A manufacturing optimization method for rubber front armrests of children's strollers

Abstract

The present invention discloses a method for optimizing the manufacturing of a rubber front armrest for a stroller, which relates to the field of production and manufacturing technology. The method comprises the following steps: performing a confidence analysis on a front armrest injection mold library to determine a target mold for the front armrest injection; performing injection molding parameter mining to establish a first space for the front armrest injection molding; performing an application scenario demand analysis on the front armrest of the stroller to be produced to establish an evaluation expectation for the front armrest injection molding and a fitness function for the front armrest injection molding; performing an optimization analysis on the first space for the front armrest injection molding based on the evaluation expectation for the front armrest injection molding to establish a second space for the front armrest injection molding; performing optimization on the second space for the front armrest injection molding based on the fitness function for the front armrest injection molding to maximize the fitness of the front armrest injection molding, determining a front armrest injection molding optimization strategy, and executing the manufacturing of the front armrest of the stroller to be produced. The present invention solves the technical problem of unstable front armrest performance in the prior art during the manufacturing of the front armrest, thereby achieving the technical effect of improving the manufacturing quality of the front armrest.

Description

Technical Field

[0001] The present invention relates to the technical field of production and manufacturing, and in particular to a method for optimizing the manufacturing of a rubber front handrail for a children's stroller. Background Art

[0002] With the rapid growth of the children's products market, strollers, as essential daily necessities, have become widely used in homes and public spaces. The quality and safety of the front armrests have become a particular concern for consumers during the design and manufacturing process. As consumer expectations for product performance and quality continue to rise, traditional stroller front armrest manufacturing methods face increasing challenges. Existing production processes often suffer from unstable front armrest performance and imprecise manufacturing processes, making it difficult to meet modern consumer demands for high-quality, long-lasting, and safe strollers. Summary of the Invention

[0003] The present application provides a method for optimizing the manufacturing of a rubber front armrest for a stroller, which is used to solve the technical problem of unstable performance of the front armrest in the prior art during the manufacturing of the front armrest.

[0004] In view of the above problems, the present application provides a method for optimizing the manufacturing of a rubber front armrest for a children's stroller.

[0005] The present application provides a method for optimizing the manufacturing of a rubber front handrail for a stroller, the method comprising:

[0006] A confidence analysis is performed on the front armrest injection mold library according to the front armrest geometric design information and the front armrest injection molding raw material information corresponding to the front armrest of the cart to be produced, and a target mold for the front armrest injection molding is determined; injection molding parameters of the front armrest injection molding target mold are mined according to the front armrest geometric design information and the front armrest injection molding raw material information, and a first space for front armrest injection molding is established; application scenario requirements of the front armrest of the cart to be produced are analyzed according to the front armrest injection molding evaluation factor, and a front armrest injection molding evaluation expectation and a front armrest injection molding fitness function are established; optimization analysis is performed on the first front armrest injection molding space according to the front armrest injection molding evaluation expectation, and a second front armrest injection molding space is established; optimization is performed on the second front armrest injection molding space to maximize the front armrest injection molding fitness function according to the front armrest injection molding fitness function, and a front armrest injection molding optimization strategy is determined, and the manufacturing of the front armrest of the cart to be produced is executed according to the front armrest injection molding optimization strategy.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This application performs confidence analysis on the front armrest injection molding mold library based on the front armrest geometric design information and the front armrest injection molding raw material information corresponding to the front armrest of the handcart to be produced, and determines the front armrest injection molding target mold; performs injection molding parameter mining on the front armrest injection molding target mold based on the front armrest geometric design information and the front armrest injection molding raw material information, and establishes a first space for front armrest injection molding; performs application scenario demand analysis on the front armrest of the handcart to be produced based on the front armrest injection molding evaluation factor, and establishes a front armrest injection molding evaluation expectation and a front armrest injection molding fitness function; performs optimization analysis on the first front armrest injection molding space based on the front armrest injection molding evaluation expectation, and establishes a second front armrest injection molding space; performs optimization on the second front armrest injection molding space to maximize the front armrest injection molding fitness function based on the front armrest injection molding fitness function, determines a front armrest injection molding optimization strategy, and executes the manufacturing of the front armrest of the handcart to be produced based on the front armrest injection molding optimization strategy. The present invention solves the technical problem of unstable front armrest performance in the existing technology during front armrest manufacturing. By performing confidence analysis on the injection mold library, mining injection molding parameters, analyzing application scenario requirements and optimizing the fitness function, the technical effect of improving the manufacturing quality of the front armrest is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a process for optimizing the manufacturing of a rubber front armrest for a stroller provided in an embodiment of the present application;

[0011] Figure 2 This is a schematic diagram of the process of establishing the first space for injection molding of the front armrest in a method for optimizing the manufacturing of the rubber front armrest of a children's stroller provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] This application provides a method for optimizing the manufacturing of rubber front armrests for children's strollers, which is used to solve the technical problem of unstable front armrest performance in the existing technology during the manufacturing of front armrests. By performing confidence analysis on the injection mold library, mining injection molding parameters, analyzing application scenario requirements and optimizing the fitness function, the technical effect of improving the manufacturing quality of the front armrests is achieved.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the present application provides a method for optimizing the manufacturing of a rubber front handrail for a child stroller, the method comprising:

[0016] Step S100: performing a confidence analysis on a front armrest injection mold library based on the front armrest geometric design information and the front armrest injection molding material information corresponding to the front armrest of the trolley to be produced, and determining a target mold for the front armrest injection molding.

[0017] In this embodiment, the geometric design information and injection molding material information for the rubber front armrest of a stroller to be produced are first obtained from a preset database. Next, the injection molding history of the front armrest injection mold library is searched to obtain a set of injection molding history information for each mold. Then, based on the geometric design information and injection molding material information of the front armrest, the confidence level of each mold's historical information is calculated to obtain the confidence level of multiple molds.

[0018] Finally, the most suitable target mold is screened out according to the confidence levels of these molds, that is, the mold with the highest confidence level is selected as the target mold for the front armrest injection molding.

[0019] Furthermore, in the method provided in the embodiment of the application, a confidence analysis is performed on the front armrest injection mold library based on the front armrest geometric design information and the front armrest injection molding material information corresponding to the front armrest of the cart to be produced to determine the target mold for the front armrest injection molding, and the method further includes:

[0020] An injection molding history search is performed on the front armrest injection mold library to obtain an injection molding history information set of each mold; a confidence calculation is performed on the injection molding history information set of each mold based on the front armrest geometric design information and the front armrest injection molding raw material information to obtain multiple mold injection molding confidences; the front armrest injection mold library is screened according to the multiple mold injection molding confidences to determine the front armrest injection molding target mold corresponding to the maximum mold injection molding confidence.

[0021] In this embodiment of the present application, the injection molding history of the front armrest injection mold library is first searched to obtain the injection molding data of each mold in historical production, thereby forming an injection molding history information set for each mold. This information set includes the geometric design information and injection molding material information of the front armrests used in history. The geometric design information refers to the design parameters such as the shape and size of the front armrest, while the injection molding material information refers to the type of material used to produce the front armrest and its properties, such as the material's fluidity and hardness.

[0022] Next, a confidence calculation is performed on each mold's injection molding history information set based on the geometric design information and injection molding material information of the handrail to be produced. This calculation first traverses each mold's injection molding history information set and extracts historical data related to the handrail's geometric design and injection molding material information. Next, the frequency of occurrence of the handrail's geometric design and injection molding material information in each mold's historical data is counted to calculate the geometric support coefficient and injection molding material support coefficient, respectively. The injection molding confidence level for that mold is then calculated by taking the mean of these two support coefficients. This process is repeated to obtain injection molding confidence levels for multiple molds.

[0023] Finally, the front armrest injection mold library is screened based on multiple mold injection confidence scores, and the mold with the highest confidence score is selected as the target mold for the front armrest injection. This process is achieved through a sorting algorithm, which ranks the molds from high to low according to their confidence scores, and selects the mold with the highest ranking as the target mold for the front armrest injection.

[0024] Furthermore, in the method provided in the embodiment of the application, based on the front armrest geometric design information and the front armrest injection molding material information, the confidence calculation is performed on the injection molding history information set of each mold to obtain multiple mold injection molding confidences, and further includes:

[0025] Traverse the mold injection molding history information sets and extract the first mold injection molding history information set; count the frequency of the front armrest geometric design information appearing in the first mold injection molding history information set to obtain the first mold geometric support coefficient; count the frequency of the front armrest injection molding raw material information appearing in the first mold injection molding history information set to obtain the first mold injection molding raw material support coefficient; calculate the mean of the first mold geometric support coefficient and the first mold injection molding raw material support coefficient to obtain the first mold injection molding confidence, and add the first mold injection molding confidence to the multiple mold injection molding confidences.

[0026] In this embodiment of the present application, the injection molding history information sets of each mold are first traversed and the first mold injection molding history information set is randomly extracted. During this process, the injection molding history data of each mold is retrieved from the front armrest injection mold library through database query or data mining technology. The history records of each mold include geometric design information and injection molding material information. The geometric design information includes design parameters such as the size and shape of the front armrest, and the injection molding material information includes the type of material used.

[0027] Next, based on the first mold injection molding history information set, the geometric support coefficient is calculated. The geometric support coefficient is used to measure the degree of match between the geometric design of the current front armrest to be produced and the historical production data of the mold. Specifically, the geometric design information (such as size, shape, etc.) of the current front armrest is compared with the geometric data in the historical production record of the mold. The similarity between the current front armrest geometric design and the mold usage record in the historical data is calculated through a matching algorithm (such as Euclidean distance calculation). If the similarity falls within the preset range, it is considered that the current front armrest geometric design information matches the historical data. When calculating the geometric support coefficient, the number of times the current front armrest geometric design information matches the historical data is divided by the total number of historical data to obtain the geometric support coefficient.

[0028] Next, the injection molding raw material support coefficient is calculated. The injection molding raw material support coefficient measures the compatibility between the injection molding raw materials used in the current front armrest to be produced and the mold in the historical records. This process evaluates their similarity by comparing the injection molding raw material information of the current front armrest (such as material type, etc.) with the raw material usage of the mold in historical production, and determines whether the injection molding raw material information of the current front armrest matches the historical data. When judging whether it matches, it is determined whether the similarity calculation result meets the preset similarity threshold. If it does, the data is considered to match. Using the similarity calculation method, the similarity between the raw materials and the historical data is calculated based on their characteristics, and the injection molding raw material support coefficient is obtained by counting the matching frequency of the information. The injection molding raw material support coefficient is obtained by dividing the number of times the injection molding raw material information of the current front armrest matches the historical data by the total number of times in the historical data.

[0029] After calculating the geometric support factor and the injection material support factor, calculate the injection confidence of the first mold. This calculation is performed by adding the geometric support factor to the injection material support factor and dividing the result by 2. Finally, add the injection confidence of the first mold to the confidence list of all molds.

[0030] Step S200: performing injection molding parameter mining on the front armrest injection molding target mold according to the front armrest geometric design information and the front armrest injection molding raw material information, and establishing a first space for front armrest injection molding.

[0031] In an embodiment of the present application, when mining the injection molding parameters of the target mold for the front armrest injection molding based on the geometric design information and injection molding raw material information of the front armrest, the key injection molding parameter factors are first determined, such as melt temperature, injection pressure, injection speed, holding time, mold temperature and cooling time, etc. The geometric design information and injection molding raw material information of the front armrest are used as constraints to perform injection molding parameter retrieval and determine the injection molding target parameters that need to be optimized. Then, based on these retrieval conditions, injection molding feature mining is performed on the target mold to extract the relationship between each injection molding parameter and the production effect of the front armrest, forming a multivariate registration domain for the injection molding of the front armrest. Finally, the first space for the injection molding of the front armrest is generated by combining the injection molding parameters in the multivariate registration domain.

[0032] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, the injection molding parameters of the front armrest injection molding target mold are mined according to the front armrest geometric design information and the front armrest injection molding raw material information to establish the first space for front armrest injection molding, and the method further includes:

[0033] Injection molding parameter factors are obtained, wherein the injection molding parameter factors include melt temperature, injection pressure, injection speed, holding time, mold temperature and cooling time; the front armrest geometric design information and the front armrest injection molding raw material information are used as injection molding retrieval constraints, and the injection molding parameter factors are used as injection molding retrieval targets; injection molding feature mining is performed on the front armrest injection molding target mold according to the injection molding retrieval constraints and the injection molding retrieval targets, and a front armrest injection molding multivariate registration domain is established; injection molding parameters are combined according to the front armrest injection molding multivariate registration domain to generate the front armrest injection molding first space.

[0034] In this embodiment, injection molding parameter factors, predefined by technical experts, are first retrieved from a preset database. These factors include melt temperature, injection pressure, injection speed, dwell time, mold temperature, and cooling time. Subsequently, the front armrest geometry and injection molding material information are used as injection molding search constraints, and the injection molding parameter factors are used as injection molding search targets.

[0035] Then, injection molding feature mining is performed on the target mold for the front armrest injection molding based on the injection molding retrieval constraints and injection molding retrieval targets. Specifically, the target mold for the front armrest injection molding is first globally interconnected with molds with the same characteristics to obtain a global injection mold. Here, molds with the same characteristics refer to injection molds with the same material structure. Next, by performing injection molding record retrieval on the global injection mold, a global library for front armrest injection molding is obtained, which contains historical records of different injection molding processes and mold configurations. Subsequently, this global library is classified according to injection molding parameter factors to form multiple front armrest injection molding parameter clusters, each of which represents a group of mold configurations with similar injection molding characteristics. Finally, centralized interval recognition is performed based on these parameter clusters to generate a multivariate registration domain for the front armrest injection molding.

[0036] Finally, based on the multivariate registration domain for the front armrest injection molding process, injection molding parameter combinations are performed. This involves traversing and combining all possible injection molding process parameters. This process randomly combines all injection molding parameter factors within their permitted ranges to generate multiple injection molding process configurations. These random combinations represent a wide range of possible injection molding process settings, encompassing all parameter variations. Ultimately, the first injection molding space for the front armrest is generated.

[0037] Furthermore, in the method provided in the embodiment of the application, the injection molding feature mining of the front armrest injection molding target mold is performed according to the injection molding retrieval constraint and the injection molding retrieval target, and a front armrest injection molding multi-element registration domain is established, which also includes:

[0038] The front armrest injection molding target mold is globally interconnected with the feature mold to obtain a global injection mold; injection molding records of the global injection mold are retrieved according to the injection molding retrieval constraints and the injection molding retrieval target to obtain a front armrest injection molding global library; the front armrest injection molding global library is classified according to the injection molding parameter factors to obtain a multivariate front armrest injection molding parameter cluster; centralized interval identification is performed based on the multivariate front armrest injection molding parameter cluster to generate the front armrest injection molding multivariate registration domain.

[0039] In an embodiment of the present application, the target mold for the front armrest injection molding is first globally interconnected with the same-feature molds, that is, multiple molds with similar material structures and injection molding characteristics are associated with the target mold to form a global injection mold set. Molds with the same characteristics refer to molds that are similar in physical properties such as material type, fluidity, and melting temperature. These molds usually exhibit similar injection molding characteristics. By using a mold management system or database query technology, these molds are uniformly managed and linked to ensure that relevant information can be extracted from multiple molds, and the target mold is associated with these molds with the same characteristics.

[0040] Next, based on the injection molding retrieval constraints and injection molding retrieval targets, the global injection mold is retrieved for injection molding records. This step uses the geometric design information and injection molding raw material information of the front armrest as retrieval constraints, combined with the injection molding process parameters (such as melt temperature, injection pressure, etc.) as retrieval targets, to screen out qualified mold data. Specifically, the geometric design information and raw material characteristics will be used as parameter range constraints, and historical injection molding records will be extracted from the global injection mold through data query and retrieval technology. These historical data contain the process parameter settings and performance of the mold in past production, thereby constructing a global library for front armrest injection molding, that is, a database containing detailed historical injection molding records of all molds that meet the retrieval criteria.

[0041] Next, the global library of front armrest injection molding is classified based on the injection molding parameter factors. This step divides molds with similar injection molding parameters into different groups through cluster analysis or classification algorithms (such as K-means clustering). Injection molding parameter factors include melt temperature, injection pressure, injection speed, etc., which determine the fluidity and filling effect of the injection molding process. By classifying these factors, molds with similar injection molding parameters are grouped into a cluster to form multiple multivariate front armrest injection molding parameter clusters. Each cluster represents a group of molds with similar injection molding process configurations, which helps to further narrow the range of mold selection and obtain a multivariate front armrest injection molding parameter cluster, that is, a collection of molds with similar injection molding parameter settings.

[0042] Next, we identify concentrated intervals based on the multivariate front armrest injection molding parameter clusters. This process uses statistical analysis methods (such as calculating mean, standard deviation, maximum, and minimum values) to identify the concentrated intervals of injection molding parameters within each cluster. By analyzing the injection molding parameters within each cluster, we identify the most representative parameter intervals and determine which injection molding parameter ranges have the greatest impact on molding quality. Identifying concentrated intervals helps determine which parameter combinations are most beneficial for production results.

[0043] Finally, based on the identification results of the concentrated intervals, a multivariate registration domain for the front armrest injection molding process is generated. The multivariate registration domain is a multidimensional parameter space, where each dimension represents an injection molding parameter factor (such as melt temperature and injection pressure). The multivariate registration domain for the front armrest injection molding process is generated by mapping the identified concentrated intervals into this multidimensional space.

[0044] Step S300: analyzing the application scenario requirements of the front armrest of the cart to be produced according to the front armrest injection molding evaluation factor, and establishing the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function.

[0045] In an embodiment of the present application, when analyzing the application scenario requirements of the front armrest of the cart to be produced based on the front armrest injection molding evaluation factor, the application scenario information of the front armrest is first obtained, which includes the functional requirements and environmental conditions of the front armrest in actual use. Then, based on the front armrest application scenario information and the front armrest injection molding evaluation factor (i.e., the surface quality, mechanical strength and environmental tolerance of the front armrest), the evaluation records of qualified front armrests are retrieved to obtain the corresponding evaluation set, including qualified evaluation data of surface quality, mechanical strength and environmental tolerance. Then, the centralized value calculation is performed on these evaluation sets respectively to obtain the expected surface quality evaluation coefficient, mechanical strength evaluation coefficient and environmental tolerance evaluation coefficient, thereby generating the front armrest injection molding evaluation expectation.

[0046] Finally, the importance of the front armrest injection molding evaluation factors was evaluated according to the front armrest application scenario information, and the weight of each evaluation factor was obtained. The weight was applied to the construction of the injection molding fitness function to generate the front armrest injection molding fitness function.

[0047] Furthermore, the method provided in the application embodiment also includes:

[0048] The front armrest injection molding evaluation factors include the front armrest surface quality, the front armrest mechanical strength and the front armrest environmental tolerance.

[0049] In an embodiment of the present application, the injection molding evaluation factors of the front armrest include the surface quality of the front armrest, the mechanical strength of the front armrest, and the environmental tolerance of the front armrest. The surface quality of the front armrest mainly evaluates the appearance characteristics of the front armrest, such as surface smoothness, whether there are bubbles, scratches or other surface defects, which will affect the visual effect and user experience of the front armrest. The mechanical strength of the front armrest focuses on the ability of the front armrest to withstand external loads during actual use, including compressive strength, bending strength, etc., which directly affects the safety and durability of the front armrest. The environmental tolerance of the front armrest evaluates the stability and durability of the material of the front armrest under different environmental conditions (such as temperature changes, humidity, ultraviolet exposure, etc.), ensuring that the front armrest can adapt to various external environments for a long time and prevent aging, corrosion and other problems. These three evaluation factors jointly determine the performance and long-term reliability of the front armrest during use.

[0050] Furthermore, in the method provided in the embodiment of the application, the application scenario requirements of the front armrest of the cart to be produced are analyzed according to the front armrest injection molding evaluation factor, and the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function are established, which also includes:

[0051] Obtain the front armrest application scenario information of the front armrest of the handcart to be produced; based on the front armrest application scenario information, retrieve qualified front armrest evaluation records according to the front armrest injection molding evaluation factor to obtain a qualified front armrest surface quality evaluation set, a qualified front armrest mechanical strength evaluation set and a qualified front armrest environmental tolerance evaluation set; perform centralized value calculation on the qualified front armrest surface quality evaluation set, the qualified front armrest mechanical strength evaluation set and the qualified front armrest environmental tolerance evaluation set respectively to obtain an expected surface quality evaluation coefficient, an expected mechanical strength evaluation coefficient and an expected environmental tolerance evaluation coefficient; output the expected surface quality evaluation coefficient, the expected mechanical strength evaluation coefficient and the expected environmental tolerance evaluation coefficient as the front armrest injection molding evaluation expectation; perform importance evaluation on the front armrest injection molding evaluation factor according to the front armrest application scenario information to obtain an injection molding evaluation factor importance evaluation result, and perform weight distribution on the front armrest injection molding evaluation factor according to the injection molding evaluation factor importance evaluation result to obtain the front armrest injection molding fitness function.

[0052] In an embodiment of the present application, the front armrest application scenario information of the front armrest of the cart to be produced is first obtained from a preset database. The front armrest application scenario information includes environmental factors and user needs. Environmental factors include the physical environment that the front armrest may face during actual use, such as temperature, humidity, ultraviolet exposure and other conditions. For example, the front armrest may be used in extreme temperatures, such as in the range of -10°C to 50°C, or in an environment with high humidity. User needs involve the mechanical loads that the front armrest must withstand during use, appearance requirements, and durability requirements.

[0053] Next, based on the front handrail application scenario information, qualified front handrail evaluation records are retrieved. This stage uses database queries or data mining methods to screen historical data that meets the application scenario requirements, including a qualified front handrail surface quality evaluation set, a qualified front handrail mechanical strength evaluation set, and a qualified front handrail environmental tolerance evaluation set. Each historical data set that meets the application scenario requirements includes a corresponding qualified front handrail surface quality evaluation, a qualified front handrail mechanical strength evaluation, and a qualified front handrail environmental tolerance evaluation, each of which is pre-annotated by technical experts.

[0054] Next, the centralized values ​​of the qualified front armrest surface quality evaluation set, the qualified front armrest mechanical strength evaluation set and the qualified front armrest environmental tolerance evaluation set are calculated respectively. Specifically, this step calculates the average evaluation values ​​of surface quality, mechanical strength and environmental tolerance through statistical methods, such as mean. When calculating the expected surface quality evaluation coefficient, the expected surface quality evaluation coefficient is obtained by calculating the mean of all samples of the qualified front armrest surface quality evaluation set. Similarly, the expected mechanical strength evaluation coefficient and the expected environmental tolerance evaluation coefficient are obtained by performing mean calculation on the qualified front armrest mechanical strength evaluation set and the qualified front armrest environmental tolerance evaluation set. The expected surface quality evaluation coefficient, the expected mechanical strength evaluation coefficient and the expected environmental tolerance evaluation coefficient are then output as the front armrest injection molding evaluation expectation.

[0055] Finally, the importance of the front armrest injection molding evaluation factors was assessed based on the front armrest application scenario information. Specifically, the importance of the injection molding evaluation factors was first assessed, taking into account the varying requirements for surface quality, mechanical strength, and environmental resistance in different application scenarios. During this process, the specific requirements of the application scenario, such as temperature, humidity, and UV resistance, influence the relative importance of each evaluation factor. For example, in some application scenarios, such as outdoor use, environmental resistance may be more important than surface quality and mechanical strength, while in other scenarios, such as infant gripping, surface quality is given a higher weight. By analyzing this application scenario information, technical experts selected weights for each evaluation factor based on the application scenario. The weights for each evaluation factor vary in different environments, and these weights are pre-defined. Finally, these weights were applied to the front armrest injection molding evaluation factors to generate the front armrest injection molding fitness function. For example, in an outdoor use scenario, the desired weights for the surface quality evaluation factor and the desired mechanical strength evaluation factor are 0.3, and the desired environmental resistance evaluation factor is 0.4.

[0056] Through the above importance evaluation, the fitness function of the front armrest injection molding is obtained. For example, for the front armrest required for outdoor use, the fitness function of the front armrest injection molding can be F=0.3Q S +0.3Q M +0.4Q E , where Q S is the expected surface quality evaluation coefficient, Q M is the expected mechanical strength evaluation coefficient, Q E is the expected environmental tolerance evaluation coefficient, and F is the injection molding adaptability of the front armrest.

[0057] Step S400: performing optimization analysis on the first injection molding space of the front armrest according to the front armrest injection molding evaluation expectation, and establishing a second injection molding space of the front armrest.

[0058] In an embodiment of the present application, when performing an optimization analysis on the first space of the front armrest injection molding according to the front armrest injection molding evaluation expectations, a preliminary injection molding strategy is first extracted from the first space of the front armrest injection molding. Then, based on the front armrest injection molding raw material information and the target mold, these preliminary strategies are simulated and evaluated for injection molding to obtain the first front armrest injection molding evaluation result. It is then determined whether the evaluation result meets the predetermined front armrest injection molding evaluation expectations. If the expectations are met, the current injection molding strategy is added to the second space of the front armrest injection molding, indicating that this is an optimized process that meets the quality standards; if the expectations are not met, the strategy will be eliminated, and the process configuration that does not meet the requirements will be eliminated. Through this process, the second space of the front armrest injection molding is finally established.

[0059] Furthermore, in the method provided in the embodiment of the application, the optimization analysis of the first space for injection molding of the front armrest is performed according to the evaluation expectation of the injection molding of the front armrest to establish the second space for injection molding of the front armrest, and the method further includes:

[0060] According to the first front armrest injection molding space, a first front armrest injection molding strategy is extracted; according to the front armrest injection molding raw material information and the front armrest injection molding target mold, a simulated injection molding evaluation is performed on the first front armrest injection molding strategy to obtain a first front armrest injection molding evaluation result; it is determined whether the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation; if the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation, the first front armrest injection molding strategy is added to the front armrest injection molding second space; if the first front armrest injection molding evaluation result does not meet the front armrest injection molding evaluation expectation, the first front armrest injection molding strategy is eliminated.

[0061] In this embodiment, a first strategy for front armrest injection molding is first extracted based on the first injection molding space of the front armrest. The first injection molding space of the front armrest represents all possible injection molding process configurations, where each configuration includes injection molding parameters such as melt temperature, injection pressure, and injection speed. The first strategy for front armrest injection molding is obtained by randomly extracting the first injection molding space of the front armrest.

[0062] Next, a simulated injection molding evaluation is performed on the first front armrest injection molding strategy based on the front armrest injection molding raw material information and the front armrest injection molding target mold. Specifically, a simulated injection molding is performed according to the first front armrest injection molding strategy based on the front armrest injection molding raw material information and the front armrest injection molding target mold, resulting in a first simulated injection-molded front armrest. The first simulated injection-molded front armrest is then evaluated for surface quality, mechanical strength, and environmental tolerance, resulting in a first front armrest surface quality coefficient, a first front armrest mechanical strength coefficient, and a first front armrest environmental tolerance coefficient. These values ​​are then output as the first front armrest injection molding evaluation results.

[0063] Then, a determination is made as to whether the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectations. When making this determination, the first front armrest surface quality coefficient, the first front armrest mechanical strength coefficient, and the first front armrest environmental tolerance coefficient in the first front armrest injection molding evaluation result are compared with the expected surface quality evaluation coefficient, expected mechanical strength evaluation coefficient, and expected environmental tolerance evaluation coefficient in the front armrest injection molding evaluation expectations. The first front armrest injection molding evaluation result is considered to meet the front armrest injection molding evaluation expectations only if all coefficients in the first front armrest injection molding evaluation result are greater than all coefficients in the front armrest injection molding evaluation expectations.

[0064] When the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation, the front armrest injection molding first strategy is added to the front armrest injection molding second space; when the first front armrest injection molding evaluation result does not meet the front armrest injection molding evaluation expectation, the front armrest injection molding first strategy is eliminated.

[0065] By traversing and extracting the first space for the front armrest injection molding, repeating the aforementioned evaluation process and judgment process, the second space for the front armrest injection molding is finally established.

[0066] Furthermore, in the method provided in the embodiment of the application, a simulation injection molding evaluation is performed on the first front armrest injection molding strategy based on the front armrest injection molding raw material information and the front armrest injection molding target mold to obtain a first front armrest injection molding evaluation result, further comprising:

[0067] Based on the front armrest injection molding raw material information and the front armrest injection molding target mold, simulated injection molding is performed according to the first front armrest injection molding strategy to obtain a first simulated injection molded front armrest; a surface quality evaluation is performed on the first simulated injection molded front armrest to obtain a first front armrest surface quality coefficient; a mechanical strength evaluation is performed on the first simulated injection molded front armrest to obtain a first front armrest mechanical strength coefficient; an environmental tolerance evaluation is performed on the first simulated injection molded front armrest to obtain a first front armrest environmental tolerance coefficient; the first front armrest surface quality coefficient, the first front armrest mechanical strength coefficient and the first front armrest environmental tolerance coefficient are output as the first front armrest injection molding evaluation result.

[0068] In an embodiment of the present application, first, based on the front armrest injection molding raw material information and the front armrest injection molding target mold, simulated injection molding is performed according to the first front armrest injection molding strategy, and the first simulated injection-molded front armrest is obtained. This process is completed by computer-aided engineering simulation (CAE) tools, and injection molding simulation software (such as Moldflow, etc.) is used to simulate the injection molding process. The front armrest injection molding raw material information includes the material's fluidity, melting temperature, viscosity, etc. These parameters will affect the pressure, temperature and flow characteristics during the injection molding process; and the front armrest injection molding target mold refers to the mold that best matches the front armrest geometric design and the selected material. By inputting these raw material information and the target mold into the injection molding simulation software, a simulated injection molding result is obtained, namely the first simulated injection-molded front armrest.

[0069] Then the surface quality of the first simulated injection-molded front armrest is evaluated to obtain the surface quality coefficient of the first front armrest. In this process, the simulation results are evaluated using a regression analysis model. The input data are the surface characteristics of the first simulated injection-molded front armrest, such as surface temperature, fluidity, and stress distribution. The regression model is trained with historical data, which includes a large amount of simulated data on the surface quality of the front armrest that has been calibrated. These training data include surface smoothness, defect types (such as bubbles, cold joints), and corresponding evaluation criteria (surface quality coefficients between 0 and 1) under different injection molding processes. The output data is the surface quality coefficient of the first front armrest, a value between 0 and 1, which represents the surface quality of the front armrest, 0 means that the surface has obvious defects, and 1 means that the surface is smooth and has no defects.

[0070] The mechanical strength of the first simulated injection-molded front armrest is then evaluated to obtain the mechanical strength coefficient of the first front armrest. This process is evaluated using a support vector machine (SVM) regression model, with the input data including stress, strain, and load data from the simulation results. The SVM regression model is trained based on known historical mechanical strength test data. The training data includes the mechanical strength test results of the front armrest under different process parameters, such as compressive strength and flexural strength. This training data is then learned through the SVM model to establish the relationship between mechanical strength and injection molding process parameters. The output data is the mechanical strength coefficient of the first front armrest.

[0071] Subsequently, the environmental tolerance evaluation of the first simulated injection-molded front armrest is performed. Specifically, the geometric parameters of the first simulated injection-molded front armrest are first collected to obtain the geometric data of the first injection-molded front armrest. Next, the geometric information of the front armrest under multiple test environments (such as high temperature, humidity, ultraviolet rays, etc.) is collected to obtain the geometric data of the front armrest in multiple environments. Then, based on the geometric data of the first injection-molded front armrest, a loss analysis is performed on the geometric data of the front armrest in multiple environments to evaluate the tolerance of the front armrest in various environments and obtain the loss information of the front armrest in multiple environments. Finally, based on these loss information, the environmental tolerance evaluation of the first simulated injection-molded front armrest is performed to obtain the environmental tolerance coefficient of the first front armrest.

[0072] Finally, the surface quality coefficient of the first front armrest, the mechanical strength coefficient of the first front armrest, and the environmental tolerance coefficient of the first front armrest are integrated and output as the injection molding evaluation result of the first front armrest.

[0073] Furthermore, in the method provided in the embodiment of the application, the environmental tolerance evaluation of the first simulated injection-molded front armrest is performed to obtain the environmental tolerance coefficient of the first front armrest, and the method further includes:

[0074] Collect the geometric parameters of the first simulated injection-molded front armrest in real time to obtain the geometric data of the first injection-molded front armrest; collect the geometric information of the first simulated injection-molded front armrest in multiple test environments to obtain the geometric data of multiple environmental front armrests; perform loss analysis on the geometric data of the multiple environmental front armrests based on the first injection-molded front armrest geometric data to obtain the loss information of multiple environmental front armrests; perform environmental tolerance evaluation on the first simulated injection-molded front armrest based on the loss information of the multiple environmental front armrests to obtain the environmental tolerance coefficient of the first front armrest.

[0075] In an embodiment of the present application, the geometric parameters of the first simulated injection-molded front armrest are first collected in real time through computer-aided design (CAD) software to obtain the geometric data of the first injection-molded front armrest, including the geometric information of the front armrest, such as size parameters.

[0076] Next, geometric data for the first simulated injection-molded armrest was collected under multiple test environments. During this process, simulation software was used to virtually test the armrest under various environmental conditions, simulating its performance in a variety of test environments, including low and high temperatures, extreme humidity, UV exposure, and immersion in sweat and saliva. The simulation software recalculated the armrest's geometry based on the material's physical properties and environmental influences, such as temperature and humidity fluctuations and UV radiation, generating geometric data for multiple environments under these varying conditions.

[0077] Subsequently, a loss analysis is performed on the geometric data of the front armrests in multiple environments based on the geometric data of the front armrests before the first injection molding. At this stage, the goal of the loss analysis is to calculate the dimensional change, that is, the dimensional loss. The geometric data of the front armrest before the first injection molding (that is, the size and shape at the time of initial injection molding) are compared with the geometric data of the front armrests in multiple environments (that is, the size and shape changes of the front armrests under different environmental conditions). Use a geometric comparison method (such as the least squares method or the bounding box method) to measure the changes in the size of the front armrest under each environmental condition. Specifically, the simulation software calculates the changes in the length, width, thickness, etc. of the front armrest under different environmental conditions to obtain the loss information of the front armrests in multiple environments.

[0078] Finally, based on the armrest loss information from multiple environments, the environmental tolerance of the armrest is evaluated. During this process, the length change, width change, and thickness change from the armrest loss information from multiple environments are summed and averaged to obtain the average length change, average width change, and average thickness change. The calculated average length change, average width change, and average thickness change are then multiplied by preset weights to obtain the mean loss. Based on this mean loss, the environmental tolerance coefficient is calculated. To determine that smaller dimensional loss indicates a higher environmental adaptability coefficient, the mean loss is reciprocally normalized. The first armrest environmental tolerance coefficient is obtained by dividing 1 by 1 plus the sum of the mean loss.

[0079] Step S500: maximizing the fitness of the front armrest injection molding of the second space of the front armrest injection molding is optimized according to the front armrest injection molding fitness function, determining the front armrest injection molding optimization strategy, and executing the manufacturing of the front armrest of the trolley to be produced according to the front armrest injection molding optimization strategy.

[0080] In the embodiment of the present application, the armrest injection molding fitness of the second space is optimized based on the armrest injection molding fitness function. Specifically, the armrest surface quality coefficient, armrest mechanical strength coefficient, and armrest environmental tolerance coefficient corresponding to each strategy in the second space are substituted into the armrest injection molding fitness function to calculate the armrest injection molding fitness corresponding to each strategy. The strategy with the highest armrest injection molding fitness is then selected as the optimization strategy for the armrest injection molding.

[0081] Finally, the manufacturing optimization of the front armrest of the trolley to be produced is carried out according to the front armrest injection molding optimization strategy.

[0082] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0083] This application performs confidence analysis on the front armrest injection molding mold library based on the front armrest geometric design information and the front armrest injection molding raw material information corresponding to the front armrest of the handcart to be produced, and determines the front armrest injection molding target mold; performs injection molding parameter mining on the front armrest injection molding target mold based on the front armrest geometric design information and the front armrest injection molding raw material information, and establishes a first space for front armrest injection molding; performs application scenario demand analysis on the front armrest of the handcart to be produced based on the front armrest injection molding evaluation factor, and establishes a front armrest injection molding evaluation expectation and a front armrest injection molding fitness function; performs optimization analysis on the first front armrest injection molding space based on the front armrest injection molding evaluation expectation, and establishes a second front armrest injection molding space; performs optimization on the second front armrest injection molding space to maximize the front armrest injection molding fitness function based on the front armrest injection molding fitness function, determines a front armrest injection molding optimization strategy, and executes the manufacturing of the front armrest of the handcart to be produced based on the front armrest injection molding optimization strategy. The present invention solves the technical problem of unstable front armrest performance in the existing technology during front armrest manufacturing. By performing confidence analysis on the injection mold library, mining injection molding parameters, analyzing application scenario requirements and optimizing the fitness function, the technical effect of improving the manufacturing quality of the front armrest is achieved.

[0084] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0086] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for optimizing the manufacturing of a rubber front armrest for a stroller, characterized in that: The method comprises: Perform confidence analysis on the front armrest injection mold library based on the front armrest geometric design information and the front armrest injection material information corresponding to the cart front armrest to be produced, and determine the target mold for the front armrest injection molding; Performing injection molding parameter mining on the front armrest injection molding target mold according to the front armrest geometric design information and the front armrest injection molding raw material information to establish a first space for front armrest injection molding; Analyze the application scenario requirements of the front armrest of the trolley to be produced according to the front armrest injection molding evaluation factor, and establish the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function; Performing optimization analysis on the first injection molding space of the front armrest according to the evaluation expectation of the injection molding of the front armrest, and establishing the second injection molding space of the front armrest; maximizing the fitness of the front armrest injection molding of the second space according to the front armrest injection molding fitness function, determining a front armrest injection molding optimization strategy, and executing the manufacturing of the front armrest of the cart to be produced according to the front armrest injection molding optimization strategy; The step of performing an optimization analysis on the first front armrest injection molding space according to the front armrest injection molding evaluation expectation to establish a second front armrest injection molding space includes: Extracting a first strategy for front armrest injection molding according to the first front armrest injection molding space; Performing a simulated injection molding evaluation on the first front armrest injection molding strategy according to the front armrest injection molding raw material information and the front armrest injection molding target mold to obtain a first front armrest injection molding evaluation result; Determining whether the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation; If the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation, adding the first front armrest injection molding strategy to the second front armrest injection molding space; If the first front armrest injection molding evaluation result does not meet the front armrest injection molding evaluation expectation, eliminating the first front armrest injection molding strategy; The method of performing confidence analysis on the front armrest injection mold library based on the front armrest geometric design information and the front armrest injection molding material information corresponding to the front armrest of the cart to be produced to determine the front armrest injection molding target mold includes: Performing an injection history search on the front armrest injection mold library to obtain an injection history information set of each mold; Based on the front armrest geometric design information and the front armrest injection molding material information, performing confidence calculation on each mold injection molding history information set to obtain multiple mold injection molding confidence levels; Screening the front armrest injection mold library according to the multiple mold injection confidences, and determining the front armrest injection target mold corresponding to the maximum mold injection confidence; The confidence calculation is performed on each mold injection history information set based on the front armrest geometric design information and the front armrest injection molding material information to obtain multiple mold injection molding confidences, including: Traversing the injection molding history information sets of each mold, and extracting the first mold injection molding history information set; Counting the frequency of occurrence of the front armrest geometric design information in the first mold injection molding history information set to obtain a first mold geometric support coefficient; Counting the frequency of occurrence of the front armrest injection molding material information in the first mold injection molding history information set to obtain the first mold injection molding material support coefficient; An average of the first mold geometry support coefficient and the first mold injection material support coefficient is calculated to obtain a first mold injection confidence, and the first mold injection confidence is added to the plurality of mold injection confidences.

2. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 1, wherein: Performing injection molding parameter mining on the front armrest injection molding target mold according to the front armrest geometric design information and the front armrest injection molding raw material information to establish a first space for front armrest injection molding, including: Obtaining injection molding parameter factors, wherein the injection molding parameter factors include melt temperature, injection pressure, injection speed, holding time, mold temperature, and cooling time; The front armrest geometric design information and the front armrest injection molding material information are used as injection molding retrieval constraints, and the injection molding parameter factor is used as an injection molding retrieval target; Performing injection molding feature mining on the front armrest injection molding target mold according to the injection molding retrieval constraints and the injection molding retrieval target, and establishing a front armrest injection molding multivariate registration domain; Injection molding parameters are combined according to the multi-element registration domain of the front armrest injection molding to generate the first space of the front armrest injection molding.

3. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 2, wherein: Performing injection molding feature mining on the front armrest injection molding target mold according to the injection molding retrieval constraints and the injection molding retrieval target, and establishing a front armrest injection molding multivariate registration domain, including: Globally interconnecting the target mold for injection molding of the front armrest with the same characteristic mold to obtain a global injection mold; Performing an injection molding record search on the global injection mold according to the injection molding search constraint and the injection molding search target to obtain a front armrest injection molding global library; Classifying the front armrest injection molding global library according to the injection molding parameter factors to obtain a multivariate front armrest injection molding parameter cluster; Centralized interval identification is performed based on the multivariate front armrest injection molding parameter cluster to generate the multivariate registration domain for the front armrest injection molding.

4. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 1, wherein: According to the front armrest injection molding evaluation factor, the application scenario requirements of the front armrest of the cart to be produced are analyzed, and the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function are established, including: Obtaining application scenario information of the front armrest of the cart to be produced; Based on the front armrest application scenario information, performing a search for qualified front armrest evaluation records according to the front armrest injection molding evaluation factors to obtain a qualified front armrest surface quality evaluation set, a qualified front armrest mechanical strength evaluation set, and a qualified front armrest environmental tolerance evaluation set; performing centralized value calculations on the qualified front handrail surface quality evaluation set, the qualified front handrail mechanical strength evaluation set, and the qualified front handrail environmental tolerance evaluation set, respectively, to obtain an expected surface quality evaluation coefficient, an expected mechanical strength evaluation coefficient, and an expected environmental tolerance evaluation coefficient; Outputting the expected surface quality evaluation coefficient, the expected mechanical strength evaluation coefficient, and the expected environmental tolerance evaluation coefficient as the front armrest injection molding evaluation expectation; An importance evaluation is performed on the front armrest injection molding evaluation factor according to the front armrest application scenario information to obtain an injection molding evaluation factor importance evaluation result, and a weight distribution is performed on the front armrest injection molding evaluation factor according to the injection molding evaluation factor importance evaluation result to obtain the front armrest injection molding fitness function.

5. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 4, wherein: Performing a simulated injection molding evaluation on the first front armrest injection molding strategy according to the front armrest injection molding raw material information and the front armrest injection molding target mold to obtain a first front armrest injection molding evaluation result, including: Based on the front armrest injection molding raw material information and the front armrest injection molding target mold, simulated injection molding is performed according to the first front armrest injection molding strategy to obtain a first simulated injection-molded front armrest; Performing a surface quality evaluation on the first simulated injection-molded front armrest to obtain a surface quality coefficient of the first front armrest; Performing a mechanical strength evaluation on the first simulated injection-molded front armrest to obtain a mechanical strength coefficient of the first front armrest; Performing an environmental tolerance evaluation on the first simulated injection-molded front armrest to obtain an environmental tolerance coefficient of the first front armrest; The surface quality coefficient of the first front armrest, the mechanical strength coefficient of the first front armrest, and the environmental tolerance coefficient of the first front armrest are output as an injection molding evaluation result of the first front armrest.

6. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 5, characterized in that: The environmental tolerance evaluation of the first simulated injection-molded front armrest is performed to obtain an environmental tolerance coefficient of the first front armrest, including: Collecting geometric parameters of the first simulated injection-molded handrail in real time to obtain geometric data of the first injection-molded handrail; Collecting geometric information of the first simulated injection-molded front armrest under multiple test environments to obtain geometric data of the front armrest in multiple environments; Performing loss analysis on the plurality of environmental front armrest geometric data according to the first injection molding front armrest geometric data to obtain a plurality of environmental front armrest loss information; An environmental tolerance evaluation is performed on the first simulated injection-molded front armrest according to the plurality of environmental front armrest loss information to obtain an environmental tolerance coefficient of the first front armrest.

7. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 1, wherein: The front armrest injection molding evaluation factors include the front armrest surface quality, the front armrest mechanical strength and the front armrest environmental tolerance.

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