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 PDFInfo
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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
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, the liquid crystal display device comprises a liquid crystal display device,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, the liquid crystal display device comprises a liquid crystal display device,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. The identifiable performance evaluation method suitable for the injection molding machine is characterized by comprising the following steps of:
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;
calculating the signal-to-noise ratio of the large characteristic 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, the liquid crystal display device comprises a liquid crystal display device,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->；
In the step 103, the signal-to-noise ratio with the expected large characteristic is obtained 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 +.>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 consistency index;
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.
2. The method of claim 1, wherein the injection defect data set in step 101 includes 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.
3. An identifiable performance evaluation system suitable for an injection molding machine, characterized in that it implements an identifiable performance evaluation method suitable for an injection molding machine according to any one of claims 1 to 2, 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.
4. An identifiable characteristic evaluation apparatus suitable for an injection molding machine, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for identifiable performance evaluation of an injection molding machine according to any one of claims 1-2 when executing the computer program.
5. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, implements the steps of the method for evaluating the identifiable performance of an injection molding machine according to any one of claims 1-2.
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