristic deviation under the current control condition, an Average running chain length (ARL) is used to represent the undetectable degree of characteristic deviation, and a ratio of an actual ARL to an acceptable ARL is used
Figure BDA0003003518920000072
As a third risk factor; the method comprises the following steps: collecting characteristic state data acquired by sensor in production process, and determining characteristic state transition probability matrix P by using Markov matrix to obtain ARL value ARL ═ Si(I-P)-1E, where P is the transition probability matrix, Si＝[0,…,1,…0]1×rIn the form of an initial probability matrix, the probability matrix,
Figure BDA0003003518920000073
further, the production engineer determines the acceptable ARL of each characteristic in the actual production process0Thereby calculating DuAnd a third risk factor D.
The "calculating characteristics integrating the RPN value to determine the root cause of the product manufacturing reliability degradation" in step 8 refers to the degree of preference X ═ X of the combination experts for the three risk factorsI,XP,XDIntegrating three risk factors to calculate an RPN value of each characteristic; the method comprises the following specific steps: using the hesitation fuzzy theory, experts express the degree of preference for three risk factors using hesitation fuzzy semantics, e.g., { c10}, wherein,
Figure BDA0003003518920000074
then, the fuzzy semantics are transformed to obtain a hesitant fuzzy binary semantic decision matrix
Figure BDA0003003518920000075
wherein
Figure BDA0003003518920000076
Figure BDA0003003518920000077
The comprehensive evaluation value of the expert on the solution set X is expressed as
Figure BDA0003003518920000078
Wherein I ═ I, P, D,
Figure BDA0003003518920000079
is a rounding function, then
Figure BDA00030035189200000710
α＝x-cround(x)(ii) a Combined expert weight S ═ S1,s2,…slThe preference values of the experts for the three risk factors }
Figure BDA00030035189200000711
wherein ,
Figure BDA0003003518920000081
t*＝M(∑j≤lsσ(j))-M(∑j＜lsσ(j))，M(x)＝∑j≤n-1tj+tj(lx-(n-1))；sσ(j)represents the jth maximum, M is a piecewise function
Figure BDA0003003518920000082
W (x) preference weights for three risk factors, t*A weight operator after t correction; at the moment, the preference degrees of the experts on the three risk factors are calculated; for each property, { I, P, D } is considered by this patent to be the respective propertyApplying the hesitation fuzzy theory again to the attribute set with different weight coefficients, and using the equation in the step 8, so that the risk RPN value of each characteristic is calculated, and the characteristic with smaller RPN value in the root cause initial set is eliminated; finally, a set of prioritized root cause sets that cause product manufacturing reliability degradation is identified, which provides a reference for the producer to exercise targeted and focused control over parameters in the process;
through the steps, the method for identifying the root cause of the product manufacturing reliability degradation based on the QFD decomposition and the RPN expansion is provided, the problem that the traditional method ignores the interaction mechanism between the manufacturing process and the product and the analysis of the product manufacturing reliability degradation mechanism is incomplete is solved, and the method has guiding significance for pertinently identifying the cause of the product reliability degradation output in the manufacturing process and improving the product reliability, and provides targeted and emphatic reference for a producer to carry out process control in the manufacturing process, so that the product reliability is effectively ensured to further meet the design reliability target of the product and the user requirements.
(3) The invention relates to a product manufacturing reliability degradation root cause identification method based on QFD decomposition and RPN value expansion, which comprises the following steps:
determining and decomposing key reliability characteristics of a product; the method comprises the following specific steps: decomposing layer by layer to determine degraded parts according to the physical structure of the product by taking the fault form of the product as a top event of the association tree decomposition;
extracting the part characteristics of the product; the method comprises the following specific steps: collecting and classifying and extracting the relevant characteristics of the degraded parts to obtain the common characteristics and the specific characteristics of the parts;
step (3) mapping and decomposing the product part characteristics to a production line; the method comprises the following specific steps: decomposing the part characteristics to corresponding production lines according to the production plan, and identifying the production lines related to the degraded part characteristics;
step (4) acquiring the characteristics of the process units on the production line and eliminating redundant characteristics; the method comprises the following specific steps: collecting relevant process parameter characteristics on a production line, and eliminating redundant characteristics based on LASSO regression to obtain a group of mutually independent root cause initial sets; therefore, a product manufacturing reliability degradation root cause association tree framework is constructed;
step (5) calculating the importance of the characteristics to the reliability characteristics of the product; the method comprises the following specific steps: collecting related data of root causes and product reliability characteristics in the production process, constructing a correlation equation of the root causes and the product reliability characteristics based on LASSO regression, and obtaining regression coefficients of all characteristics
Figure BDA0003003518920000091
Namely the first risk factor I;
step (6) calculating the probability of characteristic deviation; the method comprises the following specific steps: collecting relevant data of the equipment in the production process, analyzing the fault mode of the equipment, and counting to obtain the probability distribution P of the processing capacity state of the equipmentm＝[qp1,qp2,qp3…qpM]Calculating the probability P that the device does not fail during the production processEi(ii) a Collecting relevant data of environmental factor monitoring in the production process, and calculating environmental factor capability index CpeObtaining the probability P that the environmental factor is kept at a normal levelC(ii) a Then, the probability P of the characteristic deviation in time t is calculatedv＝1-PE×PCI.e. the second risk factor P;
step (7) calculating the undetectable degree of characteristic deviation under the current control condition; the method comprises the following specific steps: collecting related data of root causes under the current control condition, and constructing a Markov probability transition matrix
Figure BDA0003003518920000092
pijP { S ═ jh | S ═ ih }, where P isijIs the probability of a certain state of a property occurring,
Figure BDA0003003518920000093
is the width of the division interval, H is the upper limit height of the control chart, 2r-1 is the number of the divided intervals;
Figure BDA0003003518920000094
in the formula ：XiMu is a control chart parameter for actual in-process condition observations, and then the ratio D of the actual average run chain length ARL to the acceptable ARL is calculateduI.e. a third risk factor D;
step (8) calculating a characteristic integration RPN value so as to determine a root cause causing the degradation of the manufacturing reliability of the product; the method comprises the following specific steps: and (3) calculating the preference degrees X of the experts to the three risks by applying the hesitation fuzzy theory and combining the respective weights of the expertsI,XP,XDAnd integrating three risk factors of importance I of the characteristics, probability P of deviation of the characteristics and undetectable degree D of the characteristics, calculating to obtain an RPN (R ═ f (I, P and D)) of the characteristics in the initial root cause set, and removing the characteristics with smaller risk values, thereby obtaining a group of root cause sets with risk priority.
(4) The advantages and the effects are as follows:
the invention relates to a method for identifying a product manufacturing reliability degradation root cause based on QFD decomposition and an extended RPN value, which has the advantages that:
i. the invention considers the mutual relation among the reliability of a manufacturing system, the quality of a manufacturing process and the reliability of a product from the perspective of system engineering, and describes the degradation mechanism of the manufacturing reliability of the product;
in the invention, QFD is decomposed into carriers, and a product manufacturing reliability root cause correlation tree consisting of product reliability characteristics, part characteristics, production lines and process characteristics is systematically constructed from top to bottom;
the risk idea is integrated, three risk factors of the importance of the characteristics, the probability of deviation of the characteristics and the undetectable degree of the deviation of the characteristics are expanded, RPN values of all root causes are obtained, and the root causes which cause the reliability degradation of product manufacturing are determined according to the risk priority sequence;
the identification method is scientific and objective, has good manufacturability and has wide application value for manufacturers.
Drawings
Fig. 1 is a product manufacturing reliability degradation mechanism.
Fig. 2 is a flow chart of the method of the present invention.
FIG. 3 is a QFD decomposition-based product manufacturing reliability degradation root cause correlation tree.
FIG. 4 is a correlation tree for camshaft manufacturing reliability degradation root cause identification
The symbols in the figures are as follows:
5M1E refers to human (Man), Machine (Machine), Material (Material), Method (Method), Measurement (Measurement), Environment (Environment) within the manufacturing System
CRCs are key reliability features of products (CRCs)
PCs are product processing features (PCs)
I is the importance of the Process characteristics on the reliability characteristics of the product (inportant, I)
P is the Probability of process characteristic deviation (P)
D is an undetectable measure of process characteristic deviation (Detectability, D)
SD1 is the product grade property code for the camshaft; SD1.1-1.4 are camshaft component level property codes;
SD1.1.1-1.3.1 is the part grade characteristic code of camshaft
ED1-ED2 are common characteristic numbers for camshaft parts SD1.1.1-SD1.3.1; SD1-SD2 are camshaft parts
SD1.1.1-SD1.3.1 specific characteristic code
PD1 is the general code of the camshaft hole surface processing production line; PD1.1-PD1.4 is the cam shaft hole surface processing process flow code; PD1.1.1-PD1.4.1 is the equipment number related to the surface processing of camshaft hole
PC1-PC7 is the process characteristic code of camshaft hole surface processing
QFD-Quality Function Development (QFD)
RPN-priority of Risk (RPN)
LASSO-LASSO (LASSO) Least absolute shrinkage and selection operator regression
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples.
The invention relates to a method for identifying a product manufacturing reliability degradation root cause based on QFD decomposition and an extended RPN value, which is shown in figure 2and comprises the following steps
Step 1, determining and decomposing key reliability characteristics of camshafts of a certain batch of engines and collecting related data; according to the manufacturing process of the camshaft, based on the product manufacturing reliability degradation mechanism, as shown in fig. 1, it can be known that the root cause of the failure of the camshaft occurring at the early stage of use is due to the deviation of the process characteristics within the manufacturing process; therefore, the faults of the batch of camshafts are taken as a top event, and are divided into journal supporting, axial positioning, driving and auxiliary devices according to the physical structure of the camshafts; further, the layer-by-layer decomposition yields part-level spindle, cam and gear journal characteristics, as shown in the structural group of FIG. 4; collecting related data in the production process, and determining degraded parts;
and 2, collecting related characteristic data of the batch of early failed camshaft main shafts, cams and gear journals. Classifying and extracting characteristic data on the parts; then, determining that the surface quality of the shaft hole of the camshaft has deviation based on the camshaft machining precision requirements collected in the table 1;
TABLE 1 camshaft machining accuracy requirements (options)
Figure BDA0003003518920000111
Figure BDA0003003518920000121
Step 3, collecting production lines and related equipment related to the camshaft hole processing technology; the detailed process flow and equipment are shown in the production line group in fig. 4;
step 4, according to the camshaft processing production line and the related equipment, obtainingA set of Process Characteristics (PC) derived from interactions on a process unit is provided1，PC2，PC3，PC4，PC5，PC6，PC7Collecting relevant process characteristic data by using a sensor and an enterprise production resource planning data system, ensuring the integrity and accuracy of the data as much as possible, and constructing a relevant relation between the process characteristic data and the surface roughness of the camshaft by using LASSO regression; eliminating redundant process characteristics to form a group of independent root cause initial sets { PC }1，PC2，PC5，PC6，PC7Finishing an initial set association tree of reliability degradation root causes of the camshaft shown in fig. 4 on the basis of the root cause construction frame shown in fig. 3, namely 'acquiring process unit characteristics on a production line and removing redundant characteristics';
and 5, calculating the importance of the process characteristics to the surface roughness of the camshaft. Obtaining regression coefficients of all process characteristics based on the LASSO regression results in the step 4, and obtaining first risk factors I of all the characteristics after standardization;
Y＝0.286PC1+0.119PC2-0.092PC5+0.5113PC6+0.078PC7
I1＝0.317
I2＝0.132
I5＝0.102
I6＝0.567
I7＝0.086
step 6, calculating the probability of deviation of the process characteristics; collecting relevant data of production equipment in the camshaft hole processing process; firstly, analyzing the processing capacity states and the probability distribution of each device, and obtaining the proportional relation between the occurrence probabilities of the processing capacity states of the devices by analyzing various fault modes and statistical data of the fault modes; the processing of the camshaft hole relates to four devices; according to the production loss caused by the downtime of different failure modes, the process capability states of the equipment can be divided into five grades, and the statistical result of the failure data of each equipment is shown in table 2;
TABLE 2 Equipment Process capability status and Fault data
Figure BDA0003003518920000122
Figure BDA0003003518920000131
Based on the above statistical data of the equipment failure, the following can be obtained:
Figure BDA0003003518920000132
Figure BDA0003003518920000133
Figure BDA0003003518920000141
Figure BDA0003003518920000142
in the formula ：
Figure BDA0003003518920000143
the number of device failures, γ, of the devices PD1.1, PD1.2, PD1.3, PD1.4 within the time tiI-1, 2,3,4 is the down time due to failure of the devices PD1.1, PD1.2, PD1.3, PD1.4, n and γiThe' i ═ 1,2,3,4 represent the number of overhauls of the four pieces of equipment and the down time at that time, respectively, as shown in table 2;
the processing time of the camshaft in the batch is t equal to 10 days; therefore, the failure rate of each device is as follows:
PEKC1＝PE3.1×PE4.1＝0.951×0.965＝0.918
PEKC2＝0.9968
PEKC5＝0.9972
PEKC6＝PE3.2×PE4.2＝0.924×0.931＝0.860
PEKC7＝0.9982
collecting environmental noise factors affecting process characteristics as shown in Table 3, collecting environmental factor data every 0.5h, and determining the environmental factor capability status S as an ideal status when the capability index is satisfiedc＝{Cpe1,Cpe2,…CpenIs calculated and filled in table 3;
TABLE 3 environmental factor deviation data
Figure BDA0003003518920000144
The probability that the comprehensive equipment does not have faults and the probability that the environmental factors keep normal level are integrated, and the probability that each process characteristic has deviation is as follows:
Figure BDA0003003518920000145
step 7, calculating the undetectable degree of the deviation of each characteristic; according to the collected PC state data, the discrete states of each characteristic are divided into E0, E1, E2, E3 and E4; when the absorption state E4 is reached, the characteristics are deviated while determining the standard mean and the control limit H of each characteristic, thereby obtaining a state transition probability matrix of each characteristic;
Figure BDA0003003518920000151
Figure BDA0003003518920000152
Figure BDA0003003518920000153
when the characteristics deviate, the acceptable minimum acceptable value ARL of each characteristic032,50,50,32and 50; based on the state transition probability matrix, the undetected degree of occurrence deviation of each characteristic is as follows:
Figure BDA0003003518920000154
Figure BDA0003003518920000155
Figure BDA0003003518920000156
Figure BDA0003003518920000157
Figure BDA0003003518920000158
step 8, calculating an integrated RPN value of the process characteristic; first, 3 experts (expert weight S) are collectedl(0.25,0.4,0.35)) preference for three risk factors, obtaining weights for the three risk factors; the comprehensive evaluation values of the 3 experts on each risk factor are shown in table 4;
TABLE 4 comprehensive evaluation value of experts on three risk factors
Figure BDA0003003518920000159
Figure BDA0003003518920000161
The piecewise function M is constructed as:
Figure BDA0003003518920000162
combined with expert individual weights Sl(0.25,0.4,0.35), available,
Figure BDA0003003518920000163
thus, the weights of the three risk factors are calculated, WI＝0.38；WP＝0.34；WD＝0.28；
Considering as many principles as possible, as well as expert weights, it is possible to obtain,
Figure BDA0003003518920000164
and (3) integrating the values of the three risk factors of the process characteristic to obtain the RPN value of each process characteristic:
R1＝WPC1＝0.234；R2＝WPC2＝0.142；R5＝WPC5＝0.174；R6＝WPC6＝0.317；R7＝WPC7＝0.133
accordingly, PC is degraded with respect to camshaft reliability6,PC1,PC5,PC2 and PC7A set of prioritized root cause sets; namely, the concentration of cutting fluid, the cutter bounce, the rotating speed of the cutter, the feeding speed of the cutter and the positioning of a workpiece are root causes for the reliability degradation of the camshaft; in addition, when controlling these factors, control measures should be implemented with emphasis on risk priority, and the root cause identification method can be used for reasonably guiding the establishment of targeted and focused process control activities in the manufacturing process.

Claims (9)

1. A product manufacturing reliability degradation root cause identification method based on QFD decomposition and an extended RPN value proposes the following conditions:
the condition 1 is that the manufacturing system is in an ideal state, and the influence of human factors on equipment is not considered when the production equipment runs;
condition 2, equipment within a manufacturing system experiences a series of discretized states ranging from normal operation, defective operation to complete failure; the processing capacity of the apparatus is divided into Em＝[e1,e2,e3...eM], wherein ,e1 and eMRespectively representing a normal operation state and a complete failure state of the equipment;
condition 3, the probability of any processing capacity state occurring in the running process of the equipment is Pm＝[qp1,qp2,qp3...qpM]Wherein qp1 and qpMRespectively representing the probability of the occurrence of an ideal state and the probability of the occurrence of complete failure of the equipment, wherein p is a constant; any processing capacity states are independent of each other,
Figure FDA0003003518910000011
namely the probability sum of the occurrence of each processing capacity state of the equipment is 1;
condition 4, when the equipment processing capacity state is m ═ x, the equipment can not complete the specified production task, and the probability of the state is
Figure FDA0003003518910000012
The condition 5 is that the environmental factor in the manufacturing system is the target characteristic, the acceptable capability index is a, and when the actual capability index is lower than a, measures should be taken to control the environmental factor;
the method is characterized in that: the method comprises the following steps:
step 1, determining and decomposing key reliability characteristics of products;
step 2, extracting the part characteristics of the product;
step 3, mapping and decomposing the characteristics of the product parts to a production line;
step 4, acquiring the characteristics of the process units on the production line and eliminating redundant characteristics;
step 5, calculating the Importance of the characteristics to the reliability characteristics of the product as a first risk factor, namely, immunity to reliability characteristics, I;
step 6, calculating the Probability of the characteristic deviation as a second risk factor, namely Proavailability of KCs variation, P;
step 7, calculating the undetectable degree of characteristic deviation under the current control condition as a third risk factor, namely detectivity for KCs variation, D;
and 8, calculating a characteristic integration RPN value so as to determine a root cause causing the degradation of the manufacturing reliability of the product.
2. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the "determining and decomposing of the key reliability characteristics of the product" described in step 1 means that the mechanism leading to the degradation of the manufacturing reliability of the product is clarified according to the interaction mechanism between the reliability of the manufacturing system, the quality of the manufacturing process and the manufacturing reliability of the product; further, according to the fault form of the product, the product-level characteristics are decomposed layer by layer to obtain a degraded part; the specific method comprises the following steps: the early failure of the product is taken as the top event of the associated tree, the characteristics of the product at the component level, the component level and the part level are sequentially decomposed according to the failure mode of the product at the initial use stage and the physical structure of the product, the inspection data related to the product, the component and the part in the production process are collected, and the degraded part is finally determined.
3. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the step 2 of extracting the part characteristics of the product refers to the characteristic related to the part needing to be extracted after the degraded part is obtained; for a product, a series of interdependent parts are the basic units that make up the product; parts are composed of different families, and there are some characteristics in common among parts with similar functions; for each series, common features may be machined from functionally similar production lines; some parts have specific characteristics in addition to common characteristics; in a word, the part characteristic library after classification and extraction consists of common characteristics and specific characteristics; the specific method comprises the following steps: based on the identified degraded parts, according to the space structure of the manufactured parts and the manufacturing process plan, the characteristics of the parts finished on the same production line are divided into a common characteristic library, and for the characteristic of one part, the characteristic is divided into a specific characteristic library, so that the extraction and classification of the characteristic library of the parts are realized.
4. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the step 3 of mapping and decomposing the product part characteristics to the production line means that a production engineer decomposes the part characteristics to the corresponding production line according to a production plan; the establishment of the part characteristic library is beneficial to more planned and systematic production line decomposition; at this time, identifying a production line for which a deviation has occurred in relation to the degraded part characteristics; the specific method comprises the following steps: according to the part manufacturing process planning, the process flow chart and the technical document, a production line related to the part characteristic processing is determined, and data from the corresponding production line is collected, so that the production line with the deviation is determined.
5. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the step 4 of acquiring the characteristics of the process units on the production line and eliminating the redundancy characteristics refers to acquiring a group of control characteristics on the production line from mutual influence on the process units according to process composition, and on the basis, eliminating the redundancy characteristics by using LASSO regression, thereby acquiring an initial root cause set; the specific method comprises the following steps: searching process information of each process unit and data for controlling key nodes in the production process by referring to an enterprise resource planning system, and collecting process characteristic data on a production line, wherein in the process, the collected index data is ensured to be comprehensive and complete as much as possible; on the basis, a correlation equation between the product reliability characteristic and the process characteristic is constructed by using LASSO regression, so that the collinear process characteristic is eliminated, and a group of mutually independent characteristic sets, namely a root cause initial set, is obtained; to this end, step 1-4 establishes a root cause correlation tree of product manufacturing reliability degradation by means of waterfall decomposition of the QFD, and obtains mutually independent root cause initial sets.
6. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the step 5 of calculating the importance of the characteristics to the product reliability characteristics refers to that the contribution of the characteristics to the product reliability characteristics is considered as a result of the deviation of the characteristics, and the LASSO regression coefficient in the step 4 is used as a first risk factor; the method comprises the following specific steps: using the regression equation of the correlation relationship established in step 4, the dependent regression coefficient can measure the correlation between the independent variable and the dependent variable, and the regression coefficient
Figure FDA0003003518910000031
For characterizing the importance of the process characteristics to the reliability characteristics of the product, wherein p represents the number of sample data, n is the number of characteristics, yiFor the ith product reliability characteristic, xijFor the jth process characteristic related to the ith product reliability characteristic, the regression coefficient is adjusted
Figure FDA0003003518910000032
The normalization results in the importance of the property, i.e. the first risk factor I.
7. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the "calculation of the probability of the characteristic deviation" in step 6 means that the probability of the characteristic deviation within the time t is used as a second risk factor; the probability that the characteristic does not deviate within the time t is considered to be that the equipment does not malfunction during the time and the production environment factor is at a normal level, so that the probability that the characteristic deviates is
Figure FDA0003003518910000041
Determining the probability of no failure of the equipment based on the probability distribution of the processing capacity state of the equipment in the production process, and determining the probability of keeping the environmental factor at a normal level based on the capacity index of the environmental factor in the manufacturing system;
the method comprises the following specific steps: according to processing equipment related to process characteristics, fault data of the equipment in the production process and the amount of finished processing tasks are collected, environmental factors influencing the process characteristics of the manufacturing system are collected for production personnel, and process specification requirements related to the environmental factors and data collected in the actual processing process are collected;
firstly, calculating the probability that the equipment does not have faults within time t; the probability distribution of the occurrence of any processing capacity state of the equipment in the time t is Pm＝[qp1,qp2,qp3...qpM]When the processing capacity state of the equipment is m ═ x, the equipment cannot complete a given production task; in connection with the unavailability of the equipment, on the one hand, the unavailability performance of the equipment is expressed as the ratio of the loss of capacity to the capacity in an ideal state; on the other hand, the unavailable performance of the equipment is considered as the ratio of the time that the machine cannot work normally to the working time in an ideal state; unavailability of a device is expressed as
Figure FDA0003003518910000042
wherein ,
Figure FDA0003003518910000043
for the number of equipment failures in time t, γ is the down time due to the equipment failure, n and γ' represent the number of overhauls and the down time at that time, respectively, exRepresenting the state of the processing capacity of the plant, e1Is the ideal operating state of the apparatus, emRepresenting apparatusA complete failure state of; thus, the probability that the device will not fail is
Figure FDA0003003518910000044
Secondly, in the production process, the fluctuation of the environmental factors exceeding the normal level can directly cause the deviation of the characteristics; capability index of environmental factor
Figure FDA0003003518910000045
wherein T＝TU-TL，Δ＝|μ-M|，TU and TLRespectively representing specified upper and lower limits, mu and M are actual and standard distribution central values; collecting environmental factor data in the production process to obtain a group of environmental factor capability index sets Sc＝{Cpe1,Cpe2,…Cpen}; thus, the probability that the environmental factor remains at a normal level is
Figure FDA0003003518910000051
wherein ncSatisfies C for the ability indexpenNumber of times > a, NcIs the total number of samples;
at this point, the probability that the equipment does not fail within the time t and the probability that the environmental factor is kept at a normal level are calculated, and finally, P is calculatedvAnd is the second risk factor P.
8. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the step 7 of calculating the undetectable degree of characteristic deviation under the current control condition refers to the capability of detecting the characteristic deviation under the current control condition, an Average running chain length (ARL) is used to represent the undetectable degree of characteristic deviation, and the ratio of the actual ARL to the acceptable ARL is used
Figure FDA0003003518910000052
As a third windA risk factor; the method comprises the following steps: collecting characteristic state data acquired by sensor in production process, and determining characteristic state transition probability matrix P by using Markov matrix to obtain ARL value ARL ═ Si(I-P)-1E, where P is the transition probability matrix, Si＝[0,...,1,...0]1×rIn the form of an initial probability matrix, the probability matrix,
Figure FDA0003003518910000053
further, acceptable ARL of each characteristic in the actual production process is determined0Thereby calculating DuAnd a third risk factor D.
9. The method for identifying the root cause of product manufacturing reliability degradation based on QFD decomposition and extended RPN value as claimed in claim 1, wherein: the "calculation of characteristics to integrate the RPN value to determine the root cause of the deterioration in the reliability of product manufacturing" described in step 8 means that the degree of preference X ═ X in combination for the three risk factorsI,XP,XDIntegrating three risk factors to calculate an RPN value of each characteristic; the method comprises the following specific steps: by the hesitation fuzzy theory, the preference degrees of the three risk factors are expressed by the hesitation fuzzy semantics, { c10}, wherein,
Figure FDA0003003518910000054
then, the fuzzy semantics are transformed to obtain a hesitant fuzzy binary semantic decision matrix
Figure FDA0003003518910000055
wherein
Figure FDA0003003518910000056
cg1,2,3, 4; the value of the composite rating for solution set X is shown as
Figure FDA0003003518910000061
Wherein I ═ I, P, D,
Figure FDA0003003518910000062
is a rounding function, then
Figure FDA0003003518910000063
α ═ x-around (x); combining weight S ═ S1,s2,…slThe preference values for the three risk factors }
Figure FDA0003003518910000064
wherein ,
Figure FDA0003003518910000065
t*＝M(∑j≤lsσ(j))-M(∑j＜lsσ(j))，M(x)＝∑j≤n-1tj+tj(lx-(n-1))；sσ(j)represents the jth maximum, M is a piecewise function
Figure FDA0003003518910000066
W (x) preference weights for three risk factors, t*A weight operator after t correction; at the moment, the preference degrees of the three risk factors are calculated; for each property, { I, P, D } is considered as a set of properties with different weight coefficients for each property, and the hesitation fuzzy theory is applied again, using the equation described in step 8, whereby the risk RPN value of each property is calculated; finally, a set of prioritized root cause sets that lead to product manufacturing reliability degradation is identified, which provides a reference for the producer to exercise targeted and focused control over parameters in the process.
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