Injection blow hollow molding monitoring system based on internet of things

Abstract

The present application relates to the field of production monitoring, and more particularly to a blow molding monitoring system based on the Internet of Things, which is provided with a model storage module for storing object sample models corresponding to target objects at different stages; a monitoring and collecting module for collecting object data corresponding to different stages to obtain the shape characteristics of a parison body at the injection molding stage; an injection blow analysis module for calculating the injection blow quality characteristic value of the parison body according to the shape characteristics to mark the parison body; a blow molding evaluation module for performing a pre-blowing operation on the parison body in response to the marking result, and performing analysis and evaluation on the parison body after the pre-blowing operation; and a molding processing module for adaptively processing the parison body according to the determination result of the blow molding evaluation module. The present application can monitor the key stages of injection blow production in real time, improve the production efficiency of injection blow molding production through dynamic analysis and adjustment, reduce the defect rate of molded products, and ensure the quality of molded products.

Description

Technical Field

[0001] This invention relates to the field of production monitoring, and in particular to an Internet of Things-based injection blow molding monitoring system. Background Technology

[0002] Injection blow molding is a high-efficiency, high-precision plastic processing technology that is widely used in the production of high-requirement packaging in industries such as pharmaceuticals, food, and cosmetics.

[0003] In the injection blow molding process, IoT technology provides a guarantee for accurate monitoring due to its characteristics such as device networking, real-time data transmission and interaction. By deploying various sensors on production equipment, data such as temperature and changes in the shape of the blank can be collected in real time and uploaded to the cloud, realizing comprehensive monitoring of the production process. Combined with big data and artificial intelligence, the collected data can be analyzed in depth to detect production anomalies in a timely manner. The process can also be optimized and adjusted based on the data analysis results to achieve intelligent production, improve product quality stability and production efficiency, and reduce production costs and energy consumption.

[0004] Chinese Patent Application Publication No. CN115256882A discloses a multi-data monitoring-based injection blow molding control system, including a temperature-controlled core module, an injection molding module, a blow molding module, a detection module, and a molding control module. These modules work together to complete the injection blow molding process of the molded product. By monitoring the corresponding process parameters and parameters in real time at each stage of the molding process and adjusting the subsequent process parameters accordingly, the multi-data monitoring-based injection blow molding control system can effectively ensure that the product molding dimensions are adjusted to the standard range according to the actual equipment and product conditions. This results in the molded products prepared using this system having good quality stability.

[0005] However, the following problems still exist in the existing technology.

[0006] Adjustments to the injection blow molding process rely heavily on the experience of relevant personnel, making it difficult to respond promptly to production fluctuations and optimize adjustments in real time. Furthermore, quality issues with the products are often only discovered after molding, resulting in a high scrap rate and reduced production efficiency. Summary of the Invention

[0007] To address this, the present invention provides an Internet of Things-based injection blow molding monitoring system to overcome the problems in the prior art where adjustments to the injection blow molding process mainly rely on the experience of relevant personnel, making it difficult to respond to production fluctuations in a timely manner, optimize and adjust in real time, and often only detect quality problems of the products after molding, resulting in a high scrap rate and reduced production efficiency.

[0008] To achieve the above objectives, the present invention provides an Internet of Things-based injection blow molding monitoring system, comprising:

[0009] The model storage module is used to store the object sample model corresponding to the target object at several stages;

[0010] The monitoring and acquisition module is connected to the model storage module to collect object data corresponding to each stage, so as to obtain the shape features of the preform corresponding to the injection molding stage. The shape features include the concave offset value of the outer surface and the minimum deviation value of the material accumulation thickness.

[0011] The injection blowing analysis module is connected to the monitoring and acquisition module to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, so as to mark the preform;

[0012] A blow molding evaluation module, connected to the injection blow molding analysis module, performs a pre-blow molding operation on the preform in response to the marking results, and analyzes and evaluates the preform after the pre-blow molding operation, including...

[0013] The preform is transferred to the blow molding station, and the transfer characteristics of the preform during the transfer process are obtained. The blow molding stability characterization parameters of the preform are evaluated in combination with the cooling rate of the preform, and it is determined whether the preform meets the blow molding stability benchmark.

[0014] A molding processing module, connected to the blow molding evaluation module, is used to process the preform according to the judgment result of the blow molding evaluation module, including:

[0015] Adjust the blow molding sequence, and after performing a second pre-blow molding on the preform, place it at the beginning of the blow molding sequence for blow molding;

[0016] Alternatively, the preform may be scrapped.

[0017] The transfer feature includes the axial offset at the blow molding station and the sag amplitude when transferred to the blow molding station.

[0018] Furthermore, the injection blowing analysis module is used to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, including:

[0019] The ratio of the depression offset value of the outer surface to the depression offset threshold is used as the first injection quality feature;

[0020] The ratio of the minimum deviation value of the material buildup thickness to the minimum deviation threshold value of the material buildup thickness is used as the second injection-blown quality characteristic.

[0021] The sum of the first injection-blown quality feature and the second injection-blown quality feature is used as the injection-blown quality characterization value.

[0022] Furthermore, the injection blowing analysis module is used to mark the preform, including:

[0023] If the injection quality characterization value of the preform is less than the injection quality characterization threshold, the preform is marked.

[0024] Furthermore, the blow molding evaluation module, in response to the marking results, includes:

[0025] If any preform is marked, a pre-blow molding operation is performed on the preform, and the preform after the pre-blow molding operation is analyzed and evaluated.

[0026] Furthermore, the blow molding evaluation module is used to perform a pre-blow molding operation on the preform, including,

[0027] This is used to apply low-pressure expansion to the preform before it is transferred to the blow molding station.

[0028] Furthermore, the blow molding evaluation module is used to evaluate the blow molding stability characterization parameters of the preform, including:

[0029] The first blow molding stability feature is the sum of the ratio of the axis offset of the blow molding station to the axis offset threshold and the ratio of the sag amplitude transferred to the blow molding station to the sag amplitude threshold.

[0030] The ratio of the cooling rate of the preform to the cooling rate threshold is used as the second blow molding stability feature.

[0031] The first blow molding stability feature and the second blow molding stability feature are weighted and summed to determine the blow molding stability characterization parameter.

[0032] Furthermore, the blow molding evaluation module is used to determine whether the preform meets the blow molding stability criteria, including:

[0033] If the blow molding stability characterization parameter of the preform is greater than or equal to the blow molding stability characterization parameter threshold, then the preform is determined to not meet the blow molding stability benchmark.

[0034] If the blow molding stability characterization parameter of the preform is less than the blow molding stability characterization parameter threshold, then the preform is determined to meet the blow molding stability benchmark.

[0035] Furthermore, the molding processing module is used to process the preform according to the determination result of the blow molding evaluation module, including:

[0036] If the preform meets the blow molding stability benchmark, the blow molding sequence is adjusted, and the preform is pre-blow molded twice and then placed at the beginning of the blow molding sequence for blow molding.

[0037] If the preform does not meet the blow molding stability criteria, the preform shall be scrapped.

[0038] Furthermore, the monitoring and acquisition module is used to determine the minimum deviation value of the material accumulation thickness, including:

[0039] Used to determine several recessed areas and several non-recessed areas of the blank body;

[0040] Used to determine the depression thickness corresponding to each of the depression regions and the non-depression thickness corresponding to each of the non-depression regions;

[0041] The minimum difference between the thickness of each depression and the thickness of each non-depression is used to calculate the minimum deviation value of the material stacking thickness.

[0042] Furthermore, the blow molding evaluation module is used to determine the axis offset, including:

[0043] When transferring the preform to the blow molding station, the angle between the centerline of the preform and the centerline of the mold is used as the axis offset.

[0044] Compared with existing technologies, this invention includes a model storage module for storing object sample models corresponding to several stages of the target object; a monitoring and acquisition module connected to the model storage module for acquiring object data corresponding to each stage to obtain the shape characteristics of the preform corresponding to the injection molding stage; an injection blow analysis module connected to the monitoring and acquisition module for calculating the injection blow quality characterization value of the preform based on the shape characteristics to mark the preform; a blow molding evaluation module connected to the injection blow analysis module for performing a pre-blow molding operation on the preform in response to the marking results, and analyzing and evaluating the preform after the pre-blow molding operation; and a molding processing module connected to the blow molding evaluation module for adaptively processing the preform based on the judgment results of the blow molding evaluation module. This invention monitors the key stages of injection blow molding production in real time, and improves the production efficiency of injection blow hollow molding production, reduces the defect rate of molded products, and ensures the quality of molded products through dynamic analysis and adjustment.

[0045] In particular, this invention includes an injection blow molding analysis module to detect and analyze the outer surface morphology and thickness distribution of the preform formed during the injection molding stage. In the actual injection molding process, there may be situations such as poor venting of the mold cavity, gas retention inside the mold cavity forming air pockets, or excessively rapid local cooling, which may result in recessed areas in the preform formed by injection molding. These recessed areas are usually accompanied by local material loss and thickness reduction. When the preform is subsequently blow molded, the stretch ratio of the corresponding area increases, which can easily lead to problems such as preform cracking and uneven light transmission. Furthermore, recessed areas disrupt the geometric symmetry of the preform, leading to uneven gas expansion stress during blow molding. This results in a skewed preform with an uneven bottom. The degree of concavity on the outer surface of the preform is used to quantify the defect severity during injection molding. The difference in material thickness between recessed and non-recessed areas poses a risk of breakage during blow molding, worsening the wall thickness distribution of the preform. Simultaneously, due to greater material resistance in thicker areas, gas preferentially expands towards thinner areas during blow molding, causing preform eccentricity or deformation. Slower cooling in thicker areas leads to residual internal stress, potentially causing deformation during later storage. Therefore, this invention combines these characteristics to calculate injection-blown quality characterization values, quantifying the quality of the injection-blown preform and its impact on the subsequent blow molding quality and molding stability. This provides data support for subsequent preform marking. This invention monitors key stages of injection-blown production in real time, dynamically analyzing and adjusting to improve production efficiency, reduce defect rates, and ensure product quality.

[0046] In particular, this invention includes a blow molding evaluation module to classify the preforms after injection molding. Preforms with relatively good morphology are pre-blow molded before the actual blow molding process to optimize them and improve issues such as uneven morphology and thickness distribution. During the transfer of the preform to the blow molding station, gravity can cause significant sagging and stretching. This can lead to abnormalities such as a significantly thinner wall at the bottom of the preform and increased material buildup at the opening at the top. Thin-walled areas are prone to becoming structural weaknesses in the product, potentially causing cracking or leakage. Furthermore, thin-walled areas cool faster, creating a stress difference with thicker areas and increasing the risk of warping or cracking. Additionally, when the preform is transferred to the blow molding station, the offset between the preform's centerline and the mold's centerline can hinder uniform contact between the preform and the mold cavity, potentially resulting in incomplete blow molding filling, asymmetrical material distribution at the parting line, and defects such as burrs or poor film adhesion. Furthermore, while considering the transfer characteristics of the preform, this invention also comprehensively analyzes the cooling of the preform during the transfer process. Excessive cooling can lead to surface hardening of the preform, making it difficult to expand evenly during blow molding and further exacerbating insufficient material filling. Therefore, this invention evaluates the blow molding stability characterization parameters of the preform to characterize the stability of the preform during blow molding and the impact on blow molding quality during the transfer process. This provides data support for subsequently determining whether the preform meets the blow molding stability benchmark. Consequently, the preform is adaptively processed. This invention monitors the key stages of injection blow molding in real time, and through dynamic analysis and adjustment, improves the production efficiency of injection blow hollow molding, reduces the defect rate of molded products, and ensures the quality of molded products.

[0047] In particular, the present invention is equipped with a molding processing module to perform differentiated processing on the blow-molded preforms. Products with slightly poor quality are given priority for improvement after secondary pre-blow molding; products with serious quality problems are directly scrapped to avoid ineffective energy consumption. The present invention can more accurately control the blow molding process while making efficient use of resources and reducing waste and energy consumption. Attached Figure Description

[0048] Figure 1 A functional block diagram of an IoT-based injection blow molding monitoring system according to an embodiment of the invention;

[0049] Figure 2 This is a logic diagram for marking the blank body according to an embodiment of the invention.

[0050] Figure 3 A logic diagram for determining whether a preform conforms to the blow molding stability criteria in an embodiment of the invention;

[0051] Figure 4 This is a logic decision diagram for processing the preform in an embodiment of the invention. Detailed Implementation

[0052] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0053] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] It should be noted that in the description of this invention, the terms "upper," "inner," "outer," etc., indicating the direction or positional relationship are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0055] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0056] Please see Figure 1 The diagram shown is a functional block diagram of an IoT-based injection blow molding monitoring system according to an embodiment of the present invention. The IoT-based injection blow molding monitoring system according to an embodiment of the present invention includes:

[0057] The model storage module is used to store the object sample model corresponding to the target object at several stages;

[0058] The monitoring and acquisition module is connected to the model storage module to collect object data corresponding to each stage, so as to obtain the shape features of the preform corresponding to the injection molding stage. The shape features include the concave offset value of the outer surface and the minimum deviation value of the material accumulation thickness.

[0059] The injection blowing analysis module is connected to the monitoring and acquisition module to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, so as to mark the preform;

[0060] A blow molding evaluation module, connected to the injection blow molding analysis module, performs a pre-blow molding operation on the preform in response to the marking results, and analyzes and evaluates the preform after the pre-blow molding operation, including...

[0061] The preform is transferred to the blow molding station, and the transfer characteristics of the preform during the transfer process are obtained. The blow molding stability characterization parameters of the preform are evaluated in combination with the cooling rate of the preform, and it is determined whether the preform meets the blow molding stability benchmark.

[0062] A molding processing module, connected to the blow molding evaluation module, is used to process the preform according to the judgment result of the blow molding evaluation module, including:

[0063] Adjust the blow molding sequence, and after performing a second pre-blow molding on the preform, place it at the beginning of the blow molding sequence for blow molding;

[0064] Alternatively, the preform may be scrapped.

[0065] The transfer feature includes the axial offset at the blow molding station and the sag amplitude when transferred to the blow molding station.

[0066] In this embodiment, the difference between the blank and the corresponding object sample model is used as the concave offset value of the outer surface.

[0067] Specifically, there are no specific restrictions on the method of collecting object data for each stage. However, the cooling rate of the blank during the transfer process can be determined by real-time monitoring of the blank temperature using an infrared thermal imager.

[0068] To collect the concave offset value of the outer surface of the preform, the preform can be scanned by a structured light 3D scanner to generate 3D point cloud data of the preform surface. The contour of the preform is compared with the corresponding object sample model by the shape deviation analysis algorithm to determine the concave offset value of the outer surface.

[0069] The minimum deviation value of material accumulation thickness of the preform can be determined by obtaining the thickness of the concave and non-concave areas using X-ray tomography of industrial CT, and then determining the minimum deviation value of material accumulation thickness.

[0070] For capturing the sag amplitude during the transfer to the blow molding station, an industrial camera can be deployed in the vertical direction of the preform transfer path. A calibration target is used to determine the scaling factor, for example, 1 pixel = 0.01 mm. The transfer process of the preform is continuously captured, and the preform contour is extracted through an edge detection algorithm. The real-time length is calculated, and the real-time length is compared with the initial length to determine the sag amplitude.

[0071] For the acquisition of the axial offset of the preform at the blow molding station, the preform and the mold opening at the blow molding station are scanned, and the center line of the preform and the center line of the mold opening are identified in the vertical direction after scanning. The angle between the center line of the preform and the center line of the mold in the horizontal direction is determined, and then the axial offset is determined.

[0072] It is understandable that the process of transferring the preform refers to the process of transferring the preform from the clamping point to the blow molding point. Correspondingly, the sag refers to the axial elongation of the preform from the clamping point to the blow molding point due to gravity. The length of the preform at the clamping point is taken as the initial length, which will not be elaborated further.

[0073] The object data includes the cooling rate of the preform during the transfer process, the shape characteristics of the preform, and the transfer characteristics.

[0074] Specifically, there are no restrictions on the specific structure of the model storage block, injection blow analysis module, blow molding evaluation module, and molding processing module. Each of these modules or its units can be composed of logic components or combinations of logic components. Logic components include field-programmable processors, computers, or microprocessors in computers.

[0075] Specifically, the injection blowing analysis module is used to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, including:

[0076] The ratio of the depression offset value of the outer surface to the depression offset threshold is used as the first injection quality feature;

[0077] The ratio of the minimum deviation value of the material buildup thickness to the minimum deviation threshold value of the material buildup thickness is used as the second injection-blown quality characteristic.

[0078] The sum of the first injection-blown quality feature and the second injection-blown quality feature is used as the injection-blown quality characterization value.

[0079] In this embodiment, the purpose of setting the dent offset threshold and the minimum deviation threshold of material accumulation thickness is to characterize the situation where the quality of the preform after injection molding is poor, which has a serious impact on the blow molding quality of the subsequent preform. By acquiring historical object data of several identical products completed by injection blow molding, the historical data of dent offset value and minimum deviation value of material accumulation thickness on the corresponding outer surface of the preform are called, and the average dent offset value and the average minimum deviation value of material accumulation thickness are calculated. These are then used as the benchmark values ​​under normal conditions. Based on the purpose of setting the above two thresholds, the dent offset threshold is determined to be the product of the average dent offset value and the dent deviation coefficient, and the minimum deviation threshold of material accumulation thickness is determined to be the product of the average deviation of material accumulation thickness and the accumulation deviation coefficient. The dent deviation coefficient is selected in the range [1.02, 1.05], preferably 1.02 in practice, and the accumulation deviation coefficient is selected in the range [1.05, 1.08], preferably 1.05 in practice.

[0080] It is understandable that the presence of recessed areas in the preform leads to uneven material distribution and varying thicknesses, which in turn affects the performance of the blow-molded product. Therefore, in practice, the minimum difference between the material accumulation thickness in the recessed areas and the material accumulation thickness in the non-recessed areas is considered to highlight the material differences in the preform and is determined as the minimum deviation value of the material accumulation thickness. This will not be elaborated further.

[0081] Specifically, this invention includes an injection blow molding analysis module to detect and analyze the outer surface morphology and thickness distribution of the preform formed during the injection molding stage. In actual injection molding, issues such as poor mold cavity venting, gas stagnation forming air pockets, or excessively rapid local cooling can lead to recessed areas in the preform. These recessed areas are often accompanied by localized material loss and thickness reduction. During subsequent blow molding, the stretch ratio of these areas increases, potentially causing preform cracking and uneven light transmission. Furthermore, recessed areas disrupt the geometric symmetry of the preform, resulting in uneven gas expansion stress during blow molding, leading to a skewed preform with an uneven bottom. This invention quantifies the degree of defect on the outer surface of the preform during the injection molding stage by analyzing the extent of these recesses.

[0082] The difference between the material accumulation thickness corresponding to the concave area and the material accumulation thickness corresponding to the non-concave area can cause cracking during blow molding. The wall thickness distribution of the blow-molded preform deteriorates. At the same time, due to the greater material resistance in the thicker area, the gas preferentially expands to the thinner area during blow molding, causing the preform to be eccentric or deformed. The thicker area cools slowly, and internal stress remains, which may cause deformation during later storage.

[0083] Therefore, this invention combines the above-mentioned characteristics to calculate the injection blow molding quality characterization value, which quantitatively characterizes the quality of the preform formed by injection molding, and further characterizes the degree of influence on the blow molding quality of subsequent preforms, as well as the influence on the molding stability of the preforms, providing data support for subsequent marking of the preforms. This invention monitors the key stages of injection blow molding production in real time, and improves the production efficiency of injection blow molding by dynamic analysis and adjustment, reducing the defect rate of molded products and ensuring the quality of molded products.

[0084] Specifically, please refer to Figure 2 As shown, this is a logic decision diagram for marking the blank body according to an embodiment of the present invention. The injection blowing analysis module is used to mark the blank body, including:

[0085] If the injection quality characterization value of the preform is less than the injection quality characterization threshold, the preform is marked.

[0086] If the injection quality characterization value of the preform is greater than or equal to the injection quality characterization threshold, then there is no need to mark the preform.

[0087] The injection-blown quality characterization threshold is predetermined. The injection-blown quality characterization value calculated when the concave offset value of the outer surface is equal to the concave offset threshold and the minimum deviation value of the material stacking thickness is equal to the minimum deviation threshold of the material stacking thickness is determined as the injection-blown quality characterization threshold.

[0088] Specifically, the blow molding evaluation module, in response to the marking results, includes:

[0089] If any preform is marked, a pre-blow molding operation is performed on the preform, and the preform after the pre-blow molding operation is analyzed and evaluated.

[0090] Specifically, the blow molding evaluation module is used to perform a pre-blow molding operation on the preform, including,

[0091] This is used to apply low-pressure expansion to the preform before it is transferred to the blow molding station.

[0092] Specifically, the blow molding evaluation module is used to evaluate the blow molding stability characterization parameters of the preform, including:

[0093] The first blow molding stability feature is the sum of the ratio of the axis offset of the blow molding station to the axis offset threshold and the ratio of the sag amplitude transferred to the blow molding station to the sag amplitude threshold.

[0094] The ratio of the cooling rate of the preform to the cooling rate threshold is used as the second blow molding stability feature.

[0095] The first blow molding stability feature and the second blow molding stability feature are weighted and summed to determine the blow molding stability characterization parameter.

[0096] Specifically, during the transfer of the preform, the sag of the preform directly leads to uneven wall thickness, causing immediate damage. The axial offset also has geometrical irreversibility for subsequent blow molding. There is a certain misalignment between the preform and the mold cavity, resulting in eccentricity in the blow-molded product. The effect of the cooling rate can be partially offset by the blow molding time. Therefore, in practice, the transfer characteristics of the preform, namely the axial offset of the blow molding station and the sag of the preform after transfer to the blow molding station, are given priority. Thus, the first blow molding stability feature calculated based on the transfer characteristics is given a slightly higher weight. Therefore, when performing weighted summation, the weight of the first blow molding stability feature is set to 0.6, and the weight of the second blow molding stability feature is set to 0.4.

[0097] In this embodiment, the purpose of setting the sag amplitude threshold and the axis offset threshold is to characterize the poor stability of the preform during blow molding and the serious impact of transfer interference on the blow molding quality. By acquiring historical object data of several identical products completed injection blow molding, the historical data of the axis offset of the preform at the blow molding station and the historical data of the sag amplitude of the preform transferred to the blow molding station are called to solve for the mean axis offset and the mean sag amplitude, which are then used as the reference values ​​under normal conditions. Based on the purpose of setting the above two thresholds, the axis offset threshold is determined to be the product of the mean axis offset and the axis deviation coefficient, and the sag amplitude threshold is determined to be the product of the mean sag amplitude and the sag deviation coefficient. The axis deviation coefficient is selected in the interval [1.03, 1.06], preferably 1.03 in practice, and the sag deviation coefficient is selected in the interval [1.02, 1.04], preferably 1.02 in practice.

[0098] Specifically, this invention includes a blow molding evaluation module to classify the preforms after injection molding. Preforms with relatively good morphology are pre-blow molded before the actual blow molding process to optimize them and improve issues such as uneven morphology and thickness distribution. During the transfer of the preform to the blow molding station, gravity can cause significant sagging and stretching. This can lead to abnormalities such as a significantly thinner wall at the bottom of the preform and increased material buildup at the opening at the top. Thin-walled areas are prone to becoming structural weaknesses in the product, potentially causing cracking or leakage. Furthermore, thin-walled areas cool faster, creating a stress difference with thicker areas and increasing the risk of warping or cracking.

[0099] Furthermore, when the preform is transferred to the blow molding station, the offset between the preform's centerline and the mold's centerline can hinder the uniform fit of the preform to the mold cavity, potentially leading to incomplete blow molding filling, asymmetrical material distribution at the parting line, and defects such as flash, burrs, or poor film adhesion. Moreover, this invention, while considering the preform's transfer characteristics, also analyzes the preform's cooling during the transfer process. Excessive cooling can cause surface hardening of the preform, making uniform expansion difficult during blow molding and further exacerbating insufficient material filling.

[0100] Therefore, this invention characterizes the stability of the preform during blow molding and the impact on blow molding quality during transfer by evaluating blow molding stability parameters of the preform. This provides data support for subsequent determination of whether the preform meets blow molding stability benchmarks, and allows for adaptive processing of the preform. This invention monitors key stages of injection blow molding in real time, and through dynamic analysis and adjustment, improves the production efficiency of injection blow hollow molding, reduces the defect rate of molded products, and ensures the quality of molded products.

[0101] Specifically, please refer to Figure 3 As shown, this is a logic diagram for determining whether a preform meets the blow molding stability criteria according to an embodiment of the present invention. The blow molding evaluation module is used to determine whether the preform meets the blow molding stability criteria, including:

[0102] If the blow molding stability characterization parameter of the preform is greater than or equal to the blow molding stability characterization parameter threshold, then the preform is determined to not meet the blow molding stability benchmark.

[0103] If the blow molding stability characterization parameter of the preform is less than the blow molding stability characterization parameter threshold, then the preform is determined to meet the blow molding stability benchmark.

[0104] The blow molding stability characterization parameter threshold is predetermined. It is determined by calculating the blow molding stability characterization parameter threshold when the axial offset of the blow molding station is equal to the axial offset threshold, the sag amplitude when transferred to the blow molding station is equal to the sag amplitude threshold, and the cooling rate of the preform is equal to the cooling rate threshold.

[0105] Specifically, please refer to Figure 4 As shown, this is a logic decision diagram for processing the preform according to an embodiment of the present invention. The molding processing module is used to process the preform according to the decision result of the blow molding evaluation module, including:

[0106] If the preform meets the blow molding stability benchmark, the blow molding sequence is adjusted, and the preform is pre-blow molded twice and then placed at the beginning of the blow molding sequence for blow molding.

[0107] If the preform does not meet the blow molding stability criteria, the preform shall be scrapped.

[0108] Understandably, in the injection blow molding process, the injection sequence is usually determined in advance. Correspondingly, the robot moves the preform from the injection molding station to the blow molding station. Therefore, the blow molding sequence needs to be synchronized with the injection molding sequence.

[0109] Specifically, this invention includes a molding processing module that performs differentiated processing on the blow-molded preforms. Products with slightly substandard quality are given priority for improvement after secondary pre-blow molding. Products with serious quality problems are directly scrapped to avoid unnecessary energy consumption. This invention enables more precise control of the blow molding process while efficiently utilizing resources and reducing waste and energy consumption.

[0110] Specifically, the monitoring and acquisition module is used to determine the minimum deviation value of the material accumulation thickness, including:

[0111] Used to determine several recessed areas and several non-recessed areas of the blank body;

[0112] Used to determine the depression thickness corresponding to each of the depression regions and the non-depression thickness corresponding to each of the non-depression regions;

[0113] The minimum difference between the thickness of each depression and the thickness of each non-depression is used to calculate the minimum deviation value of the material stacking thickness.

[0114] Specifically, the blow molding evaluation module is used to determine the axis offset, including:

[0115] When transferring the preform to the blow molding station, the angle between the centerline of the preform and the centerline of the mold is used as the axis offset.

[0116] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An Internet of Things-based injection blow molding monitoring system, characterized in that, include: The model storage module is used to store the object sample model corresponding to the target object at several stages; The monitoring and acquisition module is connected to the model storage module to collect object data corresponding to each stage, so as to obtain the shape features of the preform corresponding to the injection molding stage. The shape features include the concave offset value of the outer surface and the minimum deviation value of the material accumulation thickness. The injection blowing analysis module is connected to the monitoring and acquisition module to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, so as to mark the preform; A blow molding evaluation module, connected to the injection blow molding analysis module, performs a pre-blow molding operation on the preform in response to the marking results, and analyzes and evaluates the preform after the pre-blow molding operation, including... The preform is transferred to the blow molding station, and the transfer characteristics of the preform during the transfer process are obtained. The blow molding stability characterization parameters of the preform are evaluated in combination with the cooling rate of the preform, and it is determined whether the preform meets the blow molding stability benchmark. A molding processing module, connected to the blow molding evaluation module, is used to process the preform according to the judgment result of the blow molding evaluation module, including: Adjust the blow molding sequence, and after performing a second pre-blow molding on the preform, place it at the beginning of the blow molding sequence for blow molding; Alternatively, the preform may be scrapped. The transfer feature includes the axial offset at the blow molding station and the sag amplitude when transferred to the blow molding station. The injection blowing analysis module is used to calculate the injection blowing quality characterization value of the preform based on the shape characteristics, including: The ratio of the depression offset value of the outer surface to the depression offset threshold is used as the first injection quality feature; The ratio of the minimum deviation value of the material buildup thickness to the minimum deviation threshold value of the material buildup thickness is used as the second injection-blown quality characteristic. The sum of the first injection quality feature and the second injection quality feature is used as the injection quality characterization value; The blow molding evaluation module is used to evaluate the blow molding stability characterization parameters of the preform, including: The first blow molding stability feature is the sum of the ratio of the axis offset of the blow molding station to the axis offset threshold and the ratio of the sag amplitude transferred to the blow molding station to the sag amplitude threshold. The ratio of the cooling rate of the preform to the cooling rate threshold is used as the second blow molding stability feature. The first blow molding stability feature and the second blow molding stability feature are weighted and summed to determine the blow molding stability characterization parameter.

2. The IoT-based injection blow molding monitoring system according to claim 1, characterized in that, The injection blowing analysis module is used to mark the preform, including: If the injection quality characterization value of the preform is less than the injection quality characterization threshold, the preform is marked.

3. The IoT-based injection blow molding monitoring system according to claim 2, characterized in that, The blow molding evaluation module responds to the labeling results, including: If any preform is marked, a pre-blow molding operation is performed on the preform, and the preform after the pre-blow molding operation is analyzed and evaluated.

4. The IoT-based injection blow molding monitoring system according to claim 1, characterized in that, The blow molding evaluation module is used to perform a pre-blow molding operation on the preform, including: This is used to apply low-pressure expansion to the preform before it is transferred to the blow molding station.

5. The IoT-based injection blow molding monitoring system according to claim 1, characterized in that, The blow molding evaluation module is used to determine whether the preform meets the blow molding stability criteria, including: If the blow molding stability characterization parameter of the preform is greater than or equal to the blow molding stability characterization parameter threshold, then the preform is determined to not meet the blow molding stability benchmark. If the blow molding stability characterization parameter of the preform is less than the blow molding stability characterization parameter threshold, then the preform is determined to meet the blow molding stability benchmark.

6. The IoT-based injection blow molding monitoring system according to claim 5, characterized in that, The molding processing module is used to process the preform according to the judgment result of the blow molding evaluation module, including: If the preform meets the blow molding stability benchmark, the blow molding sequence is adjusted, and the preform is pre-blow molded twice and then placed at the beginning of the blow molding sequence for blow molding. If the preform does not meet the blow molding stability criteria, the preform shall be scrapped.

7. The IoT-based injection blow molding monitoring system according to claim 1, characterized in that, The monitoring and acquisition module is used to determine the minimum deviation value of the material accumulation thickness, including: Used to determine several recessed areas and several non-recessed areas of the blank body; Used to determine the depression thickness corresponding to each of the depression regions and the non-depression thickness corresponding to each of the non-depression regions; The minimum difference between the thickness of each depression and the thickness of each non-depression is used to calculate the minimum deviation value of the material stacking thickness.

8. The IoT-based injection blow molding monitoring system according to claim 1, characterized in that, The blow molding evaluation module is used to determine the axis offset, including: When transferring the preform to the blow molding station, the angle between the centerline of the preform and the centerline of the mold is used as the axis offset.

Images