CN113094827B - QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method - Google Patents
QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method Download PDFInfo
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Abstract
The invention provides a QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method, which comprises the following specific steps: 1. determining and decomposing key reliability characteristics of the product; 2. extracting part characteristics of the product; 3. mapping and decomposing the characteristics of the product parts to a production line; 4. acquiring process unit characteristics on a production line and eliminating redundancy characteristics; 5. calculating the importance of the characteristics to the reliability characteristics of the product as a first risk factor; 6. calculating the probability of deviation of the characteristics as a second risk factor; 7. calculating an undetectable measure of deviation of the characteristic under the current control condition, and taking the undetectable measure as a third risk factor; 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 overcomes 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 of the production process with pertinence and emphasis.
Description
Technical Field
The invention provides a QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method, and belongs to the field of reliability.
Background
Along with the rapid development of various industries and the influence of global economy integration on national economy, the manufacturing industry level becomes an important mark for measuring the comprehensive national force of a country and is also an important expression of the international competitiveness of the country. With the advent of the intellectualization and big data age, the manufacturing industry in China has been unprecedented, and the development of the manufacturing industry promotes the production of a series of products with complex structures and integrated functions. However, the variable production environment, the complex manufacturing process, and other uncertain factors are endless, and the manufacturing process needs to ensure the reliability of the product in such a complex and variable dynamic background faces a great challenge, so that the reliability of the manufactured product often has difficulty in achieving the design reliability target. Therefore, identifying the root cause of the degradation of product reliability in the manufacturing process has become a problem to be solved urgently in the current manufacturing industry. In the past, the research on the reasons of the product reliability degradation is often focused on product fault diagnosis, and the interaction among the manufacturing process, the manufacturing system and the product reliability is ignored from the aspects of product fault data fitting and physical structure modeling, so that the product reliability degradation can not be fundamentally prevented.
In the manufacturing process of the product, raw materials enter a workshop, and an operator changes the shape, the size, the surface characteristics and other parameters of the raw materials according to a certain processing mode and a certain technical rule to finally form a finished product meeting the quality requirement. However, the increasing complexity of the products complicates the manufacturing process, the processing route, the process rules, the assembly process, and the layout of workshops, and further increases the complexity of the process due to factors such as the operator and the processing environment during the manufacturing process. Product reliability originates at the design stage and is formed 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 requirements of the product design stage, that is, to make the reliability of the output product of the manufacturing stage as close to the design requirements as possible, the manufacturing process is an object of attention of manufacturers as a key stage for the formation of the reliability of the product. However, in the prior art, in the research of process reliability in the manufacturing process, the optimization of process parameters is achieved by focusing on the control of the performance state of equipment and key processes in the manufacturing system, and most of the research is cut in from the angles of equipment and processes, and the research of manufacturing reliability guarantee with product development as a core is omitted. From a system engineering perspective, there is a dense and inseparable interaction between the manufacturing process equipment, manufacturing process, and output product. Therefore, how to directly face the reason for the degradation of the reliability of the product in the manufacturing process of product identification has become a serious engineering problem in the manufacturing field and the reliability field.
However, the existing researches are directed to the phenomena that the product is frequently subjected to initial fault and the reliability is reduced, most of the existing researches depend on the accurate physical structure of the product, and in view of the fact that the product is the direct output of the manufacturing process, the variables in the manufacturing process are important factors influencing the reliability of the product, and the existing researches are lack of further researches on how to use big data in the manufacturing process to build a relation model of the reliability of the product and the variables in the manufacturing process. In addition, the manufacturing process guarantee is an effective way for improving the reliability of the product, but most of the existing researches start from equipment and working procedures, neglect the interaction between a manufacturing system and the reliability of the product, and do not directly face the product so as to pertinently identify manufacturing process variables affecting the reliability of the product. In response to this phenomenon, the present patent developed a product manufacturing reliability degradation root cause identification method based on quality function expansion (quality function deployment, QFD) and extended risk priority (risk priority number, RPN). Based on the operation process of the manufacturing system, the connotation of the manufacturing reliability of the product is proposed, and the degradation mechanism of the manufacturing reliability of the product is combed. Further, by utilizing waterfall decomposition of QFD, from the perspective of system engineering, a root cause association tree with degraded product manufacturing reliability is built from top to bottom by taking a product as a core, thereby determining an initial set of root causes. And integrating a risk idea, comprehensively analyzing the risk of the variable in the initial set of root causes on the reliability of the product based on the expanded RPN value, comprehensively evaluating the importance of the variable, and finally determining the root cause causing the manufacturing reliability degradation of the product. The invention is suitable for identifying root causes influencing the reliability of products in the manufacturing process, and can provide references for the reliability control of manufacturers in the production process.
Disclosure of Invention
(1) The purpose of the invention is that:
in order to solve the problem that characteristics affecting the reliability of products in the existing research identification process can not be directly developed by taking the products as cores, the invention provides a systematic method for identifying the root causes of the reliability degradation of the product manufacture, namely a method for identifying the root causes of the reliability degradation of the product manufacture based on QFD decomposition and expansion RPN values. On the basis of clarifying the connotation of the reliability of product manufacture, the degradation mechanism of the reliability of product manufacture is described. And taking the early failure rate of the product as a top event, decomposing the product characteristics into part characteristics, production lines and process parameters layer by layer from top to bottom, constructing a correlation tree of root causes, and determining an initial set of the root causes. And on the basis of three risk factors, namely the importance degree of the comprehensive characteristics on the reliability characteristics of the product, the probability of deviation of the characteristics and the undetectable measurement of the deviation of the characteristics, the RPN value of each characteristic is calculated based on the hesitation fuzzy theory, and the characteristics with smaller risk consequences in the initial set of root causes are removed, so that the root causes causing the manufacturing reliability degradation of the product are determined. The purpose of identifying the root cause that causes the degradation of the reliability of the product manufacture is achieved.
(2) The technical scheme is as follows:
the basic assumptions proposed by the present invention are as follows:
assuming that 1, a manufacturing system is in an idealized state, and the influence of human factors on equipment is not considered when production equipment runs;
assume 2, a series of discretized states of equipment calendar within a manufacturing system from normal operation, defective operation, to complete failure; the processing capacity of the apparatus is divided into E m ＝[e 1 ,e 2 ,e 3 …e M], wherein ,e1 and eM Representing the normal running state and the complete failure state of the equipment respectively;
suppose that the probability of any processing capacity state of the equipment in the running process is P m ＝[qp 1 ,qp 2 ,qp 3 …qp M ]Wherein qp 1 and qpM The probability of occurrence of the ideal state and the probability of occurrence of complete failure of the equipment are represented respectively, and p is a constant; the states of any processing capability are independent of each other,
i.e. the sum of the probabilities of occurrence of the respective process capability states of the apparatus is 1;
suppose 4,When the processing capability state of the equipment is m=x, the equipment cannot complete the designated production task, and the probability of the state is that
Assuming that the environmental factor in the manufacturing system is a vision-looking characteristic, the acceptable capacity index is a, and when the actual capacity index is lower than a, a certain measure should be taken to control the environmental factor;
based on the assumption, the invention provides a product manufacturing reliability degradation root cause identification method based on QFD decomposition and expansion of RPN values, which comprises the following steps:
step 3, mapping and decomposing the characteristics of the product parts to a production line;
step 4, obtaining the characteristics of the process units on the production line and eliminating the redundancy characteristics;
step 5, calculating the importance of the characteristic to the reliability characteristic of the product as a first risk factor (Importance to reliability characteristics, I);
step 6, calculating the probability of deviation of the characteristics as a second risk factor (Probability of KCs variation, P);
step 7, calculating the undetectable degree of the characteristic deviation under the current control condition as a third risk factor (Detectability for KCs variation, 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 the step 1 refers to determining the mechanism of the degradation of the manufacturing reliability of the product 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 failure mode of the product, the characteristics of the product level are decomposed layer by layer to obtain a degraded part; the specific method is as follows: and taking early failure of the product as a top event of the association tree, sequentially decomposing the product into part level, assembly level and part level characteristics according to the physical structure of the product according to the failure form of the product in the initial stage of use, collecting inspection data related to the product, assembly and part in the production process, and finally determining the degraded part.
Wherein, the "extracting the part characteristics of the product" in the step 2 means that after the degraded part is obtained, the relevant characteristics of 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 often composed of different families, and there are some common characteristics in parts with similar functions; for each series, the common characteristics may be machined from functionally similar production lines; in addition to the common characteristics, certain parts have specific characteristics; in short, the part characteristic library after classified extraction consists of common characteristics and specific characteristics; the specific method is as follows: based on the identified degraded parts, the characteristics of the parts completed on the same production line are divided into a common characteristic library according to the space structure of the manufactured parts and the manufacturing process planning, and the characteristics specific to a certain part are divided into a specific characteristic library, so that the extraction and classification of the part characteristic library are realized.
Wherein, the step 3 of mapping and decomposing the characteristics of the parts of the product to the production line refers to decomposing the characteristics of the parts to the corresponding production line by a production engineer 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, a production line in which deviations related to degraded part characteristics are obtained is identified; the specific method is as follows: the production line related to the characteristic processing of the parts is determined by the process staff according to the part manufacturing process planning, the process flow chart, the technical document and the like, and the data from the corresponding production line is collected, so that the production line with deviation is determined.
The step 4 of obtaining the characteristics of the process units on the production line and eliminating the redundant characteristics refers to obtaining a group of control characteristics derived from the mutual influence on the process units on the production line according to the process composition, and on the basis, eliminating the redundant characteristics by using LASSO (Least absolute shrinkage and selection operator, LASSO) regression, thereby obtaining an initial set of root causes; the specific method is as follows: 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 the collected index data is ensured to be comprehensive and complete as much as possible in the process; on the basis, a correlation equation between the reliability characteristic and the process characteristic of the product is constructed by using LASSO regression, so that the co-linear process characteristic (hereinafter referred to as characteristic) is eliminated, and a group of mutually independent characteristic sets, namely a root cause initial set, are obtained; to this end, step 1-4 establishes a root cause association tree with degraded product manufacturing reliability by using waterfall decomposition of QFD, and obtains mutually independent root cause initial sets, as shown in fig. 3.
The "importance of the calculated characteristics to the product reliability characteristics" in step 5 refers to that the contribution of the characteristics to the product reliability characteristics is considered as the result of deviation of the characteristics, and the LASSO regression coefficient in step 4 is taken as a first risk factor; the specific method comprises the following steps: the correlation between the independent variable and the dependent variable can be measured by using the correlation regression equation established in the step 4, and the regression coefficient
For characterizing the importance of a process characteristic to the reliability characteristics of a product, wherein p represents the number of sample data, n is the number of characteristics, y i For the ith product reliability feature, x ij For the j-th process characteristic related to the i-th product reliability characteristic, for the regression coefficient
The normalization yields the importance of the characteristic, i.e. the first risk factor I.
The "calculating the probability of deviation of the characteristic" in step 6 refers to taking the probability of deviation of the characteristic in time t as a second risk factor; in this patentThe probability of the characteristic not deviating in time t is considered to be that the equipment does not malfunction and the production environment factor is at a normal level during this period of time, and therefore the probability of the characteristic deviating is
The method comprises the steps of determining the probability that equipment does not fail based on equipment processing capacity state probability distribution in the production process, and determining the probability that environmental factors keep normal level based on capacity indexes of the environmental factors in a manufacturing system;
the specific method comprises the following steps: according to processing equipment related to the process characteristics, collecting fault data of the equipment in the production process and finishing the processing task quantity, and collecting the process specification requirements related to the environmental factors and data acquired in the actual processing process by collecting the environmental factors influencing the process characteristics of a manufacturing system to production personnel;
firstly, calculating the probability that equipment does not fail in time t; during time t, the probability distribution of the occurrence of any machining capacity state of the equipment is P m ＝[qp 1 ,qp 2 ,qp 3 …qp M ]When the processing capacity state of the equipment is m=x, the equipment cannot complete a given production task; in combination with the unavailability of the plant, on the one hand, the unavailability of the plant can be expressed as a ratio of the loss of production capacity to the production capacity of the ideal state; on the other hand, the unavailability of a device may be considered as the ratio of the time that the machine is not operating properly to the time that it is operating in an ideal state; the unavailability of a device is expressed as
wherein ,/>
For the number of equipment failures within time t, γ is the downtime due to the equipment failure, n and γ' represent the number of overhauls and the downtime at that time, e x Representing the state of the processing capability of the apparatus e 1 Is the ideal operating state of the equipment, e m Representing a complete failure state of the device; thus, the probability of the device not failing is
Secondly, in the production process, environmental factor fluctuation exceeding the normal level can directly lead to characteristic deviation; capability index of environmental factor
wherein T＝TU -T L ，Δ＝|μ-M|，T U and TL Respectively representing the upper limit and the lower limit of regulation, wherein 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 S c ＝{C pe1 ,C pe2 ,…C pen -a }; therefore, the probability that the environmental factor remains at the normal level is +.>
wherein nc To satisfy the capability index C pen Number of times > a, N c The total number of times is the sample;
so far, calculating to obtain the probability of no failure of the equipment in the time t and the probability of keeping the environmental factor at the normal level, and finally calculating to obtain P v Is the second risk factor P.
Wherein the term "calculate the undetectable degree of deviation of characteristic under the current control condition" as referred to in step 7 means the ability to detect deviation of characteristic under the current control condition, the undetectable degree of deviation of characteristic is characterized by average running chain length (Average running chain length, ARL), the ratio of actual ARL to acceptable ARL is used
As a third risk factor; the specific method comprises the following steps: state data of characteristics acquired by sensors during production are collected, and a transition probability matrix P of the characteristic state is obtained by using a markov matrix, thereby obtaining ARL value arl=s i (I-P) -1 E, wherein P is a transition probability matrix, S i ＝[0,…,1,…0] 1×r For the initial probability matrix +.>
Further, the acceptable ARL of each characteristic in the actual production process is determined by the production engineer 0 Thereby calculating D u Is the third risk factor D.
Wherein, the "calculation of the characteristic-integrated RPN value to determine the root cause causing the degradation of the reliability of the product manufacturing" described in step 8 means that the preference degree x= { X of three risk factors by combining the expert is I ,X P ,X D Integrating three risk factors to calculate RPN values of the characteristics; the specific method comprises the following steps: through hesitation fuzzy theory, the expert expresses the preference degree of three risk factors using hesitation fuzzy semantics, e.g., { c 1 0, wherein,
subsequently, the fuzzy semantics are transformed to obtain a hesitant fuzzy binary semantic decision matrix +.>
wherein />
Expert's comprehensive evaluation value of solution set X is expressed as +.>
Wherein i=i, P, D, +.>
For rounding the function +.>
α＝x-c round(x) The method comprises the steps of carrying out a first treatment on the surface of the Combining expert weights s= { S 1 ,s 2 ,…s l Expert preference values for three risk factors +.>
wherein ,/>
t * ＝M(∑ j≤l s σ(j) )-M(∑ j＜l s σ(j) )，M(x)＝∑ j≤n-1 t j +t j (lx-(n-1))；s σ(j) Represents the jth maximum, M is a piecewise function
W (x) is the preference weight of three risk factors, t * A weight operator corrected for t; at this time, the preference degree of the expert for the three risk factors is calculated; for each characteristic, the patent considers { I, P, D } as an attribute set with different weight coefficients for each characteristic, applies the hesitation fuzzy theory again, uses the equation in the step 8, calculates the risk RPN value of each characteristic, and eliminates the characteristic with smaller RPN value in the root cause initial set; finally, a set of prioritized root causes that lead to degradation of product manufacturing reliability are 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 QFD decomposition and expansion RPN value is provided, the problem that the interaction mechanism between the manufacturing process and the product and the analysis of the product manufacturing reliability degradation mechanism are not comprehensive in the traditional method are solved, the method has guiding significance for identifying the cause of the product reliability degradation output in the manufacturing process and improving the product reliability in a targeted manner, and the method provides targeted and focused reference for process control in the manufacturing process of a producer, so that the product reliability is effectively ensured to further meet the design reliability target and the user requirement of the product.
(3) The invention relates to a QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method, which comprises the following steps:
determining and decomposing key reliability characteristics of a product; the specific method comprises the following steps: the method comprises the steps of decomposing a top event in a product fault mode as a correlation tree, and determining a part with degradation by decomposing the top event layer by layer according to the physical structure of the product;
step (2) extracting part characteristics of the product; the specific method comprises the following steps: collecting, classifying and extracting the related characteristics of the degraded parts to obtain the common characteristics and the specific characteristics of the parts;
step (3), mapping and decomposing the characteristics of the product parts to a production line; the specific method comprises the following steps: decomposing the part characteristics into corresponding production lines according to the production plan, and identifying and obtaining the production lines related to the degraded part characteristics;
step (4) obtaining the characteristics of process units on the production line and eliminating the redundant characteristics; the specific method comprises the following steps: collecting relevant technological parameter characteristics on a production line, and removing redundancy characteristics based on LASSO regression to obtain a group of mutually independent root cause initial sets; thus, a root cause association tree framework for product manufacturing reliability degradation is constructed;
step (5) calculating the importance of the characteristic to the reliability characteristic of the product; the specific method comprises the following steps: collecting related data of root cause and product reliability characteristics in the production process, constructing a related relation equation of the root cause and the product reliability characteristics based on LASSO regression, and obtaining regression coefficients of the characteristics
Namely a first risk factor I;
step (6) calculating the probability of deviation of the characteristics; the specific method comprises the following steps: collecting relevant data of equipment in the production process, analyzing the fault mode of the equipment, and obtaining the probability distribution P of the processing capacity state of the equipment through statistics m ＝[qp 1 ,qp 2 ,qp 3 …qp M ]Calculating probability P of failure of equipment in production process Ei The method comprises the steps of carrying out a first treatment on the surface of the Collecting relevant data of environmental factor monitoring in the production process, and calculating environmental factor capability index C pe Obtaining probability P that environmental factors keep normal C The method comprises the steps of carrying out a first treatment on the surface of the Subsequently, a probability P of deviation of the characteristics is calculated in time t v ＝1-P E ×P C I.e. a second risk factor P;
step (7) calculating the undetectable amount of the characteristic deviation under the current control condition; the specific method comprises the following steps: collecting related data of root cause under current control condition, and constructing Markov probability transition matrix
step (8) calculating a characteristic integration RPN value to determine a root cause causing the degradation of the manufacturing reliability of the product; the specific method comprises the following steps: applying hesitation fuzzy theory and combining weights of the experts to calculate preference degree X= { X of the experts on three risks I ,X P ,X D And then integrating the importance I of the characteristic, the probability P of deviation of the characteristic and the undetectable degree D of the characteristic, calculating to obtain the RPN value R=f (I, P, D) of the characteristic in the initial set of root causes, and eliminating the characteristic with smaller risk value, thereby obtaining a group of root cause sets with risk priority.
(4) Advantages and efficacy:
the invention relates to a QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method, which has the advantages that:
i. from the perspective of system engineering, the invention considers the interrelationship among the reliability of a manufacturing system, the quality of a manufacturing process and the reliability of a product, and describes the degradation mechanism of the manufacturing reliability of the product;
the QFD is decomposed into the carriers, and a product manufacturing reliability degradation root cause association tree with product reliability characteristics, part characteristics, production lines and process characteristics is systematically constructed from top to bottom;
the risk concept is integrated, three risk factors of the importance of the characteristics, the probability of deviation of the characteristics and the undetectable measurement of the deviation of the characteristics are expanded, RPN values of all root causes are obtained, and the root causes causing the degradation of the manufacturing reliability of the product are determined according to the priority order of the risks;
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 product manufacturing reliability degradation root cause association tree based on QFD decomposition.
FIG. 4 is a correlation tree for camshaft manufacturing reliability degradation root cause identification
The symbols in the drawings are as follows:
5M1E refers to manufacturing System personnel (Man), machine, raw materials (Material), method, measurement, environment (environmental)
CRCs are key reliability features of products (Critical reliability characteristics, CRCs)
PCs are product technological properties (Process characteristics, PCs)
I is the importance of the process characteristics to the reliability characteristics of the product (I)
P is the Probability of process characteristic variation (Procapability, P)
D is an undetectable measure of process characteristic variation (Detectability, D)
SD1 is a product-level characteristic code of the camshaft; SD1.1-1.4 are component level characteristic codes of the camshaft;
SD1.1.1-1.3.1 is the part-level characteristic code of the camshaft
ED1-ED2 are common characteristic designations for camshaft parts SD1.1.1-SD1.3.1; SD1-SD2 are camshaft parts
Specific characteristic code of SD1.1.1-SD1.3.1
PD1 is the total code of the cam shaft hole surface processing production line; PD1.1-PD1.4 are the surface processing technological process codes of the camshaft holes; PD1.1.1-PD1.4.1 are equipment codes related to cam shaft hole surface processing
PC1-PC7 are process characteristic codes for cam shaft hole surface processing
QFD-quality function expansion (quality function deployment QFD)
RPN-risk priority (risk priority number, RPN)
LASSO-LASSO (Least absolute shrinkage and selection operator, LASSO) minimum absolute shrinkage and selection operation regression
Detailed Description
The invention will now be described in further detail with reference to the drawings and examples.
The invention relates to a QFD decomposition and expansion RPN value-based product manufacturing reliability degradation root cause identification method, which is shown in figure 2and comprises the following steps of
TABLE 1 camshaft processing precision requirement (section selection)
Step 3, collecting production lines and related equipment related to a cam shaft hole processing technology; the detailed process flow and equipment are shown in the production line group in fig. 4;
step 4, obtaining a group of process characteristics { PC { from the mutual influence on the process units according to the camshaft processing production line and related equipment 1 ，PC 2 ，PC 3 ，PC 4 ，PC 5 ，PC 6 ，PC 7 The sensor and the enterprise production resource planning data system are utilized to collect related process characteristic data, the integrity and the accuracy of the data are ensured as much as possible, and the related relationship between the process characteristic data and the surface roughness of the cam shaft is constructed by utilizing LASSO regression; removing redundant technological characteristics to form a group of mutually independent root cause initial sets { PC } 1 ，PC 2 ，PC 5 ，PC 6 ，PC 7 On the basis of the root cause construction framework shown in fig. 3, an initial set association tree of the camshaft reliability degradation root causes shown in fig. 4 is completed, which is simply "acquire process unit characteristics on the production line and reject redundant characteristics";
and 5, calculating the importance of the process characteristic on the surface roughness of the camshaft. Obtaining regression coefficients of all process characteristics based on the LASSO regression result in the step 4, and obtaining a first risk factor I of each characteristic after standardization;
Y＝0.286PC 1 +0.119PC 2 -0.092PC 5 +0.5113PC 6 +0.078PC 7
I 1 ＝0.317
I 2 ＝0.132
I 5 ＝0.102
I 6 ＝0.567
I 7 ＝0.086
step 6, calculating the probability of deviation of the process characteristics; collecting related data of production equipment in the cam shaft hole processing process; firstly, analyzing the processing capacity states and 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 thereof; the processing of the camshaft hole involves four devices; according to production loss caused by downtime of different fault modes, the process capacity state of the equipment can be classified into five grades, and the statistical result of fault data of each equipment is shown in table 2;
TABLE 2 Equipment Process Capacity status and failure data
Based on the above equipment failure statistics, it is possible to obtain:
the duration of the batch of camshaft processing is t=10 days; therefore, the failure rate of each device is:
P EKC1 ＝P E3.1 ×P E4.1 ＝0.951×0.965＝0.918
P EKC2 ＝0.9968
P EKC5 ＝0.9972
P EKC6 ＝P E3.2 ×P E4.2 ＝0.924×0.931＝0.860
P EKC7 ＝0.9982
the environmental noise factors affecting the process characteristics are collected as shown in Table 3, and environmental factor data is collected every 0.5h, and when the capability index is satisfied, the capability state S of the environmental factors is determined to be an ideal state c ＝{C pe1 ,C pe2 ,…C pen Calculated and filled in table 3;
TABLE 3 environmental factor bias data
The probability of failure of the comprehensive equipment and the probability of normal level maintenance of environmental factors are given by the following probability of deviation of each process characteristic:
step 7, calculating the undetectable measure of deviation of each characteristic; based on 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, and the standard average value and the control limit H of each characteristic are determined, so that a state transition probability matrix of each characteristic is obtained;
when the characteristics deviate, the minimum acceptable value ARL of each characteristic is acceptable 0 32,50,50,32and 50; based on the state transition probability matrix, the undetectable degree of deviation of each characteristic is as follows:
step 8, calculating an integrated RPN value of the process characteristic; first, 3-bit expert (expert weight S l = (0.25,0.4,0.35)) on the three risk factors, obtaining weights of 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 values of three risk factors by expert
The piecewise function M is constructed as:
combining the weights S of the experts l = (0.25,0.4,0.35), it is possible to obtain,
thus, the weights of the three risk factors are calculated, W I ＝0.38；W P ＝0.34；W D ＝0.28；
Considering as many principles as possible, as well as expert weights, it is possible, that,
the values of three risk factors of the process characteristics are synthesized, and the RPN value of each process characteristic can be obtained:
R 1 ＝W PC1 ＝0.234；R 2 ＝W PC2 ＝0.142；R 5 ＝W PC5 ＝0.174；R 6 ＝W PC6 ＝0.317；R 7 ＝W PC7 ＝0.133
accordingly, for camshaft reliability degradation, PC 6 ,PC 1 ,PC 5 ,PC 2 and PC7 A set of root causes determined as a set of prioritized; namely, cutting fluid concentration, tool runout, tool rotational speed, tool feed speed, workpiece positioning are root causes that lead to degradation of camshaft reliability; in addition, in controlling these factors, control measures should be implemented with emphasis on risk priority, and the root cause identification method may be used to reasonably direct the formulation of targeted and focused process control activities in the manufacturing process.
Claims (5)
1. A product manufacturing reliability degradation root cause identification method based on QFD decomposition and expansion RPN value is provided as follows:
the manufacturing system is in an ideal state, and the influence of human factors on the equipment is not considered when the production equipment runs;
condition 2, equipment calendar within the manufacturing system goes through a series of discretized states from normal operation, defective operation, to complete failure; the processing capacity of the apparatus is divided into E m ＝[e 1 ,e 2 ,e 3 …e M], wherein ,e1 and eM Representing the normal running state and the complete failure state of the equipment respectively;
condition 3, probability of occurrence of any processing capability state of the equipment in the running process is P m ＝[qp 1 ,qp 2 ,qp 3 …qp M ]Wherein qp 1 and qpM The probability of occurrence of the ideal state and the probability of occurrence of complete failure of the equipment are represented respectively, and p is a constant; the states of any processing capability are independent of each other,
i.e. the sum of the probabilities of occurrence of the respective process capability states of the apparatus is 1;
condition 4, when the processing capability state of the equipment is m=x, the equipment cannot complete the designated production task, and the probability of occurrence of the state is that
The environment factor in the manufacturing system is a vision looking characteristic, the acceptable capacity index is a, and when the actual capacity index is lower than a, measures are taken to control the environment factor;
the method is characterized in that: the method comprises the following steps:
step 1, determining and decomposing key reliability characteristics of a product;
step 2, extracting part characteristics of the product;
step 3, mapping and decomposing the characteristics of the product parts to a production line;
step 4, obtaining the characteristics of the process units on the production line and eliminating the redundant characteristics;
step 5, calculating the importance of the characteristics to the reliability characteristics of the product, wherein the importance is taken as a first risk factor, namely Importance to reliability characteristics, I;
step 6, calculating the probability of deviation of the characteristics, wherein the probability is taken as a second risk factor, namely Probability of KCs variation and P;
step 7, calculating the undetectable measure of the characteristic deviation under the current control condition, and taking the undetectable measure as a third risk factor, namely Detectability for KCs variation and 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 step 4 of acquiring the characteristics of the process units on the production line and eliminating the redundancy characteristics refers to obtaining a group of control characteristics derived from the mutual influence on the process units on the production line according to the process composition, and eliminating the redundancy characteristics by using LASSO regression on the basis, 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 the collected index data is ensured to be comprehensive and complete as much as possible in the process; on the basis, a correlation equation between the reliability characteristic and the process characteristic of the product is constructed by using LASSO regression, so that the co-linear process characteristic is removed, and a group of mutually independent characteristic sets, namely a root cause initial set, are obtained; to this end, step 1-4 establishes a root cause association tree with degraded product manufacturing reliability by utilizing waterfall type decomposition of QFD, and obtains mutually independent root cause initial sets;
the "importance of the calculated characteristics to the product reliability characteristics" described in step 5 refers to that the contribution of the characteristics to the product reliability characteristics is considered as the result of deviation of the characteristics, and the LASSO regression coefficient in step 4 is taken as a first risk factor; concrete embodimentsThe method comprises the following steps: the correlation regression equation established in the step 4 is utilized, and the correlation between the independent variable and the dependent variable can be measured by the regression coefficient
For characterizing the importance of a process characteristic to the reliability characteristics of a product, wherein p represents the number of sample data, n is the number of characteristics, y i For the ith product reliability feature, x ij For the j-th process characteristic related to the i-th product reliability characteristic, for the regression coefficient +.>
The importance of the characteristics is obtained through standardization, namely a first risk factor I;
the "calculating the probability of deviation of the characteristic" in the step 6 refers to taking the probability of deviation of the characteristic within the time t as a second risk factor; the probability of the characteristic not deviating in time t is considered to be that the equipment does not malfunction and the production environment factor is at a normal level during this period of time, and therefore the probability of the characteristic deviating is
The method comprises the steps of determining the probability that equipment does not fail based on equipment processing capacity state probability distribution in the production process, and determining the probability that environmental factors keep normal level based on capacity indexes of the environmental factors in a manufacturing system;
the specific method comprises the following steps: according to processing equipment related to the process characteristics, collecting fault data of the equipment in the production process and finishing the processing task quantity, and collecting the process specification requirements related to the environmental factors and data acquired in the actual processing process by collecting the environmental factors influencing the process characteristics of a manufacturing system to production personnel;
firstly, calculating the probability that equipment does not fail in time t; during time t, the equipment can process randomlyThe probability distribution of the occurrence of the force state is P m ＝[qp 1 ,qp 2 ,qp 3 …qp M ]When the processing capacity state of the equipment is m=x, the equipment cannot complete a given production task; in combination with the unavailability of the plant, on the one hand, the unavailability performance of the plant is expressed as the ratio of the loss of production capacity to the production capacity of the ideal state; on the other hand, the unavailability of a device is considered to be the ratio of the time that the machine is not working properly to the time of working in an ideal state; the unavailability of a device is expressed as
Secondly, in the production process, environmental factor fluctuation exceeding the normal level can directly lead to characteristic deviation; capability index of environmental factor
wherein T＝TU -T L ，Δ＝|μ-M|，T U and TL Respectively representing the upper limit and the lower limit of regulation, wherein 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 S c ＝{C pe1 ,C pe2 ,…C pen -a }; therefore, the probability that the environmental factor remains at the normal level is +.>
wherein nc To satisfy the capability index C pen Number of times > a, N c The total number of times is the sample;
so far, calculating to obtain the probability of no failure of the equipment in the time t and the probability of keeping the environmental factor at the normal level, and finally calculating to obtain P v Is a second risk factor P;
the expression "calculating the undetectable degree of deviation of characteristic under the current control condition" in step 7 means the ability to detect deviation of characteristic under the current control condition, the undetectable degree of deviation of characteristic is characterized by average running chain length, i.e., average running chain length, ARL, the ratio of actual ARL to acceptable ARL is used
As a third risk factor; the specific method comprises the following steps: state data of characteristics acquired by sensors during production are collected, and a transition probability matrix P of the characteristic state is obtained by using a markov matrix, thereby obtaining ARL value arl=s i (I-P) -1 E, wherein P is a transition probability matrix, S i ＝[0,…,1,…0] 1×r For the initial probability matrix +.>
Further, determining acceptable ARL for each characteristic during actual production 0 Thereby calculating D u Is the third risk factor D.
2. The method for identifying root causes of product manufacturing reliability degradation based on QFD decomposition and expansion RPN values according to claim 1, wherein the method comprises the following steps: the "determining and decomposing the key reliability characteristics of the product" described in step 1 refers to that the mechanism causing 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; further, according to the failure mode of the product, the characteristics of the product level are decomposed layer by layer to obtain a degraded part; the specific method comprises the following steps: and taking early failure of the product as a top event of the association tree, sequentially decomposing the product into part level, assembly level and part level characteristics according to a physical structure according to the failure form of the product in the initial use stage, collecting inspection data related to the product, the assembly and the part in the production process, and finally determining the degraded part.
3. The method for identifying root causes of product manufacturing reliability degradation based on QFD decomposition and expansion RPN values according to claim 1, wherein the method comprises the following steps: the term "extracting the part characteristics of the product" in step 2 means that after the degraded part is obtained, the relevant characteristics of the part need to be extracted; the specific method comprises the following steps: based on the identified degraded parts, the characteristics of the parts completed on the same production line are divided into a common characteristic library according to the space structure of the manufactured parts and the manufacturing process planning, and the characteristic specific to a part 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 root causes of product manufacturing reliability degradation based on QFD decomposition and expansion RPN values according to claim 1, wherein the method comprises the following steps: the step 3 of mapping and decomposing the characteristics of the parts of the product to the production line refers to decomposing the characteristics of the parts to the corresponding production line by a production engineer 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, a production line in which deviations related to degraded part characteristics are obtained is identified; 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 part characteristic processing is determined, and data from the corresponding production line is collected, so that the production line with deviation is determined.
5. The method for identifying root causes of product manufacturing reliability degradation based on QFD decomposition and expansion RPN values according to claim 1, wherein the method comprises the following steps: in step 8The method for calculating the characteristic integrated RPN value to determine the root cause causing the product manufacturing reliability degradation refers to combining the preference degrees X= { X for three risk factors I ,X P ,X D Integrating three risk factors to calculate RPN values of the characteristics; the specific method comprises the following steps: through hesitation fuzzy theory, the preference degree of three risk factors is expressed by using hesitation fuzzy semantics, { c 1 0, wherein,
subsequently, the fuzzy semantics are transformed to obtain a hesitant fuzzy binary semantic decision matrix +.>
wherein />
c g =1, 2,3,4; the comprehensive evaluation value of the solution set X is expressed as
wherein ,
M(x)＝∑ j≤n-1 t j +t j (lx-(n-1))；s σ(j) represents the jth maximum, M is a piecewise function +.>
w (x) is the preference weight of three risk factors, t * A weight operator corrected for t; at this time, preference degrees of the three risk factors are calculated; for each characteristic, consider { I, P, D } as a set of attributes with different weight coefficients for each characteristic, apply the hesitation blur theory again, using the equation described in step 8, whereby the risk RPN value for each characteristic is calculated; finally, a set of prioritized root causes that lead to degradation of product manufacturing reliability are identified, which provides a reference for the producer to exercise targeted and focused control over parameters in the process. />
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