A thin-wall product injection molding device and process

Abstract

The present invention relates to the technical field of injection molding technology, and specifically discloses an injection molding device and process for thin-walled products, comprising: step one: testing thin-walled products to obtain test data of thin-walled products; step two: calculating defect coefficients of thin-walled products based on the test data; step three: analyzing the quality of thin-walled products based on the defect coefficients, determining whether the quality of thin-walled products is qualified, and generating quality assessment signals; step four: analyzing a range with a high probability of unqualified process parameters based on unqualified quality signals; the present invention facilitates timely adjustment of process parameters and avoids high-risk operations through scientific data analysis and risk assessment methods, thereby providing an effective tool for optimizing injection molding process parameters and helping to improve product quality and production stability.

Description

Technical Field

[0001] The invention relates to the technical field of injection molding technology, and in particular to an injection molding device and process for thin-walled products. Background Art

[0002] Injection molding is one of the most important and commonly used molding methods in the modern plastic processing industry. It uses an injection molding machine to heat thermoplastics (such as polyethylene, polypropylene, polystyrene, ABS, etc.) or thermosetting plastics (such as phenolic resin, epoxy resin, etc., but rarely used for injection molding) to a molten state, and then quickly injects them into the pre-designed mold cavity through the injection molding machine's nozzle and the mold's gate system under high pressure. After the plastic cools and solidifies in the mold, the mold can be opened to take out the product.

[0003] Injection molding is a key step in processing PLA materials into thin-walled products, and its process control directly affects the dimensional accuracy, surface quality and performance of thin-walled products. If the thin-walled products obtained by the injection molding process have process defects, the appearance, mechanical properties, and catheter capacity of the thin-walled products will be affected. However, in the prior art, the inspection process of injection molding often has inaccurate quality analysis, and cannot identify and avoid high-risk process parameter ranges by detailed analysis of quality failure signals, especially data of thin-walled defective products, thereby leading to uncertainty in product quality. Summary of the invention

[0004] The purpose of the present invention is to provide a thin-wall product injection molding device and process to solve the technical problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides an injection molding process for thin-walled products, comprising: extracting raw materials from plants to synthesize degradable PLA materials; blending and coloring the PLA materials to form granules; dehumidifying and drying the master batches; delivering the master batches after dehumidification and drying to an injection molding machine, and injection molding the master batches through the injection molding machine; after the injection molding is completed, taking out the injection molded product from the injection molding machine;

[0007] The injection molding process is monitored, specifically the following steps:

[0008] Step 1: Test thin-wall products and obtain test data of thin-wall products;

[0009] Among them, the detection data include: the difference ratio of the acoustic wave represents the total value and the difference ratio of the number of zones;

[0010] Step 2: Calculate the defect coefficient of thin-walled products based on the test data;

[0011] Step 3: Based on the defect coefficient, analyze the quality of thin-walled products, determine whether the quality of thin-walled products is qualified, and generate a quality assessment signal;

[0012] Among them, the quality assessment signal includes: a quality qualified signal and a quality unqualified signal;

[0013] Step 4: Based on the quality failure signal, analyze the range of process parameters with high probability of failure.

[0014] As a further solution of the present invention, the process of testing thin-walled products and obtaining the test data of the thin-walled products is as follows:

[0015] Test thin-walled products and obtain the acoustic wave difference ratio at each position of the thin-walled products;

[0016] Based on the acoustic wave difference ratio of each position point, thin-walled products are divided into regions to obtain several product sub-regions, and the acoustic wave difference ratio representation value of each product sub-region is calculated;

[0017] Based on the acoustic wave difference ratio representation value of each product sub-area, the total acoustic wave difference ratio representation value and the area quantity difference ratio are calculated respectively.

[0018] As a further solution of the present invention: based on the acoustic wave difference ratio of each position point, the thin-walled products are divided into regions, and the process of obtaining several product sub-regions is as follows:

[0019] Based on the acoustic wave difference ratios of all position points, the maximum and minimum values ​​of the acoustic wave difference ratios are extracted, which are marked as the maximum acoustic wave difference ratio and the minimum acoustic wave difference ratio, respectively, to obtain the total range of the acoustic wave difference ratios of all position points;

[0020] Dividing the total range of the sound wave difference ratio into a plurality of sound wave difference ratio sub-ranges;

[0021] Based on each acoustic wave difference ratio sub-range and in accordance with the acoustic wave difference ratio of each position point, a number of product sub-areas are obtained.

[0022] As a further solution of the present invention: the process of obtaining the total value of the acoustic wave difference ratio is as follows:

[0023] Based on a single product sub-area, the acoustic wave difference ratio representation value of the product sub-area is multiplied by the number of position points of the product sub-area to obtain the acoustic wave difference ratio representation weight;

[0024] The sound wave difference ratio representation weights of all product sub-areas are summed up to obtain the total sound wave difference ratio representation value.

[0025] As a further solution of the present invention: the process of obtaining the area quantity difference ratio is:

[0026] Extracting the total number of the acoustic wave difference ratio sub-ranges to obtain the total number of the acoustic wave difference ratio sub-ranges;

[0027] Extract the total number of product sub-areas to obtain the total number of product sub-areas;

[0028] Calculate the difference between the total number of product sub-areas and the total number of acoustic wave difference ratio sub-ranges to obtain the zone quantity difference, and calculate the ratio between the zone quantity difference and the total number of acoustic wave difference ratio sub-ranges to obtain the zone quantity difference ratio.

[0029] As a further solution of the present invention: Based on the detection data, the process of calculating the defect coefficient of thin-walled products is as follows:

[0030]

[0031] The defect coefficient QX is calculated by formula:, where a1 and a2 are preset proportional factors, and both are greater than 0, CZ is the total value represented by the acoustic wave difference ratio, and QC is the zone quantity difference ratio.

[0032] As a further solution of the present invention: the process of generating the quality assessment signal is:

[0033] Preset the defect coefficient threshold, and compare and analyze the defect coefficient with the defect coefficient threshold;

[0034] If the defect coefficient is greater than the defect coefficient threshold, a quality failure signal is generated;

[0035] If the defect coefficient is less than or equal to the defect coefficient threshold, a quality qualified signal is generated.

[0036] As a further solution of the present invention: based on the quality failure signal, the process of analyzing the range of high probability of failure of process parameters is as follows:

[0037] Based on the quality failure signal, extract the analysis data of each thin-wall defective product in the past times;

[0038] The analysis data include: defect coefficient of thin-wall defective products and target process parameters of the thin-wall defective products during injection molding;

[0039] Based on the analysis data of all thin-wall defective products, the maximum and minimum values ​​of the target process parameters are extracted to obtain the target process parameter range. At the same time, the target process parameter range is evenly divided into multiple parameter sub-ranges;

[0040] Calculate the defect probability characterization value of each parameter sub-range respectively; when the defect probability characterization value is greater than the parameter sub-range of the defect probability characterization threshold, generate a high-risk sub-range signal, and mark the corresponding parameter sub-range as a high-risk sub-range; when the defect probability characterization value is less than or equal to the parameter sub-range of the defect probability characterization threshold, generate a low-risk sub-range signal;

[0041] Based on the values ​​of the high-risk sub-ranges, all adjacent high-risk sub-ranges are merged in pairs until they cannot be merged, thereby obtaining multiple pre-high-risk ranges;

[0042] The risk assessment value of each pre-high risk range is calculated respectively, and the pre-high risk range with the largest risk assessment value is marked as a high risk range.

[0043] As a further solution of the present invention: the process of obtaining the risk assessment value is:

[0044] Based on a single pre-high risk range, extract the total number of thin-wall defective product sub-ranges and defect probability characterization values ​​of each high-risk sub-range within the pre-high risk range;

[0045] Based on a single high-risk sub-range, the total number of thin-wall defective products in the high-risk sub-range is calculated by ratio to the total number of pre-high-risk defective products to obtain the high-risk defective product quantity ratio;

[0046] The ratio of the number of high-risk defective products is multiplied by the defect probability characterization value to obtain the risk sub-assessment value of the high-risk sub-range;

[0047] The risk sub-assessment values ​​of all high-risk sub-ranges within the pre-high-risk range are summed up to obtain a risk assessment value;

[0048] Among them, the process of obtaining the defect probability characterization value is:

[0049] Based on a single parameter sub-range, the number of thin-wall defective products and the defect coefficient of each thin-wall defective product within the parameter sub-range in the analysis data are extracted;

[0050] Calculate the ratio of the total number of thin-wall defective products in the sub-range to the total number of thin-wall defective products to obtain the defective product quantity ratio;

[0051] The defect coefficients of each thin-wall defective product are summed up to obtain the total value of the defect coefficient;

[0052] The defect probability representation value is obtained by multiplying the defect product quantity ratio and the total defect coefficient value.

[0053] In a second aspect, the present invention provides a thin-walled product injection molding device, the device comprising:

[0054] Data acquisition module: to test thin-wall products and obtain test data of thin-wall products;

[0055] Among them, the detection data include: the difference ratio of the acoustic wave represents the total value and the difference ratio of the number of zones;

[0056] Data processing module: Calculate the defect coefficient of thin-walled products based on the test data;

[0057] Quality assessment module: Analyze the quality of thin-walled products based on the defect coefficient, determine whether the quality of thin-walled products is qualified, and generate quality assessment signals;

[0058] Among them, the quality assessment signal includes: a quality qualified signal and a quality unqualified signal;

[0059] Data analysis module: Based on the quality failure signal, analyze the range of process parameters with high probability of failure.

[0060] Beneficial effects of the present invention:

[0061] (1) The present invention realizes efficient evaluation of product quality through a sophisticated data collection and analysis process. First, ultrasonic detection equipment is used to perform ultrasonic detection on each position point of thin-walled products to obtain the acoustic wave difference ratio of each position point, that is, the difference ratio of the intensity of the transmitted wave and the reflected wave. Then, based on the acoustic wave difference ratio data, the product is divided into multiple sub-areas, and the acoustic wave characteristics of the position points in each area are similar. The acoustic wave difference ratio characterization value is obtained by calculating the mean of the acoustic wave difference ratio in each sub-area, and the total acoustic wave difference ratio characterization value is calculated in combination with the number of position points in the sub-area. This indicator reflects the total amount of the overall acoustic wave characteristics of the product. At the same time, by comparing the total number of acoustic wave difference ratio sub-ranges with the total number of product sub-areas, the area number difference ratio is calculated, which reflects the concentration of the product sub-area division, that is, the discrete degree of process defects. The larger the value, the better the process defects. The more dispersed the defect distribution is; the smaller the value is, the more concentrated the process defects are; next, according to the preset formula, the total value of the acoustic wave difference ratio and the zone quantity difference ratio are used as input to calculate the defect coefficient; the formula comprehensively considers the total amount of the product acoustic wave characteristics and the discrete degree of the process defects by adjusting the weight of the preset proportional factor, thereby obtaining an indicator reflecting the overall quality status of the product; finally, the calculated defect coefficient is compared and analyzed with the preset threshold; if the defect coefficient is greater than the threshold, the product quality is judged to be unqualified and a quality unqualified signal is generated; if the defect coefficient is less than or equal to the threshold, the product quality is judged to be qualified and a quality qualified signal is generated; the present invention uses a sophisticated data collection and analysis process, and the embodiment realizes a rapid and accurate evaluation of the quality of thin-walled products, providing strong support for quality control in the production process;

[0062] (2) The present invention identifies and avoids high-risk process parameter ranges by analyzing quality failure signals, especially data of thin-wall defective products, in detail. First, all thin-wall defective products that have caused quality failure signals in the recent period are reviewed and analyzed, and the defect coefficients of these products and their corresponding injection molding target process parameters are extracted. Subsequently, the process parameter range is determined based on the target process parameters of these defective products, and is evenly divided into multiple sub-ranges for detailed analysis. Next, for each parameter sub-range, its defect probability characterization value is calculated, which comprehensively considers the proportion of defective products in the sub-range and the sum of their defect coefficients. By comparing the defect probability characterization values ​​of each sub-range with a preset threshold, high-risk sub-ranges, i.e., those parameter intervals that are more likely to cause product defects, are identified. In order to more accurately locate high-risk areas, the present invention further separates adjacent The high-risk sub-ranges are merged until they can no longer be merged to form a pre-high-risk range; then, a risk assessment is performed on each pre-high-risk range, and a risk assessment value is obtained by calculating the sum of the risk sub-assessment values ​​of all the high-risk sub-ranges within it, and the pre-high-risk range with the highest assessment value is determined as the final high-risk range; finally, based on the determination of the high-risk range, the selection of process parameters in the injection molding process is guided to avoid the use of these high-risk parameters to improve product quality and production efficiency; in addition, through continuous data feedback and optimization adjustment, the generation rate of thin-walled defective products is further reduced to maximize production benefits; the embodiment of the present invention facilitates timely adjustment of process parameters and avoids high-risk operations through scientific data analysis and risk assessment methods, provides an effective tool for optimization of injection molding process parameters, and helps to improve product quality and production stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below in conjunction with the accompanying drawings.

[0064] Figure 1 is a flowchart of Embodiment 1 of the present invention;

[0065] Figure 2 is a flowchart of Embodiment 2 and Embodiment 3 of the present invention;

[0066] Figure 3 It is a flowchart of embodiment 4 of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Embodiment 1:

[0069] See also Figure 1 As shown, a thin-walled product injection molding process according to an embodiment of the present invention comprises the following steps:

[0070] 1. Extract raw materials from plants to synthesize degradable PLA materials;

[0071] Among them, plants include but are not limited to: corn cobs, straw;

[0072] Specifically, the degradable PLA material is composed of the following components by weight: 23.6 parts of polylactic acid, 3.2 parts of hexyldecanol, 1.4 parts of cetylpyridinium chloride, 1.9 parts of methyl silicone oil, 2.7 parts of lotus leaf extract, 3.6 parts of methyltriethoxysilane, 2.1 parts of sodium aminosulfonate, 3.4 parts of Phellodendron amurense extract, 3.8 parts of dimethylbenzyl butyrate, 1.1 parts of microcrystalline cellulose, and 2.3 parts of 4,5-dichloro-2-octyl-3-isothiazolinone.

[0073] 2. Blending, coloring and granulating the PLA materials;

[0074] 3. Dehumidify and dry the masterbatch;

[0075] 4. Send the masterbatch after dehumidification and drying to the injection molding machine, and perform injection molding on the masterbatch through the injection molding machine;

[0076] It needs to be explained that injection molding uses high-speed injection;

[0077] The process parameters of injection molding include: injection speed, injection pressure, injection time, molding temperature, and mold temperature;

[0078] 5. After the injection molding is completed, take the molded product out of the injection molding machine.

[0079] Embodiment 2:

[0080] Based on Example 1, injection molding is the key link in processing PLA materials into thin-walled products. Its process control directly affects the dimensional accuracy, surface quality and performance of thin-walled products. If the thin-walled products obtained by the injection molding process have process defects (process defects include but are not limited to: bubbles, shrinkage holes), the appearance, mechanical properties, and catheter capacity of the thin-walled products will be affected. Based on this, please refer to Figure 2 As shown, the thin-walled product injection molding process described in the embodiment of the present invention further includes monitoring the injection molding process, which is specifically the following steps:

[0081] Step 1: Test thin-wall products and obtain test data of thin-wall products;

[0082] Among them, the detection data include: the difference ratio of the acoustic wave represents the total value and the difference ratio of the number of zones;

[0083] In some implementation schemes, thin-walled products are tested using acoustic wave technology to obtain acoustic wave difference ratios at various locations of the thin-walled products;

[0084] Based on the acoustic wave difference ratio of each position point, thin-walled products are divided into regions to obtain several product sub-regions, and the acoustic wave difference ratio representation value of each product sub-region is calculated;

[0085] Based on the acoustic wave difference ratio representation value of each product sub-area, the total acoustic wave difference ratio representation value and the area quantity difference ratio are calculated respectively;

[0086] In a first exemplary embodiment, the process of obtaining the acoustic wave difference ratio is as follows:

[0087] Based on a single location point, using an ultrasonic detection device (ultrasonic detection devices include but are not limited to:), a sound wave of fixed intensity is emitted to the location point, and the intensity of the reflected wave at the location point is received, the difference between the intensity of the emitted wave and the intensity of the reflected wave is calculated to obtain a sound wave difference, and the ratio of the sound wave difference to the intensity of the emitted wave is calculated to obtain a sound wave difference ratio;

[0088] In a second exemplary embodiment, based on the acoustic wave difference ratio at each position point, the thin-walled products are divided into regions to obtain a plurality of product sub-regions:

[0089] Based on the acoustic wave difference ratios of all position points, the maximum and minimum values ​​of the acoustic wave difference ratios are extracted, which are marked as the maximum acoustic wave difference ratio and the minimum acoustic wave difference ratio, respectively, to obtain the total range of the acoustic wave difference ratios of all position points (the minimum value of the total range of the acoustic wave difference ratios is the minimum acoustic wave difference ratio, and the maximum value of the total range of the acoustic wave difference ratios is the maximum acoustic wave difference ratio);

[0090] Dividing the total range of the sound wave difference ratio into a plurality of sound wave difference ratio sub-ranges;

[0091] Based on each sound wave difference ratio sub-range and the sound wave difference ratio of each position point, a plurality of product sub-areas are obtained (the division of the product sub-areas is based on: adjacent position points whose sound wave difference ratios are in the same sound wave difference ratio sub-range are marked as the same product sub-area);

[0092] In a third exemplary embodiment, the process of obtaining the acoustic wave difference ratio characterization value is as follows:

[0093] Based on a single product sub-area, the sound wave difference ratios of all position points in the product sub-area are averaged to obtain a sound wave difference ratio representation value of the product sub-area;

[0094] In a fourth exemplary embodiment, the process of obtaining the total value of the acoustic wave difference ratio is as follows:

[0095] Based on a single product sub-area, the acoustic wave difference ratio representation value of the product sub-area is multiplied by the number of position points of the product sub-area (the number of position points is the number of position points of the product sub-area) to obtain the acoustic wave difference ratio representation weight;

[0096] The sound wave difference ratio representation weights of all product sub-areas are summed up to obtain the total sound wave difference ratio representation value;

[0097] In a fifth exemplary embodiment, the process of obtaining the area quantity difference ratio is as follows:

[0098] Extracting the total number of the acoustic wave difference ratio sub-ranges to obtain the total number of the acoustic wave difference ratio sub-ranges;

[0099] Extract the total number of product sub-areas to obtain the total number of product sub-areas;

[0100] Calculate the difference between the total number of product sub-areas and the total number of acoustic wave difference ratio sub-ranges to obtain the zone quantity difference, calculate the ratio between the zone quantity difference and the total number of acoustic wave difference ratio sub-ranges to obtain the zone quantity difference ratio (the zone quantity difference ratio reflects the difference in quantity between the acoustic wave difference ratio sub-range and the product sub-areas. If the total number of product sub-areas is much larger than the total number of acoustic wave difference ratio sub-ranges, it indicates that the division of product sub-areas is not centralized, which indirectly reflects the discrete degree of process defects of thin-walled products. The larger the value, the more dispersed the distribution of process defects. The smaller the value, the relatively concentrated the process defects).

[0101] Step 2: Calculate the defect coefficient of thin-walled products based on the test data;

[0102] In some embodiments, the total value and the area quantity difference ratio of the acoustic wave difference ratio of the thin-walled product are obtained by the formula: , calculate and obtain the defect coefficient QX, where a1 and a2 are preset proportional factors, and both are greater than 0, CZ is the total value represented by the acoustic wave difference ratio, and QC is the zone quantity difference ratio;

[0103] Step 3: Based on the defect coefficient, analyze the quality of thin-walled products, determine whether the quality of thin-walled products is qualified, and generate a quality assessment signal;

[0104] Among them, the quality assessment signal includes: a quality qualified signal and a quality unqualified signal;

[0105] In some implementation schemes, a defect coefficient threshold is preset, and the defect coefficient is compared and analyzed with the defect coefficient threshold;

[0106] If the defect coefficient is greater than the defect coefficient threshold, it means that the process defects of the thin-walled product are large or scattered, and the quality of the thin-walled product is judged to be unqualified, that is, a quality unqualified signal is generated;

[0107] If the defect coefficient is less than or equal to the defect coefficient threshold, it means that the process defects of the thin-walled product are small and concentrated, and the quality of the thin-walled product is determined to be qualified, that is, a quality qualified signal is generated;

[0108] The technical solution of the embodiment of the present invention is mainly as follows: through a sophisticated data collection and analysis process, an efficient evaluation of product quality is achieved; first, ultrasonic detection equipment is used to perform ultrasonic detection on each position point of thin-walled products to obtain the acoustic wave difference ratio of each position point, that is, the difference ratio of the intensity of the transmitted wave and the reflected wave; then, based on the acoustic wave difference ratio data, the product is divided into multiple sub-areas, and the acoustic wave characteristics of the position points in each area are similar; by calculating the mean of the acoustic wave difference ratio in each sub-area, the acoustic wave difference ratio characterization value is obtained, and combined with the number of position points in the sub-area, the acoustic wave difference ratio characterization total value is calculated, and this indicator reflects the total amount of the overall acoustic wave characteristics of the product; at the same time, by comparing the total number of acoustic wave difference ratio sub-ranges with the total number of product sub-areas, the area number difference ratio is calculated, which reflects the concentration of the product sub-area division, that is, the discrete degree of process defects; the larger the value, It indicates that the distribution of process defects is more dispersed; the smaller the value, the relatively concentrated the process defects are; next, according to the preset formula, the total value represented by the acoustic wave difference ratio and the zone quantity difference ratio are used as input to calculate the defect coefficient; the formula comprehensively considers the total amount of the product acoustic wave characteristics and the discrete degree of the process defects by adjusting the weight of the preset proportional factor, so as to obtain an indicator reflecting the overall quality status of the product; finally, the calculated defect coefficient is compared and analyzed with the preset threshold; if the defect coefficient is greater than the threshold, the product quality is judged to be unqualified and a quality unqualified signal is generated; if the defect coefficient is less than or equal to the threshold, the product quality is judged to be qualified and a quality qualified signal is generated; through a sophisticated data collection and analysis process, the embodiment of the present invention realizes rapid and accurate evaluation of the quality of thin-walled products, and provides strong support for quality control in the production process.

[0109] Embodiment 3:

[0110] Based on Example 1 and Example 2, please refer to Figure 2 As shown, the thin-wall product injection molding process described in the embodiment of the present invention further includes the following steps:

[0111] Step 4: Based on the quality failure signal, analyze the range of process parameters with high probability of failure, so as to facilitate the selection of process parameters in the subsequent injection molding process;

[0112] In some embodiments, based on the unqualified quality signal, the analysis data of each thin-wall defective product (the thin-wall defective product is: the thin-wall product corresponding to the unqualified quality signal) in the past times (the past times are the unqualified quality signals of the previous fixed times generated based on the current unqualified quality signal, wherein the fixed times may be 50 times or 100 times) are extracted;

[0113] The analysis data include: defect coefficient of thin-wall defective products and target process parameters of the thin-wall defective products in the injection molding process (target process parameters are one of the process parameters);

[0114] Based on the analysis data of all thin-wall defective products, the maximum and minimum values ​​of the target process parameters are extracted to obtain the target process parameter range. At the same time, the target process parameter range is evenly divided into multiple parameter sub-ranges;

[0115] Calculate the defect probability characterization value of each parameter sub-range respectively; when the defect probability characterization value is greater than the parameter sub-range of the defect probability characterization threshold, generate a high-risk sub-range signal, and mark the corresponding parameter sub-range as a high-risk sub-range; when the defect probability characterization value is less than or equal to the parameter sub-range of the defect probability characterization threshold, generate a low-risk sub-range signal;

[0116] Based on the values ​​of the high-risk sub-ranges, all adjacent high-risk sub-ranges are merged in pairs until they cannot be merged, thereby obtaining multiple pre-high-risk ranges;

[0117] Calculate the risk assessment value of each pre-high risk range respectively, and mark the pre-high risk range with the largest risk assessment value as a high risk range;

[0118] In a first exemplary embodiment, the process of obtaining the defect probability characterization value is as follows:

[0119] Based on a single parameter sub-range, the number of thin-wall defective products and the defect coefficient of each thin-wall defective product within the parameter sub-range in the analysis data are extracted;

[0120] Calculate the ratio of the number of defective products in the sub-range of thin-walled products (the total number of defective products in the sub-range of thin-walled products is the number of defective products in the parameter sub-range) to the total number of defective products in the thin-walled products (the total number of defective products in the thin-walled products is the number of defective products in the analysis data) to obtain the defective product number ratio;

[0121] The defect coefficients of each thin-wall defective product are summed up to obtain the total value of the defect coefficient;

[0122] The defective product quantity ratio is multiplied by the total value of the defect coefficient to obtain a defect probability representation value;

[0123] In a second exemplary embodiment, the process of obtaining the risk assessment value is as follows:

[0124] Based on a single pre-high risk range, extract the total number of thin-wall defective product sub-ranges and defect probability characterization values ​​of each high-risk sub-range within the pre-high risk range;

[0125] Based on a single high-risk sub-range, the total number of thin-wall defective products in the high-risk sub-range is calculated by ratio to the total number of pre-high-risk defective products to obtain the high-risk defective product quantity ratio;

[0126] The ratio of the number of high-risk defective products is multiplied by the defect probability characterization value to obtain the risk sub-assessment value of the high-risk sub-range;

[0127] The risk sub-assessment values ​​of all high-risk sub-ranges within the pre-high-risk range are summed up to obtain a risk assessment value;

[0128] The technical solution of the embodiment of the present invention is mainly as follows: by analyzing the quality unqualified signals, especially the data of thin-walled defective products in detail, to identify and avoid high-risk process parameter ranges; first, review and analyze all thin-walled defective products that have caused quality unqualified signals in the recent period, and extract the defect coefficients of these products and their corresponding injection molding target process parameters; then, determine the process parameter range according to the target process parameters of these defective products, and evenly divide it into multiple sub-ranges for detailed analysis; then, for each parameter sub-range, calculate its defect probability characterization value, which comprehensively considers the proportion of the number of defective products in the sub-range and the sum of their defect coefficients; by comparing the defect probability characterization values ​​of each sub-range with a preset threshold, identify high-risk sub-ranges, that is, those parameter intervals that are more likely to cause product defects; in order to more accurately locate high-risk areas, the present invention further Merge adjacent high-risk sub-ranges until they can no longer be merged to form a pre-high-risk range; then, perform a risk assessment on each pre-high-risk range, calculate the sum of the risk sub-assessment values ​​of all the high-risk sub-ranges within it, obtain a risk assessment value, and determine the pre-high-risk range with the highest assessment value as the final high-risk range; finally, guide the selection of process parameters in the injection molding process based on the determination of the high-risk range to avoid using these high-risk parameters so as to improve product quality and production efficiency; in addition, through continuous data feedback and optimization adjustment, further reduce the generation rate of thin-walled defective products and maximize production benefits; the embodiment of the present invention facilitates timely adjustment of process parameters and avoids high-risk operations through scientific data analysis and risk assessment methods, provides an effective tool for optimizing injection molding process parameters, and helps to improve product quality and production stability.

[0129] Embodiment 4:

[0130] Based on Example 1, Example 2 and Example 3, please refer to Figure 3 As shown, a thin-walled product injection molding device according to an embodiment of the present invention comprises:

[0131] Data acquisition module: to test thin-wall products and obtain test data of thin-wall products;

[0132] Among them, the detection data include: the difference ratio of the acoustic wave represents the total value and the difference ratio of the number of zones;

[0133] Data processing module: Calculate the defect coefficient of thin-walled products based on the test data;

[0134] Quality assessment module: Analyze the quality of thin-walled products based on the defect coefficient, determine whether the quality of thin-walled products is qualified, and generate quality assessment signals;

[0135] Among them, the quality assessment signal includes: a quality qualified signal and a quality unqualified signal;

[0136] Data analysis module: Based on the quality failure signal, analyze the range of process parameters with high probability of failure.

[0137] The above thresholds are set for the convenience of comparison. The thresholds depend on the amount of sample data and the number of bases set by technicians in this field for each set of sample data.

[0138] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The factors in the formula are set by technicians in this field according to actual conditions; for example: Formula , a technician in this field collects multiple groups of test data and sets a corresponding defect coefficient for each group of test data; substitutes the set defect coefficient and the collected test data into the formula, any three formulas constitute a three-variable linear equation system, the calculated factors are screened and averaged, and the values ​​of a1 and a2 are obtained as 1.47 and 1.23 respectively;

[0139] The size of the factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the factor depends on the amount of test data and the preliminary setting of the corresponding defect coefficient for each set of test data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the defect coefficient is proportional to the value of the difference ratio of the number of zones.

[0140] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A thin-walled product injection molding process, characterized in that: include: Extract raw materials from plants to synthesize degradable PLA materials; Blending, coloring and granulating the PLA material; Dehumidifying and drying the masterbatch; sending the masterbatch after dehumidification and drying to the injection molding machine, and injection molding the masterbatch through the injection molding machine; after the injection molding is completed, taking the injection molded product out of the injection molding machine; The injection molding process is monitored, specifically the following steps: Step 1: Test thin-wall products and obtain test data of thin-wall products; Among them, the detection data include: the difference ratio of the acoustic wave represents the total value and the difference ratio of the number of zones; Step 2: Calculate the defect coefficient of thin-walled products based on the test data; Step 3: Based on the defect coefficient, analyze the quality of thin-walled products, determine whether the quality of thin-walled products is qualified, and generate a quality assessment signal; Among them, the quality assessment signal includes: a quality qualified signal and a quality unqualified signal; Step 4: Based on the quality failure signal, analyze the range of process parameters with high probability of failure; The process of testing thin-walled products and obtaining test data for thin-walled products is as follows: Test thin-walled products and obtain the acoustic wave difference ratio at each position of the thin-walled products; Based on the acoustic wave difference ratio of each position point, thin-walled products are divided into regions to obtain several product sub-regions, and the acoustic wave difference ratio representation value of each product sub-region is calculated; Based on the acoustic wave difference ratio representation value of each product sub-area, the total acoustic wave difference ratio representation value and the area quantity difference ratio are calculated respectively; Based on the acoustic wave difference ratio at each location point, the thin-walled products are divided into regions to obtain several product sub-regions: Based on the acoustic wave difference ratios of all position points, the maximum and minimum values ​​of the acoustic wave difference ratios are extracted, which are marked as the maximum acoustic wave difference ratio and the minimum acoustic wave difference ratio, respectively, to obtain the total range of the acoustic wave difference ratios of all position points; Dividing the total range of the sound wave difference ratio into a plurality of sound wave difference ratio sub-ranges; Based on each acoustic wave difference ratio sub-range, several product sub-areas are obtained according to the acoustic wave difference ratio of each position point; The process of obtaining the total value of the acoustic wave difference ratio is as follows: Based on a single product sub-area, the acoustic wave difference ratio representation value of the product sub-area is multiplied by the number of position points of the product sub-area to obtain the acoustic wave difference ratio representation weight; The sound wave difference ratio representation weights of all product sub-areas are summed up to obtain the total sound wave difference ratio representation value; The process of obtaining the difference ratio of the number of districts is as follows: Extracting the total number of the acoustic wave difference ratio sub-ranges to obtain the total number of the acoustic wave difference ratio sub-ranges; Extract the total number of product sub-areas to obtain the total number of product sub-areas; Calculate the difference between the total number of product sub-areas and the total number of acoustic wave difference ratio sub-ranges to obtain the area quantity difference, and calculate the ratio between the area quantity difference and the total number of acoustic wave difference ratio sub-ranges to obtain the area quantity difference ratio; The process of calculating the defect coefficient of thin-walled products is: By formula: , calculate and obtain the defect coefficient QX, where a1 and a2 are preset proportional factors, and both are greater than 0, CZ is the total value represented by the acoustic wave difference ratio, and QC is the zone quantity difference ratio; Based on the quality failure signal, the process of analyzing the range of high probability of process parameter failure is as follows: Based on the quality failure signal, extract the analysis data of each thin-wall defective product in the past times; The analysis data include: defect coefficient of thin-wall defective products and target process parameters of the thin-wall defective products during injection molding; Based on the analysis data of all thin-wall defective products, the maximum and minimum values ​​of the target process parameters are extracted to obtain the target process parameter range. At the same time, the target process parameter range is evenly divided into multiple parameter sub-ranges; Calculate the defect probability characterization value of each parameter sub-range respectively; when the defect probability characterization value is greater than the parameter sub-range of the defect probability characterization threshold, generate a high-risk sub-range signal, and mark the corresponding parameter sub-range as a high-risk sub-range; when the defect probability characterization value is less than or equal to the parameter sub-range of the defect probability characterization threshold, generate a low-risk sub-range signal; Based on the values ​​of the high-risk sub-ranges, all adjacent high-risk sub-ranges are merged in pairs until they cannot be merged, thereby obtaining multiple pre-high-risk ranges; Calculate the risk assessment value of each pre-high risk range respectively, and mark the pre-high risk range with the largest risk assessment value as a high risk range; The process of obtaining risk assessment value is as follows: Based on a single pre-high risk range, extract the total number of thin-wall defective product sub-ranges and defect probability characterization values ​​of each high risk sub-range within the pre-high risk range; Based on a single high-risk sub-range, the total number of thin-wall defective products in the high-risk sub-range is calculated by ratio to the total number of pre-high-risk defective products to obtain a high-risk defective product quantity ratio; Calculate the product of the ratio of the number of high-risk defective products and the defect probability characterization value to obtain the risk sub-assessment value of the high-risk sub-range; The risk sub-assessment values ​​of all high-risk sub-ranges within the pre-high-risk range are summed up to obtain a risk assessment value; Among them, the process of obtaining the defect probability characterization value is: Based on a single parameter sub-range, extracting the number of thin-wall defective products and the defect coefficient of each thin-wall defective product within the parameter sub-range in the analysis data; Calculate the ratio of the total number of thin-wall defective products in the sub-range to the total number of thin-wall defective products to obtain the defective product quantity ratio; The defect coefficients of each thin-wall defective product are summed up to obtain the total value of the defect coefficient; The defect probability representation value is obtained by multiplying the defect product quantity ratio and the total defect coefficient value.

2. The thin-wall product injection molding process according to claim 1, characterized in that: And the process of generating the quality assessment signal is: Preset the defect coefficient threshold, and compare and analyze the defect coefficient with the defect coefficient threshold; If the defect coefficient is greater than the defect coefficient threshold, a quality failure signal is generated; If the defect coefficient is less than or equal to the defect coefficient threshold, a quality qualified signal is generated.

3. A thin-walled product injection molding device, characterized in that: The thin-walled product injection molding device is used to perform the thin-walled product injection molding process as described in any one of claims 1 to 2, and the thin-walled product injection molding device comprises: Data acquisition module: to test thin-wall products and obtain test data of thin-wall products; Wherein, the detection data includes: the total value represented by the acoustic wave difference ratio and the zone quantity difference ratio; Data processing module: calculating the defect coefficient of thin-walled products based on the detection data; Quality assessment module: based on the defect coefficient, analyzes the quality of thin-wall products, determines whether the quality of thin-wall products is qualified, and generates a quality assessment signal; Wherein, the quality assessment signal includes: a qualified quality signal and an unqualified quality signal; Data analysis module: Based on the quality failure signal, analyze the range of process parameters with high probability of failure.

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