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CN111861124B - Identifiable performance evaluation method, identifiable performance evaluation system and storage medium applicable to injection molding machine - Google Patents

Identifiable performance evaluation method, identifiable performance evaluation system and storage medium applicable to injection molding machine Download PDF

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CN111861124B
CN111861124B CN202010560145.2A CN202010560145A CN111861124B CN 111861124 B CN111861124 B CN 111861124B CN 202010560145 A CN202010560145 A CN 202010560145A CN 111861124 B CN111861124 B CN 111861124B
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CN111861124A (en
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张汉瑞
毛忠發
苏治铭
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Shantou University
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Abstract

The invention relates to a recognizable performance evaluation method, a recognizable performance evaluation system and a recognizable performance evaluation storage medium suitable for an injection molding machine, comprising the following steps of 101, acquiring an injection molding defect data set of a single type of injection molding machine processed cutting workpiece tested at the time; step 102, calculating the signal-to-noise ratio of the injection molding machine tested at the time to the ith injection molding defect in the injection molding defect data setThe method comprises the steps of carrying out a first treatment on the surface of the Step 103, according to the signal-to-noise ratio of the injection molding machine to the ith injection molding defect in the injection molding defect data setObtaining the performance factor r of the injection molding machine tested at the time through conversion; 104, repeating the steps 101 to 103 for a plurality of times to obtain the corresponding performance factors of the m types of injection molding machines needing performance comparison，The larger the value of (c) represents the better the performance of the injection molding machine; according to the invention, the performance evaluation of the injection molding machine can be obtained through multiple tests and comparison of the performance factors, so that the analysis of the injection molding machine is facilitated. The invention is suitable for the field of data analysis.

Description

Identifiable performance evaluation method, identifiable performance evaluation system and storage medium applicable to injection molding machine
Technical Field
The invention relates to the field of artificial intelligence, in particular to an identifiable performance evaluation method, an identifiable performance evaluation system and a storage medium suitable for an injection molding machine.
Background
The performance evaluation is to perform various tests on a product through various tests, form an intuitive document and evaluate the product. One purpose of the evaluation is to provide a reference for the optimization of the product performance, which involves a wide and complex range and is never endless. Wherein the product may be an injection molding machine.
A reliable quality injection molding machine is a primary condition for ensuring product quality, and for performance evaluation of the injection molding machine, many unknown injection molding defects are involved. These injection molding defects may be caused by external environmental factors or failure of the mold system during operation of the injection molding machine, which strongly affect the performance of the injection molding machine at the time of purchase when the evaluation criteria must be met. These injection molding defects can be generally classified into large-looking, eye-looking and small-looking characteristics, and specifically generally include injection molding dissatisfaction, flash, shrinkage, weld cross-talk, dimensional instability, warpage, strain, bubbles, cracks, delamination, non-gloss, difficulty in demolding, scorching, silver streaks, and the like.
In the past, most evaluation methods used only an SPC (Statistical Process Control ) system to control injection defects of an injection molding machine product, evaluate whether the product of the injection molding machine reaches a target value or is within a target domain on the corresponding injection defect, and thus control and evaluate the performance of a single injection molding machine to produce the product. Different injection molding defects are defined differently, the expected large characteristic, the expected mesh characteristic and the expected small characteristic of different injection molding defects of a product cannot be confused, so that test results of different injection molding defects of the product can be respectively compared, and then an evaluation report of a single machine is analyzed by using an effective technical research method, so that the advantages and disadvantages of different types of injection molding machines are determined, and the performance evaluation should be more convincing. However, performance assessment of individual injection molding machines does not meet industry standards for purchasing requirements for injection molding machines, and industry needs to be able to compare various "stand-alone" injection molding machine models. It is important that our method be able to perform interactive analysis and comparison, whether or not different injection molding products are used for performance evaluation of different types of injection molding machines.
It would be highly desirable to provide a method of identifiable performance evaluation for an injection molding machine whereby the above objectives are achieved.
Disclosure of Invention
The object of the present invention is to solve at least one of the disadvantages of the prior art and to provide a method for evaluating the identifiable properties of an injection molding machine.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the method for evaluating the identifiable performance of the injection molding machine comprises the following steps:
step 101, acquiring an injection molding defect data set of a cutting workpiece processed by a single type of injection molding machine tested at the time;
step 102, calculating the expected characteristic signal-to-noise ratio of the injection molding machine for the ith injection molding defect in the injection molding defect data setSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>；
Step 103, obtaining the signal-to-noise ratio of the expected large characteristic according to the analytic hierarchy processSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>And calculate the fused signal to noise ratio；
104, according to the fusion signal-to-noise ratio of the injection molding machine tested at the time to the ith injection molding defect in the injection molding defect data setObtaining the performance factor r of the injection molding machine tested at the time through conversion;
step 105, repeating the steps 101 to 103 for several times to obtain the corresponding performance factors of the m types of injection molding machines needing performance comparison，/>The larger the value of (c) represents the better the performance of the injection molding machine;
wherein the value range of i is [1, n ], n represents the total number of injection molding defects; the value range of j is [1, m ], and m is the type of the injection molding machine for testing.
Further, the signal-to-noise ratio of the large characteristic is calculated in step 102The mode of (2) is specifically obtained by the following formula:
；
calculating the signal to noise ratio of the eye observation characteristicThe mode of (2) is specifically obtained by the following formula:
；
calculating the signal-to-noise ratio of the small-looking characteristicThe mode of (2) is specifically obtained by the following formula:
；
wherein,,representation ofMeasuring the calculated quality characteristic value for the ith injection molding defect once; />Representing an average value of quality characteristics calculated for a plurality of measurements of an ith injection molding defect; n represents the total number of injection molding defects; />A target value representing a quality characteristic;standard deviation, and->。
Further, in step 103, the signal-to-noise ratio of the expected large characteristic is obtained according to the analytic hierarchy processSignal to noise ratio of eye observation characteristicSignal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>Specific modes of the weight A, B, C of (2) include the following:
step 201, selecting 6 experts, numbering 6 experts as A1-A6 in sequence, and obtaining the evaluation indexes of the experts、/>And +.>Establishing a judgment matrix according to the judgment result of the relative importance degree of the device, wherein the judgment matrix specifically comprises the following steps:
，/>，/>，/>，
，/>；
step 202, performing normalization processing on each column of elements of the judgment matrix, wherein general terms of the elements are as follows:
wherein the method comprises the steps ofRepresenting the elements of the ith row and the jth column of the judgment matrix;
step 203, performing row-by-row addition on the normalized judgment matrix of each column, and performing normalization processing to obtain a feature vector H of the judgment matrix, where the feature vector H is represented by the following formula:
step 204, obtaining the maximum feature root of the judgment matrix through the judgment matrix and the feature vector calculationWherein->Representation vector->T is [1,6 ]]At represents a judgment matrix of a corresponding number;
step 205, performing consistency test on the judgment matrix to obtain index weights A, B, C of all evaluation indexes, cr=ci/RI, where CI represents a consistency index,RI represents a random uniformity index.
Further, the signal to noise ratio is fused in step 104The conversion mode with the performance factor r is specifically expressed by the following formula:
where n represents the total number of injection molding defects,and representing the fusion signal-to-noise ratio of the injection molding machine to the ith injection molding defect in the injection molding defect data set.
Further, the injection defect data set in step 101 includes the following 14 injection defects, namely, n is 14:
injection molding dissatisfaction, edge overflow, shrinkage, welding, dimensional instability, warping, strain, air bubbles, cracking, delamination, non-gloss, difficult demolding, scorching, silver streaks.
The invention also proposes an identifiable performance evaluation system suitable for an injection molding machine, comprising:
the first acquisition module is used for acquiring an injection molding defect data set of a cutting workpiece processed by a single type of injection molding machine for the test;
a first calculation module for calculating the signal-to-noise ratio of the injection molding machine tested at this time to the expected large characteristic of the ith injection molding defect in the injection molding defect data set；
The second calculation module is used for calculating the signal-to-noise ratio of the injection molding machine for the eye-looking characteristic of the ith injection molding defect in the injection molding defect data set；
A third calculation module for calculating the signal-to-noise ratio of the injection molding machine for the injection molding defect of the ith injection molding defect in the injection molding defect data set；
A fourth calculation module for obtaining the signal-to-noise ratio of the expected large characteristic according to the analytic hierarchy processSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>Is A, B, C;
a fifth calculation module for calculating the fusion signal-to-noise ratio；
A first conversion module, configured to perform signal-to-noise ratio on an ith injection defect in the injection defect data set according to the injection molding machine tested at the timeObtaining the performance factor r of the injection molding machine tested at the time through conversion;
a second obtaining module, configured to repeat the steps 101 to 103 multiple times to obtain m types of injection molding machines corresponding to the m types of injection molding machines that need to perform performance comparisonPerformance factor，/>The larger the value of (c) represents the better the performance of the injection molding machine.
The invention also proposes an identifiable performance evaluation device suitable for an injection molding machine, comprising:
a memory for storing a computer program;
a processor for implementing the steps of any one of the method for evaluating identifiable properties for an injection molding machine when executing the computer program.
The present invention also proposes a computer-readable storage medium in which a computer program is stored, which computer program, when being executed by a processor, implements the steps of any of the identifiable performance evaluation methods applicable to injection molding machines.
The beneficial effects of the invention are as follows: according to the identifiable performance evaluation method, system and storage medium suitable for the injection molding machine, the injection molding defect data set of the cut workpiece processed by the single type of injection molding machine for single test is counted, the expected characteristic signal-to-noise ratio and the expected small characteristic signal-to-noise ratio of the injection molding defect are obtained through calculation, the fusion signal-to-noise ratio is obtained through calculation, the conversion of the fusion signal-to-noise ratio and the performance factor is carried out, and finally the performance evaluation of the injection molding machine is obtained through multiple tests and comparison of the performance factor, so that the analysis of the injection molding machine can be facilitated.
Drawings
FIG. 1 is a flow chart of an identifiable performance evaluation method of the present invention for an injection molding machine;
fig. 2 shows a schematic diagram of the recognizable performance evaluation apparatus of the present invention applied to an injection molding machine.
Detailed Description
The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Referring to fig. 1, a recognizable performance evaluation method suitable for an injection molding machine is provided, which includes the following steps:
step 101, acquiring an injection molding defect data set of a cutting workpiece processed by a single type of injection molding machine tested at the time;
step 102, calculating the expected characteristic signal-to-noise ratio of the injection molding machine for the ith injection molding defect in the injection molding defect data setSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>；
Step 103, obtaining the signal-to-noise ratio of the expected large characteristic according to the analytic hierarchy processSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>And calculate the fused signal to noise ratio；
104, according to the fusion signal-to-noise ratio of the injection molding machine tested at the time to the ith injection molding defect in the injection molding defect data setObtaining the performance factor r of the injection molding machine tested at the time through conversion;
step 105, repeating the steps 101 to 103 for several times to obtain the corresponding performance factors of the m types of injection molding machines needing performance comparison，/>The larger the value of (c) represents the better the performance of the injection molding machine;
wherein the value range of i is [1, n ], n represents the total number of injection molding defects; the value range of j is [1, m ], and m is the type of the injection molding machine for testing.
In this embodiment, an injection molding defect data set of a single type of injection molding machine processed cutting workpiece is counted, a large signal-to-noise ratio, a small signal-to-noise ratio and a small signal-to-noise ratio of the injection molding defect are obtained through calculation, a fusion signal-to-noise ratio is obtained through calculation, conversion of the fusion signal-to-noise ratio and a performance factor is carried out, and finally performance evaluation of the injection molding machine is obtained through multiple tests and comparison of the performance factors, so that analysis of the auxiliary injection molding machine can be facilitated.
As a preferred embodiment of the present invention, the signal-to-noise ratio of the large characteristic is calculated in step 102The mode of (2) is specifically obtained by the following formula:
；
calculating the signal to noise ratio of the eye observation characteristicThe mode of (2) is specifically obtained by the following formula:
；
calculating the signal-to-noise ratio of the small-looking characteristicThe mode of (2) is specifically obtained by the following formula:
；
wherein,,representing the quality characteristic value calculated by single measurement of the ith injection molding defect; />Representing an average value of quality characteristics calculated for a plurality of measurements of an ith injection molding defect; n represents the total number of injection molding defects; />A target value representing a quality characteristic;standard deviation, and->. As a preferred embodiment of the present invention, the signal-to-noise ratio of the desired large characteristic is obtained by the analytic hierarchy process in step 103>Sight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>Specific modes of the weight A, B, C of (2) include the following:
step 201, selecting6 specialists are determined, 6 specialists are numbered A1-A6 in sequence, and each evaluation index of the specialists is obtained、/>And +.>Establishing a judgment matrix according to the judgment result of the relative importance degree of the device, wherein the judgment matrix specifically comprises the following steps:
，/>，/>，/>，
，/>；
step 202, performing normalization processing on each column of elements of the judgment matrix, wherein general terms of the elements are as follows:
wherein the method comprises the steps ofRepresenting the elements of the ith row and the jth column of the judgment matrix;
step 203, performing row-by-row addition on the normalized judgment matrix of each column, and performing normalization processing to obtain a feature vector H of the judgment matrix, where the feature vector H is represented by the following formula:
step 204, obtaining the maximum feature root of the judgment matrix through the judgment matrix and the feature vector calculationWherein->Representation vector->T is [1,6 ]]At represents a judgment matrix of a corresponding number;
step 205, performing consistency test on the judgment matrix to obtain index weights A, B, C of all evaluation indexes, cr=ci/RI, where CI represents a consistency index,RI represents a random uniformity index.
Specifically, table 1 below shows the average random uniformity index RI
TABLE 1
When CR <0.10, the constructed judgment matrix is indicated to meet the consistency requirement, otherwise, the value of the judgment matrix is corrected.
For the signal to noise ratio of the hope-to-large characteristicSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>The importance levels among the three indexes are determined by a scale method, the quantization values are as shown in the following table 2,
TABLE 2
Finally obtaining the signal to noise ratio of the expected large characteristic through calculationSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>The weights A, B, C of (2) are as shown in table 3 below,
TABLE 3 Table 3
As a preferred embodiment of the present invention, the signal to noise ratio is fused in step 104The conversion mode with the performance factor r is specifically expressed by the following formula:
where n represents the total number of injection molding defects,and representing the fusion signal-to-noise ratio of the injection molding machine to the ith injection molding defect in the injection molding defect data set. Fusion signal-to-noise ratio +.>The method has the advantages of convenient calculation and high conversion correlation for the conversion of the performance factor r.
As a preferred embodiment of the present invention, the injection defect data set in step 101 includes the following 14 injection defects, namely, n is 14:
injection molding dissatisfaction, edge overflow, shrinkage, welding, dimensional instability, warping, strain, air bubbles, cracking, delamination, non-gloss, difficult demolding, scorching, silver streaks.
The above 14 injection molding defect data are basically common data for ordinary testing, and are representative.
The invention also proposes an identifiable performance evaluation system suitable for an injection molding machine, comprising:
the first acquisition module is used for acquiring an injection molding defect data set of a cutting workpiece processed by a single type of injection molding machine for the test;
a first calculation module for calculating the signal-to-noise ratio of the injection molding machine tested at this time to the expected large characteristic of the ith injection molding defect in the injection molding defect data set；
The second calculation module is used for calculating the signal-to-noise ratio of the injection molding machine for the eye-looking characteristic of the ith injection molding defect in the injection molding defect data set；
A third calculation module for calculating the signal-to-noise ratio of the injection molding machine for the injection molding defect of the ith injection molding defect in the injection molding defect data set；
A fourth calculation module for obtaining the signal-to-noise ratio of the expected large characteristic according to the analytic hierarchy processSight characteristic signal to noise ratio->Signal to noise ratio of the hope-small characteristic>For the fusion signal to noise ratio +.>Is A, B, C;
a fifth calculation module for calculating the fusion signal-to-noise ratio；
A first conversion module, configured to perform signal-to-noise ratio on an ith injection defect in the injection defect data set according to the injection molding machine tested at the timeObtaining the performance factor r of the injection molding machine tested at the time through conversion;
a second obtaining module, configured to repeat the steps 101 to 103 multiple times to obtain performance factors corresponding to m types of injection molding machines that need performance comparison，/>The larger the value of (c) represents the better the performance of the injection molding machine.
Referring to fig. 2, the present invention also proposes an identifiable performance evaluation apparatus suitable for an injection molding machine, comprising:
a memory 100 for storing a computer program;
a processor 200 for implementing the steps of any one of the method for evaluating identifiable properties for an injection molding machine when executing the computer program.
The present invention also proposes a computer-readable storage medium in which a computer program is stored, which computer program, when being executed by a processor, implements the steps of any of the identifiable performance evaluation methods applicable to injection molding machines.
The computer readable storage medium referred to in this application includes Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The description of the relevant parts in the method, the device and the computer readable storage medium for evaluating the identifiable performance of the injection molding machine provided in the embodiment of the present application is referred to in the detailed description of the corresponding parts in the method for evaluating the identifiable performance of the injection molding machine provided in the embodiment of the present application, and is not repeated herein. In addition, the parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of the corresponding technical solutions in the prior art, are not described in detail, so that redundant descriptions are avoided.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (5)

1.适用于注塑机的可识别性能评估方法，其特征在于，包括以下步骤：1. An identifiable performance evaluation method suitable for injection molding machines, characterized in that it comprises the following steps: 步骤101、获取本次测试的单种类型的注塑机加工的切削工件的注塑缺陷数据集；Step 101, obtaining the injection molding defect data set of the cutting workpiece processed by a single type of injection molding machine in this test; 步骤102、计算本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的望大特性信噪比、望目特性信噪比/>以及望小特性信噪比/>；Step 102, calculate the maximum characteristic signal-to-noise ratio of the injection molding machine tested this time for the i-th injection defect in the injection defect data set , Wangmu characteristic SNR/> And look at the small characteristic signal-to-noise ratio /> ; 步骤103、根据层次分析法得到望大特性信噪比、望目特性信噪比/>以及望小特性信噪比/>分别对融合信噪比/>的权重A、B、C，并计算得到所述融合信噪比；Step 103, according to the analytic hierarchy process, obtain the signal-to-noise ratio of the large characteristic , Wangmu characteristic SNR/> And look at the small characteristic signal-to-noise ratio /> Fusion SNR /> weights A, B, C, and calculate the fusion signal-to-noise ratio ; 步骤104、根据本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的融合信噪比，通过转换得到本次测试的注塑机的性能因子r；Step 104, according to the fusion signal-to-noise ratio of the i-th injection molding defect in the injection molding defect data set according to the injection molding machine tested this time , the performance factor r of the injection molding machine in this test is obtained by conversion; 步骤105、多次重复上述步骤101至步骤103，得到需要进行性能比较的m种类型的注塑机对应的性能因子，/>的值越大代表注塑机的性能越好；Step 105, repeat the above steps 101 to 103 multiple times to obtain the performance factors corresponding to the m types of injection molding machines that need to be compared in performance , /> The larger the value, the better the performance of the injection molding machine; 其中i的取值范围为[1,n]，n表示注塑缺陷总数目；j的取值范围为[1，m]，m为进行测试的注塑机的种类；The value range of i is [1,n], n represents the total number of injection molding defects; the value range of j is [1, m], m is the type of injection molding machine to be tested; 上述步骤102中计算望大特性信噪比的方式具体通过以下公式获得：In the above-mentioned step 102, calculate the expected large characteristic signal-to-noise ratio The method is specifically obtained by the following formula: ； ; 计算望目特性信噪比的方式具体通过以下公式获得：Calculate the signal-to-noise ratio of the look-and-eye characteristic The method is specifically obtained by the following formula: ； ; 计算望小特性信噪比的方式具体通过以下公式获得：Calculating the Signal-to-Noise Ratio of Smaller Features The method is specifically obtained by the following formula: ； ; 其中，表示对第i个注塑缺陷单次测量计算的品质特性值；/>表示对第i个注塑缺陷多次测量计算的品质特性的平均值；n表示注塑缺陷总数目；/>表示品质特性的目标值；/>标准偏差，且/>；in, Indicates the quality characteristic value calculated for a single measurement of the i-th injection defect; /> Indicates the average value of the quality characteristics calculated by multiple measurements of the i-th injection defect; n indicates the total number of injection defects; /> Indicates the target value of the quality characteristic; /> standard deviation, and/> ; 上述步骤103中根据层次分析法得到望大特性信噪比、望目特性信噪比/>以及望小特性信噪比/>分别对融合信噪比/>的权重A、B、C的具体方式包括以下：In the above-mentioned step 103, according to the analytic hierarchy process, the signal-to-noise ratio of the expected large characteristic is obtained , Wangmu characteristic SNR/> And look at the small characteristic signal-to-noise ratio /> Fusion SNR /> The specific ways of weights A, B, and C include the following: 步骤201、选定6个专家，将6个所述专家依次编号为A1-A6，获取专家对各个评价指标、以及/>的相对重要程度的判断结果，建立判断矩阵，所述判断矩阵具体包括以下：Step 201, select 6 experts, number the 6 experts as A1-A6 in turn, and obtain the evaluation indicators of each expert , and /> The judgment result of the relative importance degree of the relative importance degree, set up judgment matrix, described judgment matrix specifically comprises the following: ，/>，/>，/>， , /> , /> , /> , ，/>； , /> ; 步骤202、将判断矩阵的每一列元素进行归一化处理，其元素的一般项为：Step 202, normalize the elements in each column of the judgment matrix, and the general items of the elements are: 其中表示判断矩阵第i行，第j列的元素；in Represents the element in row i and column j of the judgment matrix; 步骤203、将各列归一化后的判断矩阵进行按行相加的操作，并进行归一化处理得到判断矩阵的特征向量H，用以下式子表示：Step 203, perform row-wise addition operation on the judgment matrix after the normalization of each column, and perform normalization processing to obtain the eigenvector H of the judgment matrix, which is represented by the following formula: 步骤204、通过判断矩阵以及特征向量计算得到判断矩阵的最大特征根，其中/>表示向量/>的第i个元素，t取[1，6]，At代表相应编号的判断矩阵；Step 204, obtain the maximum eigenvalue of the judgment matrix by calculating the judgment matrix and the eigenvector , where /> represents a vector /> The i-th element of , t is [1, 6], At represents the judgment matrix of the corresponding number; 步骤205、对所述判断矩阵进行一致性检验得到各个评价指标的指标权重A、B、C，CR=CI/RI，其中CI表示一致性指标，，RI表示随机一致性指标；Step 205, perform a consistency check on the judgment matrix to obtain the index weights A, B, and C of each evaluation index, CR=CI/RI, where CI represents the consistency index, , RI means random consistency index; 上述步骤104中融合信噪比与性能因子r之间的转换方式具体通过如下式子表示：Fusion signal-to-noise ratio in the above step 104 The conversion method between R and the performance factor r is specifically expressed by the following formula: 其中n表示注塑缺陷总数目，表示注塑机对注塑缺陷数据集中的第i个注塑缺陷的融合信噪比。where n represents the total number of injection molding defects, Indicates the fusion signal-to-noise ratio of the injection molding machine for the i-th injection defect in the injection defect data set. 2.根据权利要求1所述的适用于注塑机的可识别性能评估方法，其特征在于，上述步骤101中的注塑缺陷数据集包括以下14种注塑缺陷，即n取14：2. The identifiable performance evaluation method suitable for injection molding machines according to claim 1, wherein the injection molding defect data set in the above step 101 includes the following 14 types of injection molding defects, that is, n is 14: 注塑不满、溢边、缩水、熔接横、尺寸不稳定、翘曲、拉伤、气泡、龟裂、分层、不光泽、脱模困难、烧焦、银丝纹。Injection dissatisfaction, overflow, shrinkage, horizontal welding, dimensional instability, warping, strain, air bubbles, cracks, delamination, dullness, difficult demoulding, burnt, silver streaks. 3.适用于注塑机的可识别性能评估系统，其特征在于，实现如权利要求1至2任一项所述的适用于注塑机的可识别性能评估方法，包括：3. An identifiable performance evaluation system suitable for injection molding machines, characterized in that it implements the identifiable performance evaluation method suitable for injection molding machines as described in any one of claims 1 to 2, including: 第一获取模块，用于获取本次测试的单种类型的注塑机加工的切削工件的注塑缺陷数据集；The first acquisition module is used to acquire the injection molding defect data set of the cutting workpiece processed by a single type of injection molding machine in this test; 第一计算模块，用于计算本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的望大特性信噪比；The first calculation module is used to calculate the maximum characteristic signal-to-noise ratio of the injection molding machine in this test for the i-th injection defect in the injection defect data set ; 第二计算模块，用于计算本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的望目特性信噪比；The second calculation module is used to calculate the signal-to-noise ratio of the eye-to-eye characteristic of the injection molding machine in this test for the i-th injection defect in the injection defect data set ; 第三计算模块，用于计算本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的望小特性信噪比；The third calculation module is used to calculate the expected minimum characteristic signal-to-noise ratio of the injection molding machine in this test for the i-th injection defect in the injection defect data set ; 第四计算模块，用于根据层次分析法得到望大特性信噪比、望目特性信噪比/>以及望小特性信噪比/>分别对融合信噪比/>的权重A、B、C；The fourth calculation module is used to obtain the signal-to-noise ratio of the expected large characteristic according to the analytic hierarchy process , Wangmu characteristic SNR/> And look at the small characteristic signal-to-noise ratio /> Fusion SNR /> The weights A, B, C of 第五计算模块，用于计算得到所述融合信噪比；The fifth calculation module is used to calculate and obtain the fusion signal-to-noise ratio ; 第一转换模块，用于根据本次测试的注塑机对注塑缺陷数据集中的第i个注塑缺陷的信噪比，通过转换得到本次测试的注塑机的性能因子r；The first conversion module is used for the signal-to-noise ratio of the i-th injection defect in the injection defect data set according to the injection molding machine tested this time , the performance factor r of the injection molding machine in this test is obtained by conversion; 第二获取模块，用于多次重复上述步骤101至步骤103，得到需要进行性能比较的m种类型的注塑机对应的性能因子，/>的值越大代表注塑机的性能越好。The second acquisition module is used to repeat the above steps 101 to 103 multiple times to obtain the performance factors corresponding to m types of injection molding machines that need to be compared in performance , /> The larger the value, the better the performance of the injection molding machine. 4.适用于注塑机的可识别性能评估设备，其特征在于，包括：4. The identifiable performance evaluation equipment suitable for injection molding machines, characterized in that it includes: 存储器，用于存储计算机程序；memory for storing computer programs; 处理器，用于执行所述计算机程序时实现如权利要求1-2任一项所述的适用于注塑机的可识别性能评估方法的步骤。A processor, configured to implement the steps of the identifiable performance evaluation method applicable to injection molding machines according to any one of claims 1-2 when executing the computer program. 5.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-2任一项所述的适用于注塑机的可识别性能评估方法的步骤。5. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the identifiable performance evaluation method suitable for injection molding machines according to any one of claims 1-2 are realized.
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