Injection molding control method and device for automobile parts

Abstract

The invention discloses an injection molding control method and device for automobile parts, and relates to the technical field of injection molding control, the injection molding condition similarity coefficient is determined by obtaining part volume, melt flow index and wall thickness data of a first part and each second part and performing clustering analysis; a second part most similar to the first part is selected as a third part, and the optimal injection molding parameters of the third part serve as initial injection molding parameters of the first part; obtaining mold complexity and surface smoothness data of the first part and the third part, calculating an adjustment coefficient, and adjusting the initial injection molding parameter according to the adjustment coefficient to obtain a second injection molding key parameter; performing injection molding control by taking the second injection molding key parameter as an initial injection molding parameter of the first part; by means of the process, initial injection molding parameters can be reasonably set for new automobile parts, waste and production cost is reduced, meanwhile, the optimization process is short, and the production period is shortened.

Description

Technical Field

[0001] The invention relates to the technical field of injection molding control, and in particular to an injection molding control method and device for automobile parts. Background Art

[0002] Injection molding of automotive parts refers to the process of using thermoplastics to melt at high temperature and inject them into the mold through an injection molding machine, and then forming automotive parts after cooling; this process is widely used to manufacture various complex shapes and lightweight automotive parts, such as instrument panels, headlight housings, dashboards, interior trims, etc.; the advantage of injection molding is that it can achieve high-precision, large-scale production, and can use lower-cost plastic materials to manufacture parts with high strength and good surface quality. The process involves precise control of multiple key injection molding parameters such as injection pressure, injection time, holding time, holding pressure, and cooling time to ensure the dimensional accuracy, surface finish and functionality of parts. With the increasing requirements of the automotive industry for the performance and lightweight of parts, injection molding technology plays an increasingly important role in automobile manufacturing.

[0003] In the actual injection molding process of automobile parts, for new automobile parts that have not undergone injection molding, the initial setting of injection molding key parameters is very important; the automobile parts are injection molded by setting the initial injection molding key parameters, and the injection molding key parameters are continuously optimized according to the subsequent injection molding results to find the best injection molding key parameters for automobile parts; however, in the actual injection molding process, if the initial injection molding key parameters of new automobile parts that have not undergone injection molding are improperly set, a large amount of waste may be caused, which increases production costs and also leads to a long optimization process of the best injection molding key parameters, resulting in a long production cycle. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems and provide an injection molding control method and device for automobile parts.

[0005] In a first aspect of the present invention, a method for controlling injection molding of automotive parts is first proposed, the method comprising: A new automobile part that has not been subjected to injection molding is recorded as a first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as a second part; Obtaining the part volume, melt flow index and wall thickness of the first component and each second component, calculating the part volume difference, melt flow index difference and wall thickness difference of each second component and using them as data points of each second component, obtaining a data set, clustering the data set to determine the injection molding condition similarity coefficient of each second component and the first component; The second component with the largest injection molding condition similarity coefficient is used as the third component, and the optimal injection molding key parameter corresponding to the third component is used as the first injection molding key parameter of the first component; The mold complexity data and surface smoothness data of the first component and the third component are obtained to obtain the adjustment coefficient of the first component, and the first injection molding key parameter is adjusted according to the adjustment coefficient to obtain the second injection molding key parameter, and the second injection molding key parameter is used as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0006] Optionally, obtaining the part volume, melt flow index and wall thickness of the first component and each second component to calculate the part volume difference, melt flow index difference and wall thickness difference of each second component includes: Obtaining the volumes of the first component and each second component, and calculating the absolute difference between the volumes of the first component and each second component as the part volume difference of each second component; Obtaining the melt flow index of the first component and each second component, and calculating the absolute difference between the melt flow index of the first component and each second component as the melt flow index difference of each second component; The average wall thickness of the first component and each second component is obtained, and the absolute difference between the average wall thickness of the first component and each second component is calculated as the wall thickness difference of each second component.

[0007] Optionally, clustering the data set to determine the similarity coefficient of injection molding conditions between each second component and the first component includes: The steps to cluster the data set according to the K-means clustering method are as follows: Step 1: Use the elbow method to determine the optimal number of clusters K for the data set; Step 2: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center, traverse the K initial cluster centers, and assign it to the cluster corresponding to the nearest initial cluster center; Step 3: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 4: Repeat steps 2 and 3 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain the similarity coefficients of injection molding conditions between the second components and the first components.

[0008] Optionally, the final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain similarity coefficients of injection molding conditions between the second components and the first components, including: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The reciprocal of the actual coordinate distance of each data point is used as the injection molding condition similarity coefficient of the corresponding second component and the first component.

[0009] Optionally, obtaining mold complexity data and surface smoothness data of the first component and the third component to obtain the adjustment coefficient of the first component includes: Obtaining the total length of the channels and the total number of channels in the molds of the first component and the third component respectively, and calculating the difference between the total length of the channels in the molds of the first component and the third component and the difference between the total number of channels in the molds of the first component and the third component; The total number of channels is divided by the total number of channels of the third component to obtain the total number of channels difference ratio; the total length of channels is divided by the channel length of the third component to obtain the total length of channels difference ratio; The number of holes in the molds of the first component and the third component and the diameter and depth of each hole are obtained respectively, the diameter of each hole in the molds of the first component and the third component is multiplied by the depth respectively to obtain the complexity value of each hole, the sum of the complexity values ​​of all holes is calculated, and the aperture complexity values ​​of the first component and the third component are obtained respectively; the difference between the aperture complexity values ​​of the first component and the third component is calculated as the aperture difference value; The mold complexity coefficient of the first component is obtained by weighted summing the difference ratio of the total number of channels, the difference ratio of the total length of channels, and the difference in aperture; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness data.

[0010] Optionally, obtaining the adjustment coefficient of the first component according to the mold complexity coefficient and the surface smoothness data includes: Obtaining a preset surface smoothness value of the first component during injection molding, and obtaining a surface smoothness value of the third component during injection molding from the historical records, and calculating a difference between the preset surface smoothness value of the first component during injection molding and the surface smoothness value of the third component during injection molding as a surface smoothness difference coefficient of the first component; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness difference coefficient. The calculation formula is:

[0011] In the formula, is the adjustment coefficient, are the mold complexity coefficient and surface smoothness difference coefficient, They are The preset scaling factor of Both are greater than 0.

[0012] Optionally, adjusting the first injection molding key parameter according to the adjustment coefficient to obtain the second injection molding key parameter includes:

[0013] In the formula, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the first injection key parameters. is the adjustment coefficient, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the second key injection molding parameters.

[0014] In a second aspect of the present invention, an injection molding control device for automobile parts is provided, the device comprising: Marking module: a new automobile part that has not been subjected to injection molding is recorded as the first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as the second part; Cluster analysis module: obtain the part volume, melt flow index and wall thickness of the first component and each second component, calculate the part volume difference, melt flow index difference and wall thickness difference of each second component and use them as data points of each second component to obtain a data set, cluster the data set to determine the injection molding condition similarity coefficient of each second component and the first component; Corresponding module: the second component with the largest similarity coefficient of injection molding conditions is used as the third component, and the optimal injection molding key parameters corresponding to the third component are used as the first injection molding key parameters of the first component; Control module: obtain mold complexity data and surface smoothness data of the first component and the third component, obtain the adjustment coefficient of the first component, adjust the first injection molding key parameter according to the adjustment coefficient, obtain the second injection molding key parameter, and use the second injection molding key parameter as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0015] Beneficial effects of the present invention: The present invention proposes an injection molding control method and device for automobile parts. The method comprises the following steps: recording a new automobile part that has not undergone injection molding as a first part, recording a part that has completed injection molding and has the best injection molding parameters as a second part, obtaining the part volume, melt flow index and wall thickness data of the first part and each second part, and performing cluster analysis to determine the similarity coefficient of injection molding conditions; selecting the second part that is most similar to the first part as the third part, and using its best injection molding parameters as the preliminary injection molding parameters of the first part; obtaining the mold complexity and surface smoothness data of the first part and the third part, calculating the adjustment coefficient, and adjusting the preliminary injection molding parameters according to the adjustment coefficient to obtain the second injection molding key parameters; finally, using the second injection molding key parameters as the initial injection molding parameters of the first part to perform injection molding control; in this way, for new automobile parts that have not undergone injection molding, the initial injection molding key parameters of the new automobile parts can be reasonably set according to automobile parts that have undergone injection molding and have found the best injection molding key parameters, thereby reducing the production of waste products, reducing production costs, and shortening the optimization process of the best injection molding key parameters of the new automobile parts, thereby reducing the production cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 is a flow chart of an injection molding control method for automobile parts; Figure 2 The present invention is a framework diagram of an injection molding control device for automotive parts. DETAILED DESCRIPTION

[0018] 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.

[0019] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0020] The embodiment of the present invention provides an injection molding control method for automobile parts. Figure 1 , Figure 1 A flowchart of an injection molding control method for automobile parts provided by an embodiment of the present invention. The method comprises the following steps: A new automobile part that has not been subjected to injection molding is recorded as a first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as a second part; Obtaining the part volume, melt flow index and wall thickness of the first component and each second component, calculating the part volume difference, melt flow index difference and wall thickness difference of each second component and using them as data points of each second component, obtaining a data set, clustering the data set to determine the injection molding condition similarity coefficient of each second component and the first component; The second component with the largest injection molding condition similarity coefficient is used as the third component, and the optimal injection molding key parameter corresponding to the third component is used as the first injection molding key parameter of the first component; The mold complexity data and surface smoothness data of the first component and the third component are obtained to obtain the adjustment coefficient of the first component, and the first injection molding key parameter is adjusted according to the adjustment coefficient to obtain the second injection molding key parameter, and the second injection molding key parameter is used as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0021] Based on an injection molding control method for automobile parts provided by an embodiment of the present invention, through the above-mentioned method, for new automobile parts that have not undergone injection molding, the initial injection molding key parameters of the new automobile parts can be reasonably set according to automobile parts that have undergone injection molding and for which the best injection molding key parameters have been found, thereby reducing the production of waste and production costs while shortening the optimization process of the best injection molding key parameters for new automobile parts and shortening the production cycle.

[0022] In one embodiment, obtaining the part volume, melt flow index, and wall thickness of the first component and each second component and calculating the part volume difference, melt flow index difference, and wall thickness difference of each second component includes: Obtaining the volumes of the first component and each second component, and calculating the absolute difference between the volumes of the first component and each second component as the part volume difference of each second component; Obtaining the melt flow index of the first component and each second component, and calculating the absolute difference between the melt flow index of the first component and each second component as the melt flow index difference of each second component; The average wall thickness of the first component and each second component is obtained, and the absolute difference between the average wall thickness of the first component and each second component is calculated as the wall thickness difference of each second component.

[0023] It should be noted that the volume of new auto parts that have not been injection molded (first parts) and auto parts that have been injection molded (second parts) can be obtained through CAD models; specifically, CAD software (such as SolidWorks, AutoCAD, etc.) can be used to directly extract the three-dimensional geometric data of parts, and the volume calculation tool of the software can be used to calculate the volume of each part; Melt flow index refers to the index of fluidity of plastic materials in the molten state; the larger the melt flow index, the greater the fluidity of the material used for injection molding of automotive parts; generally speaking, the melt flow index of different plastic materials is provided by material suppliers or plastic manufacturers, and each plastic material has a corresponding melt flow index range; the melt flow index of the first component can be obtained by querying the data provided by the material supplier or plastic manufacturer based on the plastic material determined to be used for injection molding of the first component; the melt flow index of each second component can be obtained by querying the data provided by the material supplier or plastic manufacturer based on the corresponding plastic material used in injection molding; In addition, the average wall thickness of the first component and each second component can be obtained through a CAD model or other three-dimensional modeling tools. The average wall thickness of the part can be calculated using the wall thickness analysis tool in CAD software (such as SolidWorks, CATIA, etc.). The tool automatically analyzes the wall thickness of the part at different locations and obtains the overall average wall thickness by weighted average. If it is a molded part, more accurate wall thickness data can be obtained through physical measurement (such as calipers, micrometers) or by using industrial CT scanning technology. Wall thickness refers to the thickness of the component material, usually the distance from the outer surface to the inner surface of the part; it is a key parameter in injection molding, which directly affects the strength, rigidity, fluidity of the part and the cooling time during the molding process.

[0024] It should be noted that the smaller the volume difference between the first component and the second component, the smaller the difference in melt flow index, and the smaller the difference in wall thickness, the more suitable the corresponding optimal injection molding key parameters of the second component are as the initial injection molding key parameters of the first component. The reason is that: the smaller the volume difference between the first component and the second component, the more similar they are in size, and the fluidity and filling capacity are also closer, which means that they have similar requirements for parameters such as injection pressure and injection time during the injection molding process; the smaller the difference in melt flow index, the similar material fluidity of the two, which means similar requirements for pressure and temperature during the injection molding process; the smaller the difference in wall thickness, the closer the requirements for cooling time and holding time during the molding process, so the optimal injection molding key parameters of the second component are more suitable as the initial injection molding key parameters of the first component. In general, the smaller the difference, the more similar the physical properties and injection molding process requirements of the two are, and the optimized initial injection molding parameters can provide molding support for the first component more efficiently, reducing the difficulty of adjustment and optimization.

[0025] In one embodiment, clustering the data set to determine the similarity coefficient of the injection molding conditions of each second component and the first component includes: The steps to cluster the data set according to the K-means clustering method are as follows: Step 1: Use the elbow method to determine the optimal number of clusters K for the data set; Step 2: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center, traverse the K initial cluster centers, and assign it to the cluster corresponding to the nearest initial cluster center; Step 3: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 4: Repeat steps 2 and 3 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain the similarity coefficients of injection molding conditions between the second components and the first components.

[0026] In one implementation, according to the above method, the optimal clustering results of the data set can be obtained. These clustering results can be used to identify different clusters in the data set, as well as the center and characteristics of each cluster. Such clustering results are helpful for further data analysis and interpretation.

[0027] It should be noted that clustering the data set by K-means clustering is simple and efficient, and can quickly process large-scale data sets; K-means clustering is a commonly used unsupervised learning algorithm, which is used to divide the data set into pre-defined K clusters; the goal of this algorithm is to divide the data points into K clusters, so that each data point belongs to the cluster represented by the cluster center closest to it, and the data points within the cluster are as similar as possible, while the data points between different clusters may not be similar; Cluster analysis can help evaluate the similarity of injection molding conditions between each second component and the first component; if the volume difference of the second component is smaller, the melt flow index difference is smaller, and the wall thickness difference is smaller, it means that the injection molding conditions of the second component and the first component are more similar, so the corresponding optimal injection molding key parameters of the second component are more suitable as the initial injection molding key parameters of the first component; conversely, it means that the corresponding optimal injection molding key parameters of the second component are less suitable as the initial injection molding key parameters of the first component; in addition, in this case, the K-means clustering method can effectively identify the differences in physical properties and injection molding process requirements between these second components and other second components by classifying these devices into a cluster; specifically, the results of cluster analysis can divide the devices into different groups, and some groups may show a higher injection molding condition similarity index, which means that the smaller the volume difference of the second component, the smaller the melt flow index difference, and the smaller the wall thickness difference. Therefore, cluster analysis of these data points can help identify which second component has the most similar injection molding conditions to the first component, so as to ensure that the initial injection molding key parameters of the final first component are the most appropriate, reduce the scrap rate of the first component injection molding, and shorten the cycle of optimizing the optimal initial injection molding key parameters when the first component is injected; Therefore, by taking the part volume difference, melt flow index difference and wall thickness difference between each second component and the first component as the data point of each second component, a data set is obtained, and the data set is clustered to determine the injection molding condition similarity coefficient of each second component and the first component; and the volume difference, melt flow index difference, wall thickness difference and the inverse of the injection molding condition similarity coefficient between each second component and the first component present a common linear relationship. The smaller the volume difference, melt flow index difference and wall thickness difference between the second component and the first component, the larger the injection molding condition similarity coefficient of the corresponding second component and the first component.

[0028] It should be noted that, first, the volume difference, melt flow index difference, and wall thickness difference between each second component and the first component are used as data points of each second component. When these three differences are used as the characteristics of each energy storage device, the data point of each energy storage device can be expressed as a three-dimensional vector, that is, [volume difference, melt flow index difference, wall thickness difference], and the corresponding data points of all second components constitute a data set; Cluster the data set according to the K-means clustering method: S1: Use the elbow method to determine the optimal number of clusters K for a data set; the elbow method is one of the common methods used to determine the optimal number of clusters K in K-means clustering. The basic idea is to find an "elbow point" by observing the relationship between the number of clusters K and the total internal deviation square sum (WCSS) of the clustering results. The K value corresponding to the "elbow point" is the position where the rate of decrease of WCSS slows down significantly after the number of clusters increases to a certain extent. WCSS refers to the sum of the squares of the distances from each data point to the center of the cluster to which it belongs, which indicates the compactness of the clustering results.

[0029] S2: According to the optimal number of clusters K, randomly select K data points from the data points as the initial cluster centers of clustering. The initial cluster centers refer to the K cluster centers randomly selected before starting the clustering process. These initial cluster centers are the starting points when the algorithm starts; S3: For each data point in the data set, the distance between it and each initial cluster center is calculated using the Euclidean distance. For example, the data point and the initial cluster centers The distance between , and its calculation formula is ,in, Represents data points In the The values ​​in the clusters, Represents the initial cluster center In the The value of the clusters, Represented as data points and the initial cluster centers The number of features is calculated, and the data points are assigned to the cluster to which the nearest cluster center belongs. Euclidean distance refers to the distance between two points in Euclidean space, and is also one of the most commonly used distance metrics. In K-means clustering, Euclidean distance is usually used to calculate the distance between data points and cluster centers. S4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated. The calculation formula is: ,in Expressed as The set of data points in a cluster, Expressed as The new cluster centers of the clusters; S5: Repeat S3 and S4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0030] It should be specifically explained that the step of assigning a data point to the cluster to which the nearest cluster center belongs is to sort the distances between the data point and each cluster center from large to small, and select the smallest distance to assign the data point to the cluster to which it belongs.

[0031] In one embodiment, the final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain the similarity coefficients of injection molding conditions between the second components and the first components, including: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The reciprocal of the actual coordinate distance of each data point is used as the injection molding condition similarity coefficient of the corresponding second component and the first component.

[0032] In one implementation, the above method combines the factors of volume difference, melt flow index difference, and wall thickness difference in multiple dimensions, which can more comprehensively evaluate the similarity of the injection molding conditions of the second component and the first component. The data points are grouped by K-means clustering method, and the influence of each device is adjusted according to the weight coefficient of each cluster, so that the clustering result not only reflects the status of the device itself, but also considers its relative importance in the entire system; in addition, by combining the actual coordinate distance of the device with the weight coefficient, the similarity of the injection molding conditions of each second component and the first component can be quantified more accurately.

[0033] It should be noted that, because the volume difference, melt flow index difference, and wall thickness difference are selected as data points for clustering, these three data points can now be used as coordinates and mapped to the three-dimensional coordinate axis. Each injection molding condition similarity coefficient between the second component and the first component will be mapped to a point on the three-dimensional coordinate axis. After clustering is completed, all data points will be distributed in different final clusters on the three-dimensional coordinate axis. The closer the final cluster is to the original center and the larger the injection molding condition similarity coefficient between the second component and the first component corresponding to the data point included therein is, the more suitable the corresponding optimal injection molding key parameter of the second component is as the initial injection molding key parameter of the first component. On the contrary, the smaller the value is, the less suitable the corresponding optimal injection molding key parameter of the corresponding second component is as the initial injection molding key parameter of the first component. At the same time, the weight coefficient of each final cluster is calculated to correct the distance from the data point to the original center. In this way, the different importance of each final cluster can be ensured, and the influence of noise and outliers on the clustering results can be reduced to a certain extent, so that the final calculated injection molding condition similarity coefficient of each second component and the first component is more accurate.

[0034] In one embodiment, the second component with the largest injection molding condition similarity coefficient is used as the third component, and the optimal injection molding key parameters corresponding to the third component are used as the first injection molding key parameters of the first component; the mold complexity data and surface smoothness data of the first component and the third component are obtained to obtain the adjustment coefficient of the first component, and the first injection molding key parameters are adjusted according to the adjustment coefficient to obtain the second injection molding key parameters, and the second injection molding key parameters are used as the initial injection molding key parameters of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0035] In one implementation, the second component with the largest similarity coefficient of injection molding conditions is used as the third component, and the best injection molding key parameters corresponding to the third component are used as the first injection molding key parameters of the first component. The benefit of selecting the second component with the largest similarity coefficient of injection molding conditions as the third component. The third component represents the component that is most similar to the first component in terms of physical properties and injection molding requirements. Therefore, the best injection molding key parameters corresponding to the third component (such as injection pressure, injection time, holding pressure, holding time and cooling time) are used as the first injection molding key parameters of the first component. These initial injection molding parameters provide a basis for the subsequent injection molding process; in addition, by analyzing and finally obtaining the initial injection molding key parameters of the first component through the above steps, the accuracy and efficiency of the injection molding process can be significantly improved; firstly, this method can avoid the trial and error process from scratch by referring to the injection molding parameters of similar components that have undergone injection molding, and reduce the blindness of the initial parameter setting. Secondly, by adjusting the initial injection molding parameters by considering specific factors such as mold complexity and surface smoothness, it can better adapt to the special needs of the first component, thereby improving the molding quality and reducing the occurrence rate of defects such as shrinkage, bubbles, short shots, etc. In addition, the adjusted initial injection molding key parameters make the production process more controllable, optimize the injection molding cycle, reduce production costs and shorten the production cycle, which helps to improve production efficiency and ensure batch consistency. Overall, this systematic parameter adjustment method can not only improve the molding quality of the first component, but also greatly improve the stability and economy of the production process.

[0036] Specifically, the mold complexity data and surface smoothness data of the first component and the third component are obtained, and the adjustment coefficient of the first component is obtained, including: Obtaining the total length of the channels and the total number of channels in the molds of the first component and the third component respectively, and calculating the difference between the total length of the channels in the molds of the first component and the third component and the difference between the total number of channels in the molds of the first component and the third component; The total number of channels is divided by the total number of channels of the third component to obtain the total number of channels difference ratio; the total length of channels is divided by the channel length of the third component to obtain the total length of channels difference ratio; The number of holes in the molds of the first component and the third component and the diameter and depth of each hole are obtained respectively, the diameter of each hole in the molds of the first component and the third component is multiplied by the depth respectively to obtain the complexity value of each hole, the sum of the complexity values ​​of all holes is calculated, and the aperture complexity values ​​of the first component and the third component are obtained respectively; the difference between the aperture complexity values ​​of the first component and the third component is calculated as the aperture difference value; The mold complexity coefficient of the first component is obtained by weighted summing the difference ratio of the total number of channels, the difference ratio of the total length of channels, and the difference in aperture; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness data.

[0037] It should be noted that the mold complexity coefficient is obtained by weighted summing the difference ratio of the total number of channels, the difference ratio of the total length of channels, and the aperture difference. The calculation formula is: , where is the mold complexity coefficient, They are the total number of channels phase difference, total channel length phase difference, and aperture difference, They are The preset scaling factor of Both are greater than 0.

[0038] It should be noted that It is set by professionals according to the actual situation. Generally, The sum is 1, and the specific value is not limited; in addition, before calculating the mold complexity coefficient, it is necessary to calculate the total number of channels, the total length of the channels, and the aperture difference without unit.

[0039] It should be noted that the total length and number of channels in the molds of the first and third components can be directly extracted from the mold design drawings or CAD models; secondly, the number, diameter and depth of holes in the mold can also be obtained. These precise dimensions can usually be obtained from the CAD model or mold production process; It should be noted that when the mold complexity coefficient of the first component is larger, the corresponding adjustment coefficient is larger, and the corresponding second injection molding key parameters are all adjusted larger on the basis of the first injection molding key parameters. The reason is that when the mold complexity coefficient is larger, it means that the design and structure of the mold are more complex, the channel length is longer, the number of channels is more, and the aperture is finer, which means that during the injection molding process, the flow, cooling, pressure distribution, etc. of the material will face higher challenges. Therefore, it is necessary to increase the injection pressure, extend the injection time, increase the holding pressure and holding time to ensure that the melt can smoothly fill every detail of the mold, while maintaining a longer cooling time to ensure the quality and strength of the molding. By improving these injection molding key parameters, it is possible to better adapt to the needs of complex molds, avoid problems such as uneven flow, incomplete filling or molding defects during the injection molding process, and ensure high-quality production of parts.

[0040] In one implementation, the benefit of analyzing the mold complexity coefficient of the first component for adjusting the second key injection molding parameters is that the mold complexity coefficient can accurately reflect the complexity of the mold design and the challenges in the molding process. Through this coefficient, the impact of the mold on melt flow, pressure distribution and cooling efficiency can be reasonably inferred, thereby helping production personnel to make appropriate adjustments based on the initial injection molding parameters. For example, when the mold complexity is high, by increasing the injection pressure, extending the injection time, and increasing the holding pressure and cooling time, the complexity of the mold can be better coped with, uniform filling can be ensured, and defects caused by insufficient pressure or too fast cooling can be prevented. At the same time, this adjustment helps to improve the stability and consistency of production, reduce the number of mold trials, thereby reducing production costs and shortening production cycles.

[0041] In one embodiment, obtaining the adjustment coefficient of the first component according to the mold complexity coefficient and the surface smoothness data includes: Obtaining a preset surface smoothness value of the first component during injection molding, and obtaining a surface smoothness value of the third component during injection molding from the historical records, and calculating a difference between the preset surface smoothness value of the first component during injection molding and the surface smoothness value of the third component during injection molding as a surface smoothness difference coefficient of the first component; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness difference coefficient. The calculation formula is:

[0042] In the formula, is the adjustment coefficient, are the mold complexity coefficient and surface smoothness difference coefficient, They are The preset scaling factor of Both are greater than 0.

[0043] It should be noted that It is set by professionals according to the actual situation. Generally, The sum of is 1, and the specific value is not limited; in addition, before calculating the adjustment coefficient, it is necessary to remove the unit from the mold complexity coefficient and the surface smoothness difference coefficient and then calculate; It should be noted that the surface smoothness value is an indicator to measure the smoothness of the surface of a component, usually expressed as the reciprocal of the surface roughness value (Ra value); the Ra value refers to the average deviation of the surface profile of a component within a unit length. The smaller the value, the smoother the surface, and the larger the corresponding surface smoothness value; the surface smoothness of the third component can be obtained through relevant production records or test data; these data are usually stored in production equipment or quality control systems and can be retrieved through database retrieval; and the preset surface smoothness value of the first component during injection molding is generally set by professionals based on the actual component, and no specific limitation or elaboration is made.

[0044] It should be noted that when the surface smoothness difference coefficient of the first component is larger, the corresponding adjustment coefficient is larger, and the corresponding second injection molding key parameters are all adjusted larger on the basis of the first injection molding key parameters. The reason is that the surface smoothness directly affects the fluidity of the material and the filling uniformity of the mold during the injection molding process. The larger the surface smoothness difference coefficient, the greater the difference between the surface smoothness of the first component and the third component, which may mean that surface defects, uneven flow or uneven cooling may occur during the molding process, thereby affecting the overall quality of the component. In order to make up for this difference, it is necessary to ensure that the molten material can fully fill the mold by increasing the injection pressure, injection time, holding pressure and other parameters during the injection molding process, and ensure that its surface smoothness meets the expected standard during the injection molding process. This adjustment helps to overcome the problems caused by surface differences and ensure that the molding effect meets the quality requirements. Therefore, when the surface smoothness difference coefficient is large, the corresponding injection molding key parameters need to be increased accordingly to ensure the stability of the injection molding process and the molding quality.

[0045] In one implementation, the benefit of analyzing the surface smoothness difference coefficient of the first component for adjusting the second injection molding key parameters is that the surface smoothness difference coefficient can help identify surface defects or quality fluctuations that may occur between the first component and the reference component during the molding process. When the difference coefficient is large, it indicates that the surface smoothness difference is significant, which may cause defects such as bubbles, flow marks, and deviations during the injection molding process, thereby affecting the appearance and performance of the component. By adjusting key parameters such as injection molding pressure, time, and cooling according to the surface smoothness difference coefficient, this difference can be effectively compensated to ensure that the molten material flows evenly and achieves a good surface quality, thereby improving the final quality and stability of the component. This not only reduces the scrap rate, but also shortens the debugging time, effectively improving production efficiency and yield rate.

[0046] In one embodiment, adjusting the first injection molding key parameter according to the adjustment coefficient to obtain the second injection molding key parameter includes:

[0047] In the formula, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the first injection key parameters. is the adjustment coefficient, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the second key injection molding parameters.

[0048] The second injection molding key parameter is used as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0049] In one implementation, the benefit of using the second injection molding key parameter as the initial injection molding key parameter of the first component to control the injection molding of the first component is that it can optimize the injection molding process by adjusting the coefficient based on the existing historical data and actual production experience, ensuring that the various parameters in the molding process match the molding experience of similar components, thereby reducing the time for mold trial and adjustment and improving production efficiency. In this way, the incidence of component defects can be greatly reduced, the accuracy and consistency of injection molding can be improved, and at the same time, it can better adapt to the differences in mold complexity and surface smoothness of different components, ensuring that the molding quality of components meets the expected standards and reducing waste and costs in the production process.

[0050] Based on the same inventive concept, the embodiment of the present invention also provides an injection molding control device for automobile parts. Figure 2 , Figure 2 A framework diagram of an injection molding control device for automobile parts provided by an embodiment of the present invention, the device comprising: Marking module: a new automobile part that has not been subjected to injection molding is recorded as the first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as the second part; Cluster analysis module: obtain the part volume, melt flow index and wall thickness of the first component and each second component, calculate the part volume difference, melt flow index difference and wall thickness difference of each second component and use them as data points of each second component to obtain a data set, cluster the data set to determine the injection molding condition similarity coefficient of each second component and the first component; Corresponding module: the second component with the largest similarity coefficient of injection molding conditions is used as the third component, and the optimal injection molding key parameters corresponding to the third component are used as the first injection molding key parameters of the first component; Control module: obtains mold complexity data and surface smoothness data of the first component and the third component, obtains the adjustment coefficient of the first component, adjusts the first injection molding key parameter according to the adjustment coefficient, obtains the second injection molding key parameter, and uses the second injection molding key parameter as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

[0051] Based on an injection molding control device for automobile parts provided by an embodiment of the present invention, through the above method, for new automobile parts that have not undergone injection molding, the initial injection molding key parameters of the new automobile parts can be reasonably set according to automobile parts that have undergone injection molding and for which the best injection molding key parameters have been found, thereby reducing the production of waste and production costs while shortening the optimization process of the best injection molding key parameters for new automobile parts and shortening the production cycle.

[0052] 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 used to artificially 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 method for controlling injection molding of automobile parts, characterized in that: The following steps are involved: A new automobile part that has not been subjected to injection molding is recorded as a first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as a second part; Obtaining the part volume, melt flow index and wall thickness of the first component and each second component, calculating the part volume difference, melt flow index difference and wall thickness difference of each second component and using them as data points of each second component, obtaining a data set, clustering the data set to determine the injection molding condition similarity coefficient of each second component and the first component; The second component with the largest injection molding condition similarity coefficient is used as the third component, and the optimal injection molding key parameter corresponding to the third component is used as the first injection molding key parameter of the first component; The mold complexity data and surface smoothness data of the first component and the third component are obtained to obtain the adjustment coefficient of the first component, and the first injection molding key parameter is adjusted according to the adjustment coefficient to obtain the second injection molding key parameter, and the second injection molding key parameter is used as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

2. The injection molding control method for automobile parts according to claim 1, characterized in that: Obtaining the part volume, melt flow index and wall thickness of the first component and each second component and calculating the part volume difference, melt flow index difference and wall thickness difference of each second component include: Obtaining the volumes of the first component and each second component, and calculating the absolute difference between the volumes of the first component and each second component as the part volume difference of each second component; Obtaining the melt flow index of the first component and each second component, and calculating the absolute difference between the melt flow index of the first component and each second component as the melt flow index difference of each second component; The average wall thickness of the first component and each second component is obtained, and the absolute difference between the average wall thickness of the first component and each second component is calculated as the wall thickness difference of each second component.

3. The injection molding control method for automobile parts according to claim 1, characterized in that: Clustering the data set to determine the similarity coefficients of the injection molding conditions of each second component and the first component includes: The steps to cluster the data set according to the K-means clustering method are as follows: Step 1: Use the elbow method to determine the optimal number of clusters K for the data set; Step 2: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center, traverse the K initial cluster centers, and assign it to the cluster corresponding to the nearest initial cluster center; Step 3: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 4: Repeat steps 2 and 3 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; The final clusters and the final cluster centers are mapped to three-dimensional coordinates to obtain the similarity coefficients of injection molding conditions between the second components and the first components.

4. The injection molding control method for automobile parts according to claim 3, characterized in that: The final clusters and the final cluster centers are mapped to three-dimensional coordinates, and the similarity coefficients of the injection molding conditions of the second components and the first components are obtained, including: Calculate the weight coefficient of each final cluster, the calculation formula is: ,in Expressed as The weight coefficients of the final clusters, Represented as the number of all data points in the dataset, Indicates The number of data points in the final clusters; Calculate the distance of each data point in each final cluster to the coordinate origin in three-dimensional coordinates to obtain the preliminary coordinate distance of the data point, and multiply the preliminary coordinate distance of the data point by the weight coefficient corresponding to the final cluster in which the data point is located to obtain the actual coordinate distance of the data point; The reciprocal of the actual coordinate distance of each data point is used as the injection molding condition similarity coefficient of the corresponding second component and the first component.

5. The injection molding control method for automobile parts according to claim 1, characterized in that: Obtaining mold complexity data and surface smoothness data of the first component and the third component, and obtaining the adjustment coefficient of the first component includes: Obtaining the total length of the channels and the total number of channels in the molds of the first component and the third component respectively, and calculating the difference between the total length of the channels in the molds of the first component and the third component and the difference between the total number of channels in the molds of the first component and the third component; The total number of channels is divided by the total number of channels of the third component to obtain the total number of channels difference ratio; the total length of channels is divided by the channel length of the third component to obtain the total length of channels difference ratio; The number of holes in the molds of the first component and the third component and the diameter and depth of each hole are obtained respectively, the diameter of each hole in the molds of the first component and the third component is multiplied by the depth respectively to obtain the complexity value of each hole, the sum of the complexity values ​​of all holes is calculated, and the aperture complexity values ​​of the first component and the third component are obtained respectively; the difference between the aperture complexity values ​​of the first component and the third component is calculated as the aperture difference value; The mold complexity coefficient of the first component is obtained by weighted summing the difference ratio of the total number of channels, the difference ratio of the total length of channels, and the difference in aperture; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness data.

6. The injection molding control method for automobile parts according to claim 5, characterized in that: The adjustment coefficients of the first component obtained based on the mold complexity coefficient and surface smoothness data include: Obtaining a preset surface smoothness value of the first component during injection molding, and obtaining a surface smoothness value of the third component during injection molding from the historical records, and calculating a difference between the preset surface smoothness value of the first component during injection molding and the surface smoothness value of the third component during injection molding as a surface smoothness difference coefficient of the first component; The adjustment coefficient of the first component is obtained according to the mold complexity coefficient and the surface smoothness difference coefficient. The calculation formula is: In the formula, is the adjustment coefficient, are the mold complexity coefficient and surface smoothness difference coefficient, They are The preset scaling factor of Both are greater than 0.

7. The injection molding control method for automobile parts according to claim 1, characterized in that: According to the adjustment coefficient, the first injection molding key parameter is adjusted to obtain the second injection molding key parameter including: In the formula, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the first injection key parameters. is the adjustment coefficient, They are respectively the injection pressure, injection time, holding time, holding pressure and cooling time in the second key injection molding parameters.

8. An injection molding control device for automobile parts, used to implement the injection molding control method for automobile parts according to any one of claims 1 to 7, characterized in that: The device comprises: Marking module: a new automobile part that has not been subjected to injection molding is recorded as the first part, and each automobile part that has been subjected to injection molding and for which the optimal injection molding key parameters have been found is recorded as the second part; Cluster analysis module: obtain the part volume, melt flow index and wall thickness of the first component and each second component, calculate the part volume difference, melt flow index difference and wall thickness difference of each second component and use them as data points of each second component to obtain a data set, cluster the data set to determine the injection molding condition similarity coefficient of each second component and the first component; Corresponding module: the second component with the largest similarity coefficient of injection molding conditions is used as the third component, and the optimal injection molding key parameters corresponding to the third component are used as the first injection molding key parameters of the first component; Control module: obtain mold complexity data and surface smoothness data of the first component and the third component, obtain the adjustment coefficient of the first component, adjust the first injection molding key parameter according to the adjustment coefficient, obtain the second injection molding key parameter, and use the second injection molding key parameter as the initial injection molding key parameter of the first component to control the injection molding of the first component; the injection molding key parameters include injection pressure, injection time, holding time, holding pressure and cooling time.

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