Manufacturing optimization method for rubber front handrail of baby carriage

Abstract

The invention discloses a baby carriage rubber front handrail manufacturing optimization method, and relates to the technical field of production and manufacturing, and the method comprises the following steps: carrying out confidence coefficient analysis on a front handrail injection mold library, and determining a front handrail injection target mold; injection molding parameters are excavated, and a first front handrail injection molding space is established; carrying out application scene demand analysis on the to-be-produced trolley front handrail, and establishing a front handrail injection molding evaluation expectation and a front handrail injection molding fitness function; performing optimization analysis on the front armrest injection molding first space according to the front armrest injection molding evaluation expectation, and establishing a front armrest injection molding second space; and according to the front handrail injection molding fitness function, front handrail injection molding fitness maximization optimization is conducted on the second front handrail injection molding space, a front handrail injection molding optimization strategy is determined, and manufacturing of the to-be-produced trolley front handrail is executed. The technical problem that in the prior art, when the front handrail is manufactured, the performance of the front handrail is not stable is solved, and the technical effect of improving the manufacturing quality of the front handrail is achieved.

Description

Technical Field

[0001] The 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 child stroller. Background Art

[0002] With the rapid growth of the children's products market, strollers, as important daily necessities, have been widely used in homes and public places. In particular, in the design and manufacturing process of strollers, the quality and safety of the front armrests have become the focus of consumers. As consumers' requirements for product performance and quality continue to increase, traditional methods of manufacturing front armrests for strollers are facing increasing challenges. Existing production processes often have problems such as unstable front armrest performance and inaccurate manufacturing processes, which makes it difficult to meet the modern consumer's demand for high quality, long life and safety of strollers. Summary of the invention

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

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

[0006] 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, a confidence analysis is performed on the front armrest injection molding mold library to determine the front armrest injection molding target mold; injection molding parameters are mined for 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 a first space for front armrest injection molding is established; according to the front armrest injection molding evaluation factor, the application scenario demand analysis is performed on the front armrest of the cart to be produced, and the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function are established; according to the front armrest injection molding evaluation expectation, an optimization analysis is performed on the first front armrest injection molding space to establish a second front armrest injection molding space; according to the front armrest injection molding fitness function, the front armrest injection molding fitness is maximized in the second front armrest injection molding space to determine the front armrest injection molding optimization strategy, and the front armrest of the cart to be produced is manufactured 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] The present application performs confidence analysis on a front armrest injection molding 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 determines the front armrest injection molding target mold; performs 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 establishes a first space for front armrest injection molding; performs application scenario demand analysis on the front armrest of the cart to be produced according to 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 according to the front armrest injection molding evaluation expectation, and establishes a second front armrest injection molding space; performs optimization analysis 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, determines a front armrest injection molding optimization strategy, and executes the manufacturing of the front armrest of the cart to be produced according to the front armrest injection molding optimization strategy. The present invention solves the technical problem of unstable performance of the front armrest in the prior art during the manufacture of the front armrest, and achieves the technical effect of improving the manufacturing quality of the front armrest by performing confidence analysis on the injection mold library, mining injection molding parameters, analyzing application scenario requirements and optimizing the fitness function. 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 flow of a method for optimizing the manufacturing of a rubber front handrail for a child stroller provided in an embodiment of the present application;

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

[0012] The present application provides a method for optimizing the manufacturing of a rubber front armrest for a children's 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. 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.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work 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 explicitly listed, but may include other steps or modules that are not explicitly listed or 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 confidence analysis on the front armrest injection mold library according to 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 the front armrest injection molding target mold.

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

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

[0019] Furthermore, in the method provided in the embodiment of the application, 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 trolley to be produced, and a front armrest injection molding target mold is determined, which also includes:

[0020] The front armrest injection mold library is searched for injection molding history to obtain injection molding history information sets of each mold; based on the front armrest geometric design information and the front armrest injection molding raw material information, the injection molding history information sets of each mold are calculated 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 the embodiment of the present application, the injection molding history of the front armrest injection mold library is first retrieved to obtain the injection molding data of each mold in the historical production, and the injection molding history information set of each mold is formed. These information sets include the geometric design information and injection molding raw material information of the front armrests used in the past. The geometric design information refers to the design parameters such as the shape and size of the front armrest, while the injection molding raw material information refers to the type of material used to produce the front armrest and its characteristics, such as the fluidity and hardness of the material.

[0022] Next, based on the geometric design information and injection molding material information of the front handrail to be produced, the confidence of the injection molding history information set of each mold is calculated. When calculating, first traverse the injection molding history information set of each mold, and extract the historical data related to the geometric design information and injection molding material information of the front handrail to be produced; then, count the frequency of occurrence of the geometric design information and injection molding material information of the front handrail in each mold's historical data, and calculate the geometric support coefficient and injection molding material support coefficient respectively; then, by calculating the mean of these two support coefficients, the injection molding confidence of the mold is obtained. By repeating this process, multiple mold injection confidences are obtained.

[0023] Finally, the front armrest injection mold library is screened according to the injection confidence of multiple molds, and the mold with the highest confidence value is selected as the injection target mold of the front armrest. This process is achieved through a sorting algorithm, which ranks the molds from high to low according to the confidence, and selects the mold ranked first as the injection target mold of the front armrest.

[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 of each mold injection molding history information set is calculated to obtain multiple mold injection molding confidences, and further includes:

[0025] Traverse the injection molding history information sets of each mold 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 material information appearing in the first mold injection molding history information set to obtain the first mold injection molding material support coefficient; calculate the mean of the first mold geometric support coefficient and the first mold injection molding 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 the embodiment of the present application, each mold injection history information set is first traversed, and the first mold injection history information set is randomly extracted therefrom. In this process, the injection 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 raw material information. The geometric design information includes the design parameters such as the size and shape of the front armrest, and the injection raw 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. Through a matching algorithm (such as Euclidean distance calculation), the similarity between the current front armrest geometric design and the mold usage record in the historical data is calculated. If the similarity falls within a preset range, it is considered that the current front armrest geometric design information matches the historical data. When calculating the geometric support coefficient, the geometric support coefficient is obtained by dividing the number of times the current front armrest geometric design information matches the historical data by the total number of times the historical data matches.

[0028] Next, the injection molding raw material support coefficient is calculated. The injection molding raw material support coefficient measures the compatibility of the injection molding raw materials used in the current front armrest to be produced with 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 the characteristics of the raw materials, 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 the historical data is matched.

[0029] After calculating the geometric support factor and the injection material support factor, calculate the first mold injection confidence. When calculating, add the geometric support factor to the injection material support factor and then divide by 2 to get the first mold injection confidence. Finally, add the first mold injection confidence 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 according to the geometric design information and injection molding raw material information of the front armrest, first determine the key injection molding parameter factors, such as melt temperature, injection pressure, injection speed, holding time, mold temperature and cooling time, etc. Using the geometric design information and injection molding raw material information of the front armrest as constraints, perform injection molding parameter retrieval to determine the injection molding target parameters that need to be optimized. Next, based on these retrieval conditions, perform injection molding feature mining on the target mold, extract the relationship between each injection molding parameter and the production effect of the front armrest, and form a multivariate registration domain for the injection molding of the front armrest. Finally, by combining the injection molding parameters in the multivariate registration domain, generate the first space for the injection molding of the front armrest.

[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, and the first space for front armrest injection molding is established, which also 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 multi-element registration domain is established; injection molding parameters are combined according to the front armrest injection molding multi-element registration domain, and the front armrest injection molding first space is generated.

[0034] In the embodiment of the present application, the injection molding parameter factors pre-set by the technical experts are first obtained from the preset database, and the injection molding parameter factors include melt temperature, injection pressure, injection speed, holding time, mold temperature and cooling time. Then, the front handrail geometric design information and the front handrail 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 for screening.

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

[0036] Finally, according to the multivariate registration domain of the front armrest injection molding, the injection molding parameters are combined, that is, all possible injection molding process parameters are traversed and combined. In this process, all injection molding parameter factors are randomly combined within their allowed ranges, and multiple injection molding process configurations are generated. These random combinations represent a variety of possible injection molding process settings, covering all parameter changes. Finally, the first injection molding space of the front armrest is generated.

[0037] Furthermore, in the method provided in the embodiment of the application, the injection molding feature mining is performed on the front armrest injection molding target mold according to the injection molding retrieval constraint and the injection molding retrieval target, and the 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 targets 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, melting temperature, etc. 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 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, the concentrated intervals are identified based on the multivariate front armrest injection molding parameter clusters. This process uses statistical analysis methods (such as calculating the mean, standard deviation, maximum and minimum values) to identify the concentrated intervals of the injection molding parameters in each cluster. By statistically analyzing the injection molding parameters in each cluster, the most representative parameter intervals are found, and the ranges of injection molding parameters that have the greatest impact on molding quality are identified. The identification of concentrated intervals helps determine which parameter combinations are most beneficial to production results.

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

[0044] Step S300: analyzing the application scenario requirements of the front armrest of the trolley 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 according to 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 the qualified front armrest are retrieved to obtain the corresponding evaluation set, including qualified evaluation data of surface quality, mechanical strength and environmental tolerance. Then, the centralized value calculations are 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. It was then 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 evaluation factors of the injection molding 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 allocation 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, and 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 at 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 armrest application scenario information, the qualified front armrest evaluation records are retrieved. In this stage, historical data that meets the application scenario requirements are screened out through database query or data mining methods, including 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. Each historical data that meets the application scenario requirements includes a corresponding qualified front armrest surface quality evaluation, a qualified front armrest mechanical strength evaluation, and a qualified front armrest environmental tolerance evaluation, which are values ​​pre-marked 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 the 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. Then the expected surface quality evaluation coefficient, the expected mechanical strength evaluation coefficient, and the expected environmental tolerance evaluation coefficient are output as the front armrest injection molding evaluation expectation.

[0055] Finally, the importance of the injection molding evaluation factors of the front armrest is evaluated according to the application scenario information of the front armrest. Specifically, the importance of the injection molding evaluation factors is evaluated first, considering the different requirements of different application scenarios for surface quality, mechanical strength and environmental tolerance. In this process, the specific requirements of the application scenario, such as temperature, humidity, UV resistance, etc., will affect the relative importance of each evaluation factor. For example, in some application scenarios, such as outdoor use, environmental tolerance may be more important than surface quality and mechanical strength, while in other scenarios such as infant grasping, surface quality is given a higher weight. Through the analysis of these application scenario information, technical experts select the weight distribution corresponding to each evaluation factor according to the application scenario, where each evaluation factor has different weights in different environments, and these weight combinations are pre-set. Finally, these weights are applied to the injection molding evaluation factors of the front armrest to generate the front armrest injection molding fitness function. For example, in the scenario of outdoor use, the expected surface quality evaluation coefficient and the expected mechanical strength evaluation coefficient have a weight of 0.3, and the expected environmental tolerance evaluation coefficient has a weight of 0.4.

[0056] Through the aforementioned 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 evaluation expectation of the injection molding of the front armrest, and establishing the second injection molding space of the front armrest.

[0058] In an embodiment of the present application, when optimizing and analyzing the first space for 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 for front armrest injection molding. Then, based on the front armrest injection molding raw material information and the target mold, these preliminary strategies are simulated for injection molding evaluation to obtain the first front armrest injection molding evaluation result. Then, it is 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 for 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 for front armrest injection molding is finally established.

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

[0060] According to the first front armrest injection molding space, extract 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, simulate the injection molding evaluation of the first front armrest injection molding strategy to obtain the first front armrest injection molding evaluation result; determine 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, add the first front armrest injection molding strategy 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, eliminate the first front armrest injection molding strategy.

[0061] In the embodiment of the present application, first, according to the first injection molding space of the front armrest, the first injection molding strategy of the front armrest is extracted. The first injection molding space of the front armrest represents all possible injection molding process configurations, each of which includes injection molding parameters such as melt temperature, injection pressure, injection speed, etc. The first injection molding strategy of the front armrest is obtained by randomly extracting the first injection molding space of the front armrest.

[0062] Next, the first strategy of front armrest injection molding is simulated and evaluated based on the injection molding raw material information of the front armrest and the injection molding target mold of the front armrest. Specifically, firstly, based on the injection molding raw material information of the front armrest and the injection molding target mold of the front armrest, simulated injection molding is performed according to the first strategy of front armrest injection molding to obtain the first simulated injection-molded front armrest. Then, the first simulated injection-molded front armrest is evaluated for surface quality, mechanical strength and environmental tolerance to obtain 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, and 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 the injection molding evaluation result of the first front armrest.

[0063] Then, it is judged whether the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation. When making the judgment, 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, the expected mechanical strength evaluation coefficient and the expected environmental tolerance evaluation coefficient in the front armrest injection molding evaluation expectation. Only when all the coefficients in the first front armrest injection molding evaluation result are greater than all the coefficients in the front armrest injection molding evaluation expectation, it is considered that the first front armrest injection molding evaluation result meets the front armrest injection molding evaluation expectation.

[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 injection molding of the front armrest, repeating the aforementioned evaluation process and judgment process, finally completing the establishment of the second space for the injection molding of the front armrest.

[0066] Furthermore, in the method provided in the embodiment of the application, the first strategy of front armrest injection molding is simulated and evaluated according to the front armrest injection molding raw material information and the front armrest injection molding target mold to obtain the first front armrest injection molding evaluation result, and further includes:

[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 an environmental tolerance coefficient of the first front armrest; 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, firstly, 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 strategy of front armrest injection molding, and the first simulated injection molded front armrest is obtained. This process is completed by computer-aided engineering simulation (CAE) tools, and the injection molding process is simulated using injection molding simulation software (such as Moldflow, etc.). 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, a regression analysis model is used to evaluate the simulation results. The input data is 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 calibrated simulation data of the surface quality of the front armrest. These training data contain the 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] Then, the mechanical strength of the first simulated injection-molded front armrest is evaluated to obtain the mechanical strength coefficient of the first front armrest. This process is evaluated by the support vector machine (SVM) regression model, and the input data includes the stress, strain and load data in the simulation results. The SVM regression model is trained based on the known historical mechanical strength test data, and the training data includes the mechanical strength test results of the front armrest under different process parameters, such as compressive strength, bending strength, etc. These training data will be 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 multiple environmental front armrest geometric data. Then, based on the geometric data of the first injection molded front armrest, a loss analysis is performed on the geometric data of the multiple environmental front armrests to evaluate the tolerance of the front armrest in various environments and obtain the loss information of the multiple environmental front armrests. 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 is performed on the first simulated injection-molded front armrest to obtain the environmental tolerance coefficient of the first front armrest, and the method further includes:

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

[0075] In an embodiment of the present application, firstly, the geometric parameters of the first simulated injection-molded front armrest are 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, the geometric information of the first simulated injection-molded front armrest in multiple test environments is collected. In this process, the front armrest is virtually tested according to different environmental conditions through simulation software to simulate the performance of the front armrest in multiple test environments, such as low temperature, high temperature, extreme humidity, UV exposure, sweat and saliva immersion, etc. The simulation software recalculates the geometric shape of the front armrest based on the physical properties of the material and environmental influences, such as temperature, humidity changes, UV radiation, etc., and obtains the geometric data of the front armrest in multiple environments under these different environmental 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 in 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 armrests in 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 geometric comparison methods (such as the least squares method or the bounding box method) to measure the changes in the size of the front armrests under each environmental condition. Specifically, the simulation software calculates the changes in the length, width, thickness, etc. of the front armrests under different environmental conditions to obtain the loss information of the front armrests in multiple environments.

[0078] Finally, based on the front armrest loss information of multiple environments, the environmental tolerance of the front armrest is evaluated. In this process, the length change, width change and thickness change in the front armrest loss information of multiple environments are added and averaged to obtain the average length change, average width change and average thickness change. Next, the calculated average length change, average width change and average thickness change are multiplied by the preset weights to obtain the mean loss. Based on the obtained mean loss, the environmental tolerance coefficient is calculated. In order to determine that the smaller the size loss, the higher the environmental adaptability coefficient, the mean loss is reciprocally normalized, and the first front 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 fitness of the front armrest injection molding is maximized and optimized for the second space of the front armrest injection molding according to the fitness function of the front armrest injection molding. Specifically, the front armrest surface quality coefficient, the front armrest mechanical strength coefficient and the front armrest environmental tolerance coefficient corresponding to each strategy in the second space of the front armrest injection molding are substituted into the fitness function of the front armrest injection molding to calculate the fitness of the front armrest injection molding corresponding to each strategy. Then, the strategy with the largest fitness of the front armrest injection molding is selected as the optimization strategy for the front 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] The present application performs confidence analysis on a front armrest injection molding 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 determines the front armrest injection molding target mold; performs 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 establishes a first space for front armrest injection molding; performs application scenario demand analysis on the front armrest of the cart to be produced according to 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 according to the front armrest injection molding evaluation expectation, and establishes a second front armrest injection molding space; performs optimization analysis 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, determines a front armrest injection molding optimization strategy, and executes the manufacturing of the front armrest of the cart to be produced according to the front armrest injection molding optimization strategy. The present invention solves the technical problem of unstable performance of the front armrest in the prior art during the manufacture of the front armrest, and achieves the technical effect of improving the manufacturing quality of the front armrest by performing confidence analysis on the injection mold library, mining injection molding parameters, analyzing application scenario requirements and optimizing the fitness function.

[0084] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some 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 substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0086] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the manufacturing of a rubber front handrail for a child stroller, characterized in that: The method comprises: According to 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, the confidence analysis of the front armrest injection molding mold library is performed to determine the front armrest injection molding target mold; 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; According to the front armrest injection molding evaluation factor, the application scenario requirements of the front armrest of the trolley to be produced are analyzed, and the front armrest injection molding evaluation expectation and the front armrest injection molding fitness function are established; According to the front armrest injection molding evaluation expectation, an optimization analysis is performed on the first front armrest injection molding space to establish a second front armrest injection molding space; According to the front armrest injection molding fitness function, the front armrest injection molding fitness of the second space is maximized and optimized, the front armrest injection molding optimization strategy is determined, and the front armrest of the trolley to be produced is manufactured according to the front armrest injection molding optimization strategy.

2. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 1, characterized in that: According to the front armrest geometric design information and the front armrest injection molding raw material information, injection molding parameter mining is performed on the front armrest injection molding target mold to establish a front armrest injection molding first space, 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 constraint and the injection molding retrieval target, and establishing a front armrest injection molding multi-element registration domain; Injection molding parameters are combined according to the multi-element registration domain for injection molding of the front armrest to generate the first injection molding space of the front armrest.

3. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 2, characterized in that: The injection molding feature mining is performed on the front armrest injection molding target mold 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, including: Globally interconnecting the front armrest injection target mold with the same characteristic mold to obtain a global injection mold; Perform injection molding record retrieval on the global injection mold according to the injection molding retrieval constraint and the injection molding retrieval 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 according to 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, characterized in that: According to the front armrest injection molding evaluation factor, the application scenario requirements of the front armrest of the trolley 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 trolley to be produced; Based on the front armrest application scenario information, a qualified front armrest evaluation record is retrieved 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; Performing concentrated value calculation 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; The importance of the injection molding evaluation factor of the front armrest is evaluated according to the front armrest application scenario information to obtain the injection molding evaluation factor importance evaluation result, and the injection molding evaluation factor of the front armrest is weighted 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 1, characterized in that: According to the front armrest injection molding evaluation expectation, the first space for the front armrest injection molding is optimized and analyzed to establish the second space for the front armrest injection molding, including: Extracting a first strategy for front armrest injection molding according to the first space for front armrest injection molding; 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 front armrest injection molding first strategy 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.

6. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 5, characterized in that: The first front armrest injection molding strategy is simulated and evaluated 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 front armrest injection molding first 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.

7. A method for optimizing the manufacturing of a rubber front handrail for a child stroller as claimed in claim 6, characterized in that: The first simulated injection-molded front armrest is evaluated for environmental tolerance to obtain an environmental tolerance coefficient of the first front armrest, including: Collecting geometric parameters of the first simulated injection-molded front handrail in real time to obtain geometric data of the first injection-molded front 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; Perform loss analysis on the plurality of environmental front armrest geometry data according to the first injection molding front armrest geometry 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.

8. The method for optimizing the manufacturing of a rubber front handrail for a child stroller according to claim 1, characterized in that: According to 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, the confidence analysis of the front armrest injection molding mold library is performed to determine the front armrest injection molding target mold, including: Performing injection molding history retrieval on the front armrest injection mold library to obtain injection molding history information sets of each mold; Based on the front armrest geometric design information and the front armrest injection molding raw material information, confidence calculation is performed on each mold injection molding history information set to obtain multiple mold injection molding confidences; The front armrest injection mold library is screened according to the multiple mold injection confidences, and the front armrest injection target mold corresponding to the maximum mold injection confidence is determined.

9. The method for optimizing the manufacturing of a rubber front handrail for a stroller according to claim 8, characterized in that: Based on the front armrest geometric design information and the front armrest injection molding raw material information, confidence calculation is performed on each mold injection molding history information set to obtain multiple mold injection molding confidences, including: Traversing the mold injection history information sets, and extracting the first mold injection 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; The 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.

10. The method for optimizing the manufacturing of a rubber front handrail for a child stroller according to claim 1, characterized in that: 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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