CN112330233A - Injection molding product quality detection method based on data model - Google Patents
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- 238000013499 data model Methods 0.000 title claims abstract description 55
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Abstract
The invention belongs to the field of industrial internet manufacturing, relates to a quality detection technology, and particularly relates to a data model-based injection molding product quality detection method, which specifically comprises the following steps: step S1: establishing a data model, and setting data model variables, wherein the data model variables comprise five variable units of speed, period, pressure, temperature and position; step S2: a user selects a data model variable on an application system, and sets an upper limit value, a lower limit value and a quality influence weight of the variable; step S3: starting a data model, and analyzing the product quality according to the data model to detect a defective product while the system receives the acquired variable data; step S4: and analyzing to obtain whether the detected product is a good product, a defective product or a suspected defective product, and performing secondary quality detection on the defective product. According to the invention, the quality of the detected product is analyzed and judged by comparing the data model variable with the corresponding bad interval and suspected interval, so that the detection efficiency is greatly improved, and the labor force is liberated.
Description
Technical Field
The invention belongs to the field of industrial internet manufacturing, relates to a quality detection technology, and particularly relates to a data model-based injection molding product quality detection method.
Background
The injection molding process is a process for manufacturing a semi-finished product with a certain shape by pressurizing, injecting, cooling, separating and the like molten raw materials. The injection molding process of the plastic part mainly comprises six stages of mold closing, filling, pressure maintaining, cooling, mold opening, demolding and the like, the debugging process of the injection molding process is a process with weak theory and strong experience, a standard process range can be determined by an injection molding theoretical process master, then fine adjustment is carried out in the standard process range, however, due to the fact that the management levels of all factories are different, the process master is rare, the levels of process learners are different, when the product has quality problems and needs to be adjusted, the process master is not present, process data finally adjusted by the process learners possibly exceed the standard process range of the product, and after the product is placed for a period of time, problems such as product size ultra-difference and the like can be caused due to internal stress release and the like, and defective products in batches can be generated. Therefore, the injection molding process point inspection is carried out in factories producing precise rubber and plastic products to ensure that the injection molding process parameters do not exceed the standard process range.
The injection molding process point inspection is an injection molding production process management method which inspects process parameters specified by injection molding equipment according to a certain standard and a certain period so as to discover process abnormality early and correct the process abnormality in time, so that the process parameters of the injection molding equipment can be kept within the standard range of the process parameters and the stable product quality is ensured.
Present injection moulding technology point is examined all through the people, an injection moulding equipment has about eighty process parameters to examine when the point is examined, the point of an equipment is examined the time and is about ten minutes, a small-size injection moulding processing workshop has twenty injection moulding equipment usually, it needs two hundred minutes about to accomplish once full workshop injection moulding technology point and examine so, very consume human cost and time cost, the work load and the cost of realizing examining entirely are all very big, and it is very poor to examine ageing, the condition of appearing lou examining also takes place occasionally.
Disclosure of Invention
The invention aims to provide a data model-based injection molding product quality detection method, which is used for solving the problems that the spot inspection of the current injection molding process is carried out by people, the labor cost and the time cost are very consumed, the workload and the cost for realizing the full inspection are very large, and the inspection timeliness is very poor;
the technical problems to be solved by the invention are as follows:
(1) how to provide a quality detection method of an injection molding product which can replace manual detection;
(2) how to provide a quality detection method with higher detection efficiency.
The purpose of the invention can be realized by the following technical scheme:
a data model-based injection molding product quality detection method specifically comprises the following steps:
step S1: establishing a data model, and setting data model variables, wherein the data model variables comprise five variable units of speed, period, pressure, temperature and position;
step S2: a user selects a data model variable on an application system, and sets an upper limit value, a lower limit value and a quality influence weight of the variable;
step S3: starting a data model, and analyzing the product quality according to the data model to detect a defective product while the system receives the acquired variable data;
step S4: and analyzing to obtain whether the detected product is a good product, a defective product or a suspected defective product, and performing secondary quality detection on the defective product.
Further, the specific process of acquiring the variable data in step S3 includes the following steps:
step V1: obtaining an injection speed variable and a screw rotating speed variable of an injection molding process, respectively marking the injection speed variable and the screw rotating speed variable as ZS and LS, and obtaining the injection speed variable and the screw rotating speed variable through a formula
Obtaining a speed variable SD of the injection molding process, wherein alpha 1, alpha 2 and alpha 3 are all preset proportionality coefficients;
step V2: obtaining a molding cycle variable of an injection molding process, and marking the molding cycle variable as CZ;
step V3: obtaining an injection pressure variable and a pressure maintaining pressure variable of an injection molding process, respectively marking the injection pressure variable and the pressure maintaining pressure variable as ZY and BY through a formula
Obtaining a pressure variable YL of the injection molding process, wherein beta 1, beta 2 and beta 3 are all preset proportionality coefficients;
step V4: obtaining a melt adhesive temperature variable and a charging basket temperature variable of an injection molding process, respectively marking the melt adhesive temperature variable and the charging basket temperature variable as RW and LW, and calculating the melt adhesive temperature variable and the charging basket temperature variable according to a formula
Obtaining a temperature variable WD of the injection molding process; wherein gamma 1, gamma 2 and gamma 3 are all preset proportionality coefficients;
v5: and acquiring a screw position variable of the injection molding process, and marking the screw position variable as LW.
Further, the detecting and analyzing process of the defective product in the step S3 includes the following steps:
step P1: marking the upper limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As As, Bs, Cs, Ds and Es respectively, and marking the lower limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As Ax, Bx, Cx, Dx and Ex respectively;
step P2: dividing a variable range formed by upper and lower limit values of a speed variable into i speed variable intervals, i =1, 2, … …, n, setting defective product speed intervals BLs and suspected speed intervals YSs in Ai speed intervals, comparing a speed variable SD of a detected workpiece with the defective product speed intervals BLs and the suspected speed intervals YSs, and judging that the detected workpiece is a speed defective product when the speed variable SD is larger than or equal to the minimum value of the defective product speed intervals BLs; when the minimum value of the suspected speed interval YSs is less than or equal to the speed variable SD which is less than the minimum value of the defective speed interval BLs, the detected workpiece is judged to be a suspected speed defective product; when the speed variable SD is smaller than the minimum value of the suspected speed interval YSs, judging that the detected workpiece is good;
step P3: dividing a variable range formed by upper and lower limit values of a forming period variable into m forming period variable intervals, wherein m =1, 2, … …, n, setting a defective product forming period interval BLz and a suspected forming period interval YSz in Bm forming period intervals, comparing a forming period variable CZ of a detected workpiece with a defective product forming period interval BLz and a suspected forming period interval YSz, and judging that the detected workpiece is a forming period defective product when the forming period variable CZ is larger than or equal to the minimum value of the defective product forming period interval BLz; when the minimum value of the suspected molding cycle interval YSz is less than or equal to the molding cycle variable CZ which is less than the minimum value of the defective molding cycle interval BLz, determining that the detected workpiece is a suspected molding cycle defective product; when the molding cycle variable CZ is smaller than the minimum value of the suspected molding cycle interval YSz, judging that the detected workpiece is a good product;
step P4: dividing a variable range formed by upper and lower limit values of a pressure variable into t pressure variable intervals, t =1, 2, … …, n, setting a defective product pressure interval BLy and a suspected speed interval YSy in Ct pressure intervals, comparing a pressure variable YL of a detected workpiece with the defective product pressure interval BLy and the suspected speed interval YSy, and judging that the detected workpiece is a pressure defective product when the pressure variable YL is not less than the minimum value of the defective product pressure interval BLy; when the minimum value of the suspected pressure interval YSy is less than or equal to the minimum value of the pressure variable YL < the defective product pressure interval BLy, the detected workpiece is judged to be a suspected pressure defective product; when the pressure variable YL is smaller than the minimum value of the suspected pressure interval YSy, the detected workpiece is judged to be good;
step P5: dividing a variable range consisting of upper and lower limit values of a temperature variable into u temperature variable intervals, u =1, 2, … …, n, setting a defective product temperature interval Blw and a suspected speed interval YSw in the Du temperature intervals, comparing a temperature variable WD of a detected workpiece with a defective product temperature interval BLw and a suspected speed interval YSw, and judging that the detected workpiece is a temperature defective product when the temperature variable WD is not less than the minimum value of the defective product temperature interval BLw; when the minimum value of the suspected temperature interval YSw is less than or equal to the minimum value of the temperature variable WD < the minimum value of the defective product temperature interval BLw, the detected workpiece is judged to be a suspected temperature defective product; when the temperature variable WD is smaller than the minimum value of the suspected temperature interval YSw, judging that the detected workpiece is good;
step P6: dividing a variable range formed by upper and lower limit values of a screw position variable into w screw position variable intervals, w =1, 2, … …, n, setting a defective screw position interval BLl and a suspected speed interval YSl in the Ew screw position intervals, comparing a screw position variable LW of a detected workpiece with the defective screw position interval BLl and the suspected screw position interval YSlw, and judging that the detected workpiece is a screw position defective workpiece when the screw position variable LW is not less than the minimum value of the defective screw position interval BLl; when the minimum value of the suspected screw position interval YSl is not more than the minimum value of the screw position variable LW < the minimum value of the defective screw position interval BLl, the detected workpiece is judged to be a defective product at the suspected screw position; when the screw position variable LW is smaller than the minimum value of the suspected screw position interval YSl, judging that the detected workpiece is good;
step P7: and carrying out secondary detection on all detection workpieces which are judged to be suspected defective products.
Further, the secondary detection process in step P7 includes the following steps:
q1: obtaining the number of times of judging that the suspected defective products are judged to be the suspected defective products, marking the number of times of judging as t, judging that the suspected defective products are defective products when t is more than or equal to 3, and not executing the next step; when t is less than 3, executing the next step;
q2: obtaining a speed variable SD, a forming period variable CZ, a pressure variable YL, a temperature variable WD and a screw position variable LW of suspected defective products through formulas
Obtaining a bad coefficient BLX of suspected defective products;
q3: when BLX is larger than or equal to BLXmax, judging the suspected defective product as a defective product; and when the BLX is less than BLXmax, judging that the suspected defective product is a good product, wherein the BLXmax is a preset bad coefficient threshold value.
The invention has the beneficial effects that: the invention has the following beneficial effects:
1. the quality detection method comprises the steps of obtaining five data model variables of a detected product, sequentially comparing the five data model variables of the detected product with corresponding bad intervals and suspected intervals, and analyzing and judging the quality of the detected product, so that the quality detection of the detected product is completed, the existing manual detection method is replaced, the detection efficiency is greatly improved, and the labor force is liberated;
2. by further detecting the suspected defective products, the defective products and the good products in the suspected defective products can be screened and washed, so that the detection method can be used for accurately and error-free detection of the detected products, and the accuracy of the detection result is improved;
3. the product quality detection method based on the data model is not specific to a certain product, has wide adaptability and high efficiency, and is characterized in that a user selects variables of the data model according to the characteristics of the product, sets the upper and lower limit values and the quality influence weight of the variables, realizes the product quality detection based on the data model, analyzes the occurrence probability of defective products of the product quality according to the data model, realizes the first inspection and the full inspection of the product detection, realizes the automatic classification of the defective products, is simple in operation in the whole process, and can be reused and flexibly adjusted by using the data model of the same type of product.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a logic diagram of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an injection molding product quality detection method based on a data model is provided, where an MES system is a set of production informatization management system oriented to a workshop execution layer of a manufacturing enterprise, and the MES system may provide management modules for the enterprise, including manufacturing data management, planning and scheduling management, production scheduling management, inventory management, quality management, human resource management, work center/equipment management, tool and tool management, purchase management, cost management, project bulletin board management, production process control, bottom layer data integration analysis, upper layer data integration decomposition, and the like, and create a solid, reliable, comprehensive, and feasible manufacturing collaborative management platform for the enterprise. Checking the contents in a MES system production process control module by an injection molding process; the specific detection method comprises the following steps:
step S1: establishing a data model, and setting data model variables, wherein the data model variables comprise five variable units of speed, period, pressure, temperature and position, and the 5 variable section elements of the data model can comprise specific injection molding process variables influencing the product quality, such as the speed comprising injection speed, screw rotating speed and the like, the period comprising a molding period and the like, the pressure comprising injection pressure, pressure maintaining pressure and the like, the temperature comprising melt adhesive temperature, barrel temperature and the like, and the position comprising screw position and the like; the defective product probability uses a weight calculation method, and is simple and practical;
step S2: the method comprises the following steps that a user selects data model variables on an application system, sets upper and lower limit values and quality influence weights of the variables, selects the variables of a data model according to the characteristics of a product, sets the upper and lower limit values and the quality influence weights of the variables, and achieves product quality detection based on the data model;
step S3: starting a data model, analyzing the product quality according to the data model while the system receives the acquired variable data to detect a defective product, realizing the first inspection and the full inspection of the product detection, and realizing the automatic classification of the defective product, wherein the whole process is simple to operate, and the data model of the same type of product can be reused and can be elastically adjusted;
step S4: and analyzing to obtain whether the detected product is a good product, a defective product or a suspected defective product, and performing secondary quality detection on the defective product.
The specific process of acquiring the variable data in step S3 includes the following steps:
step V1: obtaining an injection speed variable and a screw rotating speed variable of an injection molding process, respectively marking the injection speed variable and the screw rotating speed variable as ZS and LS, and obtaining the injection speed variable and the screw rotating speed variable through a formula
Obtaining a speed variable SD of the injection molding process, wherein alpha 1, alpha 2 and alpha 3 are all preset proportionality coefficients;
step V2: obtaining a molding cycle variable of an injection molding process, and marking the molding cycle variable as CZ;
step V3: obtaining an injection pressure variable and a pressure maintaining pressure variable of an injection molding process, respectively marking the injection pressure variable and the pressure maintaining pressure variable as ZY and BY through a formula
Obtaining a pressure variable YL of the injection molding process, wherein beta 1, beta 2 and beta 3 are all preset proportionality coefficients;
step V4: obtaining a melt adhesive temperature variable and a charging basket temperature variable of an injection molding process, respectively marking the melt adhesive temperature variable and the charging basket temperature variable as RW and LW, and calculating the melt adhesive temperature variable and the charging basket temperature variable according to a formula
Obtaining a temperature variable WD of the injection molding process; wherein gamma 1, gamma 2 and gamma 3 are all preset proportionality coefficients;
v5: and acquiring a screw position variable of the injection molding process, and marking the screw position variable as LW.
The detection and analysis process of the defective products in the step S3 includes the following steps:
step P1: marking the upper limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As As, Bs, Cs, Ds and Es respectively, and marking the lower limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As Ax, Bx, Cx, Dx and Ex respectively;
step P2: dividing a variable range formed by upper and lower limit values of a speed variable into i speed variable intervals, i =1, 2, … …, n, setting defective product speed intervals BLs and suspected speed intervals YSs in Ai speed intervals, comparing a speed variable SD of a detected workpiece with the defective product speed intervals BLs and the suspected speed intervals YSs, and judging that the detected workpiece is a speed defective product when the speed variable SD is larger than or equal to the minimum value of the defective product speed intervals BLs; when the minimum value of the suspected speed interval YSs is less than or equal to the speed variable SD which is less than the minimum value of the defective speed interval BLs, the detected workpiece is judged to be a suspected speed defective product; when the speed variable SD is smaller than the minimum value of the suspected speed interval YSs, judging that the detected workpiece is good;
step P3: dividing a variable range formed by upper and lower limit values of a forming period variable into m forming period variable intervals, wherein m =1, 2, … …, n, setting a defective product forming period interval BLz and a suspected forming period interval YSz in Bm forming period intervals, comparing a forming period variable CZ of a detected workpiece with a defective product forming period interval BLz and a suspected forming period interval YSz, and judging that the detected workpiece is a forming period defective product when the forming period variable CZ is larger than or equal to the minimum value of the defective product forming period interval BLz; when the minimum value of the suspected molding cycle interval YSz is less than or equal to the molding cycle variable CZ which is less than the minimum value of the defective molding cycle interval BLz, determining that the detected workpiece is a suspected molding cycle defective product; when the molding cycle variable CZ is smaller than the minimum value of the suspected molding cycle interval YSz, judging that the detected workpiece is a good product;
step P4: dividing a variable range formed by upper and lower limit values of a pressure variable into t pressure variable intervals, t =1, 2, … …, n, setting a defective product pressure interval BLy and a suspected speed interval YSy in Ct pressure intervals, comparing a pressure variable YL of a detected workpiece with the defective product pressure interval BLy and the suspected speed interval YSy, and judging that the detected workpiece is a pressure defective product when the pressure variable YL is not less than the minimum value of the defective product pressure interval BLy; when the minimum value of the suspected pressure interval YSy is less than or equal to the minimum value of the pressure variable YL < the defective product pressure interval BLy, the detected workpiece is judged to be a suspected pressure defective product; when the pressure variable YL is smaller than the minimum value of the suspected pressure interval YSy, the detected workpiece is judged to be good;
step P5: dividing a variable range consisting of upper and lower limit values of a temperature variable into u temperature variable intervals, u =1, 2, … …, n, setting a defective product temperature interval Blw and a suspected speed interval YSw in the Du temperature intervals, comparing a temperature variable WD of a detected workpiece with a defective product temperature interval BLw and a suspected speed interval YSw, and judging that the detected workpiece is a temperature defective product when the temperature variable WD is not less than the minimum value of the defective product temperature interval BLw; when the minimum value of the suspected temperature interval YSw is less than or equal to the minimum value of the temperature variable WD < the minimum value of the defective product temperature interval BLw, the detected workpiece is judged to be a suspected temperature defective product; when the temperature variable WD is smaller than the minimum value of the suspected temperature interval YSw, judging that the detected workpiece is good;
step P6: dividing a variable range formed by upper and lower limit values of a screw position variable into w screw position variable intervals, w =1, 2, … …, n, setting a defective screw position interval BLl and a suspected speed interval YSl in the Ew screw position intervals, comparing a screw position variable LW of a detected workpiece with the defective screw position interval BLl and the suspected screw position interval YSlw, and judging that the detected workpiece is a screw position defective workpiece when the screw position variable LW is not less than the minimum value of the defective screw position interval BLl; when the minimum value of the suspected screw position interval YSl is not more than the minimum value of the screw position variable LW < the minimum value of the defective screw position interval BLl, the detected workpiece is judged to be a defective product at the suspected screw position; when the screw position variable LW is smaller than the minimum value of the suspected screw position interval YSl, judging that the detected workpiece is good;
step P7: carrying out secondary detection on all detection workpieces judged to be suspected defective products; the secondary detection process comprises the following steps:
step P71: obtaining the number of times of judging that the suspected defective products are judged to be the suspected defective products, marking the number of times of judging as t, judging that the suspected defective products are defective products when t is more than or equal to 3, and not executing the next step; when t is less than 3, executing the next step;
step P72: obtaining a speed variable SD, a forming period variable CZ, a pressure variable YL, a temperature variable WD and a screw position variable LW of suspected defective products through formulas
Obtaining a bad coefficient BLX of suspected defective products;
step P73: when BLX is larger than or equal to BLXmax, judging the suspected defective product as a defective product; and when the BLX is less than BLXmax, judging that the suspected defective product is a good product, wherein the BLXmax is a preset bad coefficient threshold value.
The data acquisition can use the application based on protocols such as OPCDA, OPCUA, ModBusTCP and the like, the near-real-time collection of process data is realized, and the high efficiency of quality detection is achieved by combining a data model.
The product quality detection method based on the data model is not specific to a certain product, and has wide adaptability and high efficiency.
The invention has the following beneficial effects:
1. the quality detection method comprises the steps of obtaining five data model variables of a detected product, sequentially comparing the five data model variables of the detected product with corresponding bad intervals and suspected intervals, and analyzing and judging the quality of the detected product, so that the quality detection of the detected product is completed, the existing manual detection method is replaced, the detection efficiency is greatly improved, and the labor force is liberated;
2. by further detecting the suspected defective products, the defective products and the good products in the suspected defective products can be screened and washed, so that the detection method can be used for accurately and error-free detection of the detected products, and the accuracy of the detection result is improved;
3. the product quality detection method based on the data model is not specific to a certain product, has wide adaptability and high efficiency, and is characterized in that a user selects variables of the data model according to the characteristics of the product, sets the upper and lower limit values and the quality influence weight of the variables, realizes the product quality detection based on the data model, analyzes the occurrence probability of defective products of the product quality according to the data model, realizes the first inspection and the full inspection of the product detection, realizes the automatic classification of the defective products, is simple in operation in the whole process, and can be reused and flexibly adjusted by using the data model of the same type of product.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The above formulas are all numerical values obtained by normalization processing, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. A data model-based injection molding product quality detection method is characterized by comprising the following steps:
step S1: establishing a data model, and setting data model variables, wherein the data model variables comprise five variable units of speed, period, pressure, temperature and position;
step S2: a user selects a data model variable on an application system, and sets an upper limit value, a lower limit value and a quality influence weight of the variable;
step S3: starting a data model, and analyzing the product quality according to the data model to detect a defective product while the system receives the acquired variable data;
step S4: and analyzing to obtain whether the detected product is a good product, a defective product or a suspected defective product, and performing secondary quality detection on the defective product.
2. The method for detecting the quality of the injection molding product based on the data model as claimed in claim 1, wherein the specific process of obtaining the variable data in the step S3 includes the following steps:
step V1: obtaining an injection speed variable and a screw rotating speed variable of an injection molding process, respectively marking the injection speed variable and the screw rotating speed variable as ZS and LS, and obtaining the injection speed variable and the screw rotating speed variable through a formula
Obtaining a speed variable SD of the injection molding process, wherein alpha 1, alpha 2 and alpha 3 are all preset proportionality coefficients;
step V2: obtaining a molding cycle variable of an injection molding process, and marking the molding cycle variable as CZ;
step V3: obtaining an injection pressure variable and a pressure maintaining pressure variable of an injection molding process, respectively marking the injection pressure variable and the pressure maintaining pressure variable as ZY and BY through a formula
Obtaining the pressure variable YL of the injection molding process, wherein beta 1, beta 2 and beta 3All are preset proportionality coefficients;
step V4: obtaining a melt adhesive temperature variable and a charging basket temperature variable of an injection molding process, respectively marking the melt adhesive temperature variable and the charging basket temperature variable as RW and LW, and calculating the melt adhesive temperature variable and the charging basket temperature variable according to a formula
Obtaining a temperature variable WD of the injection molding process; wherein gamma 1, gamma 2 and gamma 3 are all preset proportionality coefficients;
v5: and acquiring a screw position variable of the injection molding process, and marking the screw position variable as LW.
3. The method of claim 2, wherein the step of detecting and analyzing defective products in step S3 comprises the steps of:
step P1: marking the upper limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As As, Bs, Cs, Ds and Es respectively, and marking the lower limit values of the set speed variable, period variable, pressure variable, temperature variable and position variable As Ax, Bx, Cx, Dx and Ex respectively;
step P2: dividing a variable range formed by upper and lower limit values of a speed variable into i speed variable intervals, i =1, 2, … …, n, setting defective product speed intervals BLs and suspected speed intervals YSs in Ai speed intervals, comparing a speed variable SD of a detected workpiece with the defective product speed intervals BLs and the suspected speed intervals YSs, and judging that the detected workpiece is a speed defective product when the speed variable SD is larger than or equal to the minimum value of the defective product speed intervals BLs; when the minimum value of the suspected speed interval YSs is less than or equal to the speed variable SD which is less than the minimum value of the defective speed interval BLs, the detected workpiece is judged to be a suspected speed defective product; when the speed variable SD is smaller than the minimum value of the suspected speed interval YSs, judging that the detected workpiece is good;
step P3: dividing a variable range formed by upper and lower limit values of a forming period variable into m forming period variable intervals, wherein m =1, 2, … …, n, setting a defective product forming period interval BLz and a suspected forming period interval YSz in Bm forming period intervals, comparing a forming period variable CZ of a detected workpiece with a defective product forming period interval BLz and a suspected forming period interval YSz, and judging that the detected workpiece is a forming period defective product when the forming period variable CZ is larger than or equal to the minimum value of the defective product forming period interval BLz; when the minimum value of the suspected molding cycle interval YSz is less than or equal to the molding cycle variable CZ which is less than the minimum value of the defective molding cycle interval BLz, determining that the detected workpiece is a suspected molding cycle defective product; when the molding cycle variable CZ is smaller than the minimum value of the suspected molding cycle interval YSz, judging that the detected workpiece is a good product;
step P4: dividing a variable range formed by upper and lower limit values of a pressure variable into t pressure variable intervals, t =1, 2, … …, n, setting a defective product pressure interval BLy and a suspected speed interval YSy in Ct pressure intervals, comparing a pressure variable YL of a detected workpiece with the defective product pressure interval BLy and the suspected speed interval YSy, and judging that the detected workpiece is a pressure defective product when the pressure variable YL is not less than the minimum value of the defective product pressure interval BLy; when the minimum value of the suspected pressure interval YSy is less than or equal to the minimum value of the pressure variable YL < the defective product pressure interval BLy, the detected workpiece is judged to be a suspected pressure defective product; when the pressure variable YL is smaller than the minimum value of the suspected pressure interval YSy, the detected workpiece is judged to be good;
step P5: dividing a variable range consisting of upper and lower limit values of a temperature variable into u temperature variable intervals, u =1, 2, … …, n, setting a defective product temperature interval Blw and a suspected speed interval YSw in the Du temperature intervals, comparing a temperature variable WD of a detected workpiece with a defective product temperature interval BLw and a suspected speed interval YSw, and judging that the detected workpiece is a temperature defective product when the temperature variable WD is not less than the minimum value of the defective product temperature interval BLw; when the minimum value of the suspected temperature interval YSw is less than or equal to the minimum value of the temperature variable WD < the minimum value of the defective product temperature interval BLw, the detected workpiece is judged to be a suspected temperature defective product; when the temperature variable WD is smaller than the minimum value of the suspected temperature interval YSw, judging that the detected workpiece is good;
step P6: dividing a variable range formed by upper and lower limit values of a screw position variable into w screw position variable intervals, w =1, 2, … …, n, setting a defective screw position interval BLl and a suspected speed interval YSl in the Ew screw position intervals, comparing a screw position variable LW of a detected workpiece with the defective screw position interval BLl and the suspected screw position interval YSlw, and judging that the detected workpiece is a screw position defective workpiece when the screw position variable LW is not less than the minimum value of the defective screw position interval BLl; when the minimum value of the suspected screw position interval YSl is not more than the minimum value of the screw position variable LW < the minimum value of the defective screw position interval BLl, the detected workpiece is judged to be a defective product at the suspected screw position; when the screw position variable LW is smaller than the minimum value of the suspected screw position interval YSl, judging that the detected workpiece is good;
step P7: and carrying out secondary detection on all detection workpieces which are judged to be suspected defective products.
4. The data model-based injection molding product quality inspection method according to claim 3, wherein the secondary inspection process in step P7 comprises the steps of:
q1: obtaining the number of times of judging that the suspected defective products are judged to be the suspected defective products, marking the number of times of judging as t, judging that the suspected defective products are defective products when t is more than or equal to 3, and not executing the next step; when t is less than 3, executing the next step;
q2: obtaining a speed variable SD, a forming period variable CZ, a pressure variable YL, a temperature variable WD and a screw position variable LW of suspected defective products through formulas
Obtaining a bad coefficient BLX of suspected defective products;
q3: when BLX is larger than or equal to BLXmax, judging the suspected defective product as a defective product; and when the BLX is less than BLXmax, judging that the suspected defective product is a good product, wherein the BLXmax is a preset bad coefficient threshold value.
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