Selection method, optimization method and application method for thermal forming die insert

Abstract

The invention relates to the technical field of thermal forming production, and discloses a selection method, an optimization method and an application method for a thermal forming mold insert, and the method comprises the following steps: A1, obtaining key factors influencing the service life of the insert; analyzing the influence relationship between the key factors and the service life of the insert, and establishing a prediction model of the key factors and the service life of the insert; a2, the forming process is simulated, the abrasion stress borne by the mold insert is calculated, and an area positioning map is obtained; selecting an insert of which the risk value of the thermal forming part production process is within a set range through the prediction model; a3, multiple performance tests are conducted on different materials, a material performance comparison report is obtained, and the optimal processing technology is matched; and A4, integrating the data, establishing a material selection decision tree, and forming an insert material selection standard. According to the method, key factors are found according to the abrasion data, the insert material and the surface treatment mode of the mold insert in the mass production project, the insert selection method is quantified, the mold development and maintenance cost is reduced, and meanwhile the service life of the mold is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of thermoforming production, and in particular to a selection method, an optimization method and an application method for thermoforming die inserts. Background Art

[0002] In the thermoforming process, mold inserts are core components that come into direct contact with high-temperature materials. Their performance directly affects molding quality, production efficiency, and mold life. The current industry generally adopts an empirical material selection method, relying on traditional material manuals or supplier recommendations, and lacks systematic quantitative analysis methods. Due to the complex thermoforming working conditions, inserts are subjected to high temperatures (above 700°C), high pressure, and cyclic thermal stress for a long time, resulting in frequent problems such as wear, thermal fatigue cracking, and annealing softening. Especially in high-friction areas, such as deep drawing or complex curved surface forming areas, the insert replacement rate remains high. Some production lines even require the replacement of key inserts every 50,000 stampings, which not only increases maintenance costs but also leads to downtime losses.

[0003] However, existing technologies make it difficult to accurately predict insert life at the beginning of a project, resulting in inserts that cannot be precisely matched to actual needs during actual hot forming operations. This results in mold costs accounting for 15%-25% of total production costs, becoming a factor in the high cost of hot forming production. More importantly, with the use of materials such as high-strength steel and patch plates, forming loads and friction conditions have further deteriorated. Although some existing technologies have attempted to use high-end materials as insert materials, they have not established material selection standards, resulting in the coexistence of performance redundancy and local failure, which in turn leads to waste of resources.

[0004] Therefore, there is an urgent need for an accurate insert selection system to fundamentally solve the problems of inaccurate life prediction, frequent replacement and high cost of existing inserts during use. Summary of the Invention

[0005] The present invention aims to provide a method for selecting, optimizing and applying thermoforming die inserts, so as to solve the problems in the prior art of inaccurate life prediction of inserts during use, incompatibility of matching with actual needs, frequent replacement and excessive cost.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: Option 1, a method for selecting a thermoforming mold insert, includes the following steps: Step A1, obtaining key factors affecting insert life; analyzing the influence relationship between key factors and insert life, and establishing a prediction model for each key factor and insert life; Step A2: Simulate the thermoforming process, calculate the wear stress on different mold inserts, and generate a regional location map. Use the prediction model to evaluate and select inserts whose thermoforming part production process risk values ​​are within a set range. Then, generate insert parameters for the corresponding materials. Step A3: Conduct multiple performance tests on different materials, obtain a material performance comparison report, and match the optimal processing technology; Step A4: Integrate the above data, establish a decision tree for process and insert material selection, and form insert material selection standards.

[0007] Solution 2 provides an optimization method for thermoforming mold inserts, which is applied to the above-mentioned method for selecting thermoforming mold inserts and is used to optimize insert performance based on insert selection criteria; the method comprises the following steps: Step B1: Select target inserts based on the established standards; compare the performance gap between existing inserts and standard performance to identify key improvement areas; Step B2: Obtain basic performance data of the insert material and provide an optimization plan based on the improvement direction; Step B3: Verify the performance of the improved insert, provide the verification results, and adjust the optimization plan based on the results.

[0008] Solution 3 provides an application method for thermoforming mold inserts, which is applied to the above-mentioned method for selecting thermoforming mold inserts, and is used to optimize and fine-tune product production according to the insert selection solution, as well as replace and maintain the inserts; the method comprises the following steps: Step C1: Based on product design data and production requirements, automatically recommend insert matching solutions and predict high-wear areas. It also compares the comprehensive benefits of different materials and processes to determine the most suitable configuration and assess the expected lifespan of the inserts. Step C2: Real-time collection of insert working data and dynamic assessment of insert wear status; when the warning value is reached, a maintenance warning is triggered and optimization suggestions are pushed; Step C3: Analyze the correlation between actual wear data and product defects, and reversely optimize the production process and product design.

[0009] The principles and advantages of this solution are: In the field of thermoforming, mold material selection is often simplified to a single performance indicator. This leads to one-sided thinking during mold design, such as prioritizing hardness requirements or thermal conductivity. This leads to ignoring the complex synergistic effects between performance parameters such as hardness and toughness, wear resistance, and thermal fatigue resistance. As a result, in actual mold production, even if one performance indicator is improved, it is easy to have hidden dangers in other key performance indicators.

[0010] Secondly, during thermoforming production, the product manufacturing process is routinely split into two unrelated sections: the product design line and the mold production line. On the product side, the focus is on the product's geometry and material properties, while on the mold side, the emphasis is on structure and application performance. Although the mold shape needs to be set according to the product structure, the design of their respective performance requirements is more like two parallel lines with no intersection. This is also something that is overlooked in existing technology: the subtle influence between product and mold. Even if the two can be produced in matching structure and shape, a slight change in product design can shorten the mold life exponentially, and adjustments to the mold material can unexpectedly affect the product's surface quality.

[0011] This solution creatively constructs a new collaborative optimization method. It builds a life prediction model for mold inserts through multi-parameter analysis, breaking the limitation that the performance of insert materials can only be evaluated by a single indicator, breaking the traditional single-indicator optimization thinking. At the same time, this solution establishes a two-way feedback channel between product flow and mold flow through dynamic analysis and selection matching of insert performance. When the system detects abnormal wear of an insert at a certain location, it can not only adjust the process parameters, but also automatically generate product design modification suggestions to achieve true collaborative optimization. The mold can be optimized through the selection of inserts, and the mold wear data will also reversely optimize the product design, thereby reducing the wear rate of the mold inserts, increasing the service life of the inserts, and improving product quality, realizing a true intelligent manufacturing closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The figure is a schematic flow chart of the method for selecting inserts for thermoforming molds according to the present invention.

[0013] Figure 2 The load and wear relationship curves of the three materials listed in the present invention under the same conditions are shown.

[0014] Figure 3 Schematic diagram of the sliding distance in the present invention.

[0015] Figure 4 The figure is a graph showing the effect of load and punching times on the service life of the three materials listed in the present invention.

[0016] Figure 5 The figure is a schematic flow chart of the optimization method for thermoforming mold inserts of the present invention.

[0017] Figure 6 The figure is a schematic flow chart of the application method of the present invention for thermoforming mold inserts. DETAILED DESCRIPTION

[0018] The following is further described in detail through specific implementation methods: Example The selection method, optimization method and application method for thermoforming mold inserts in this embodiment quantitatively analyze the factors affecting the life of the mold insert, build an accurate prediction model, and through dynamic monitoring and simulation, quickly analyze and judge the insert performance, quantify the insert selection method, reduce the mold development cost and improve the mold life.

[0019] Option 1: Selection method for thermoforming mold inserts, as shown in the attached Figure 1 As shown, based on multi-dimensional data analysis, CAE simulation and experimental verification, a standardized material selection system is formed, including the following steps: SA1 identifies the key factors influencing insert life; analyzes the relationship between these factors and insert life, and establishes a predictive model for each key factor and insert life. In the early stages of a new project, simply inputting process parameters yields an estimated lifespan, enabling quantitative insert material selection and ensuring predictable lifespan and controllable costs.

[0020] In this example, key factors for inserts are extracted by acquiring production data, material performance data, and process data. Production data includes mold wear records, replacement frequency, and failure modes. Material performance data includes insert material, hardness, thermal conductivity, and thermal expansion coefficient. Process data includes stamping speed, temperature, pressure, and surface treatment methods.

[0021] Key factors analyze the correlation between each parameter and wear loss through correlation analysis, and select parameters with correlations greater than a set threshold as key factors. In this embodiment, the Pearson coefficient can be used to determine the correlation between each parameter and wear loss, and parameters with correlation coefficients greater than 0.6 are selected as key factors. After correlation analysis, in this embodiment, key factors include the normal load at the contact point, sliding distance, sliding speed, R size, material thickness, temperature, friction coefficient, and others.

[0022] Through multiple analytical tests, we found that for different inserts, a key factor in their wear resistance testing is the normal load at the contact point. Therefore, under most working conditions, the normal load weight coefficient of the contact point can be set to 60%, and the weight coefficient can also be adjusted according to the processing conditions. The life analysis can also be used to determine the impact of the normal load at the contact point between the insert and the product. Based on the measurement of three materials (A\B\C) under the same conditions, the following is obtained: Figure 2The load-wear relationship curve shown in the figure shows that the greater the normal load at the contact point, the greater the impact on its life. There is also a critical load (force) for the insert. When the load exceeds the critical value (which can be called the failure force, i.e., the maximum limit of tensile strength), the wear will increase exponentially, thereby accelerating the impact on the insert life. Based on the influence of load on life, the key factors obtained are analyzed in correlation with load to obtain the influence relationship between the key factors and insert life.

[0023] Specifically, combined with Figure 3 The diagram of slip distance shown in the figure shows that during the downward pressing process, when the upper die contacts the product, the initial contact point is marked as E. At the completion of hot stamping, the contact point between the upper die and the product slides to point F. The distance E to F is defined as the slip distance. Analysis shows that a longer slip distance increases wear on the insert, and under the same load, the insert life is shortened. Therefore, the weight coefficient for slip distance is set to 5%.

[0024] In most hot stamping processes, the die is generally closed within 1-2 seconds, and the sliding speed is the speed from point E to point F. Its effect on the insert life in a short period of time is small. However, if the high-speed stamping is used, the faster the sliding speed, the greater the instantaneous power generated, resulting in a larger instantaneous load, which has a greater impact on the insert life. It is possible to exceed the critical value and have a greater impact on the insert life. Therefore, the weight coefficient of the sliding speed is set to 1%.

[0025] Regarding the R size, when the R angle is smaller, the force per unit area will increase, the load it bears will increase, and the lifespan will be shortened. The weight coefficient can be set to 10%. Similarly, when the material thickness increases, the punching force generated will increase, resulting in greater pressure on the mold and a shorter insert lifespan. The weight coefficient can be set to 10%. For hot stamping processes, when the temperature is lower, the hardness of the material will increase, resulting in greater pressure required, which will increase wear and reduce lifespan. The weight coefficient can be set to 1%. When faced with different insert materials, depending on the surface roughness and material of the insert, when the friction coefficient is greater, the friction will increase under the same load, and its lifespan will be shortened. The weight coefficient can be set to 10%. The weight coefficients of other factors such as material, process, surface treatment, etc. are approximately 3%.

[0026] According to the influence relationship of the analysis, a preliminary wear prediction model of key factors and unit load point is established, thereby constructing a prediction model of each key factor and insert life. Figure 4As shown in the figure, the analysis yields life curves for different materials, which plot loads and stamping times. It shows that when the load reaches a certain level and the number of stampings reaches a certain amount, the lifespan decreases significantly. Therefore, load control and a reasonable stamping capacity are also crucial to lifespan. Therefore, through model simulation, the load per unit stress point can be derived based on all key factors, and the corresponding insert material can be selected based on the load.

[0027] SA2 simulates the thermoforming process, calculates the wear stress on different mold inserts, and generates a regional positioning map. A predictive model is used to evaluate and select inserts whose thermoforming part production process risk values ​​are within a set range. The insert parameters are then generated for the corresponding materials.

[0028] In this embodiment, the influence relationship analyzed in SA1 and the weight coefficient obtained by calculation are used as input parameters together with the material parameters and process data to output a contact pressure distribution cloud map, thereby calculating the friction force distribution, that is, calculating the sliding resistance between the contact surface of the mold and the material, and marking the high pressure area as a potential high wear area.

[0029] The material's thermal expansion coefficient is then coupled with the temperature and structural fields to calculate the stress caused by the temperature gradient—the internal stress caused by the mold's high-temperature expansion—and the resulting thermal strain distribution, which reflects the wear stress. Finally, a friction thermogram and areas of concentrated thermal stress are output. Areas with friction exceeding a set value are marked as high-wear zones, resulting in a regional location map. This allows accurate identification of the areas most prone to failure under actual operating conditions, serving as a basis for experimental design and material selection, determining test conditions, and ensuring reliability.

[0030] The prediction model is used to evaluate and select inserts whose process risk values ​​for hot-formed parts production are within a set range; that is, inserts whose risk values ​​are less than the failure force are selected. In this embodiment, inserts whose risk values ​​are in the numerical range to the left of the failure force are selected, that is, they are judged to be risk-free. These inserts are selected as process insert options under risk-free conditions, and insert parameters under the corresponding process are formed to ensure that the inserts matched under the corresponding process are risk-free and optional inserts.

[0031] SA3, conducts multiple performance tests on inserts of different materials, obtains material performance comparison reports, and matches the optimal processing technology.

[0032] In this embodiment, the performance tests include high-temperature friction and wear tests, thermal cycle tests, and surface treatment evaluations to obtain a material performance ranking table for process matching.

[0033] High-temperature friction and wear testing simulates high-temperature and high-pressure environments, such as 100-700°C, and measures the wear rate of insert materials at different temperatures. Key measurement parameters include temperature gradient, load, friction coefficient, and wear volume. For example, the measurement results show that the wear volume of material A at 500°C is 30% lower than that of material B.

[0034] Thermal cycle testing follows the actual heat treatment process, such as 1030℃ quenching + 530℃ tempering, to detect hardness attenuation. For example, the HRC drop after 1000 cycles is obtained to obtain the hardness and thermal stability of different materials.

[0035] Surface treatment evaluations compare the friction coefficient changes of coatings (such as plasma nitriding) at high temperatures, measure the friction coefficient of different surface roughnesses, and examine the effects of coating adhesion and scale shedding on insert wear resistance. Performance after surface treatment, as well as performance and thermal conductivity testing after laser cladding, are also evaluated to accurately predict insert life and mold maintenance and pressure holding frequency. This results in a material performance ranking, facilitating the matching of optimal process windows based on the comprehensive performance indicators of different materials. This ensures the accuracy of simulation results, effectively eliminates deviations from theoretical models, and further adjusts the thresholds of material selection rules.

[0036] SA4, integrate the above data, establish a decision tree for process and insert material selection, and form the insert material selection standard.

[0037] In this embodiment, all the data obtained in the above steps, including weights, simulation data and experimental data, are combined with economic parameters to modify the insert material selection rules. Among them, economic parameters include material cost and processing difficulty. A decision tree for material selection is established. If the friction work is greater than X, material A is selected; if the thermal stress is greater than Y, a cooling channel design is added, etc. Finally, the material selection criteria and simulation templates are output. The templates include preset material parameters, friction models, etc., so that they can be quickly applied to different simulation tests to improve versatility and applicability. Rapid material selection for new projects is achieved, reducing trial and error costs.

[0038] Option 2 Provided is an optimization method for thermoforming mold inserts, which is applied to the above-mentioned selection method for thermoforming mold inserts and is used to optimize insert performance according to insert selection criteria; as shown in the attached Figure 5 As shown, the following steps are included: SB1, select target inserts based on established standards; compare the performance gap between existing inserts and standard performance to identify key improvement areas.

[0039] In this embodiment, an operating environment mapping analysis is first performed, considering not only the temperature threshold but also the temperature fluctuation range. For example, for operating temperatures fluctuating between 550-700°C, materials with excellent thermal fatigue resistance, such as A, are preferred, rather than simply pursuing the highest temperature resistance. Furthermore, a temperature-life curve model is established, combining thermal cycle predictions to quickly select inserts for the desired process.

[0040] At the same time, the dynamic friction coefficient evaluation method is used to analyze the stability of the friction coefficient with temperature changes. For working conditions where the friction coefficient increases significantly with increasing temperature, it is recommended to switch the insert grade. In this embodiment, the insert grades include ordinary inserts, medium inserts, high-grade inserts and special inserts. In addition, surface treatment options such as surface nitriding, plasma nitriding, and laser cladding can also be adopted, where the grades achieved by different alloy compositions and processes include ordinary, medium, high, and special. According to the different switching selection levels, the appropriate insert is selected accordingly, rather than a single coating insert treatment. In this way, multi-dimensional insert selection such as material heat resistance grade and surface treatment is carried out to obtain the target insert.

[0041] Next, we compare the performance of the selected inserts, translating mold requirements into measurable material properties, such as a lifespan requirement of 100,000 cycles. We then calculate the gap between the current insert performance and the target value, analyze and identify key areas for improvement, and ultimately propose precise optimization strategies.

[0042] SB2, obtain basic performance data of insert materials and provide optimization solutions based on improvement directions.

[0043] In this embodiment, the optimization analysis of inserts includes two categories. The first is when the risk value exceeds the maximum bearing capacity during the risk assessment, that is, exceeds the failure force and poses a risk. In this case, the insert can be optimized to increase the failure force threshold, thereby reducing the risk and expanding the range of insert options. The second is when the risk value assessment shows no risk, that is, the risk value is within an acceptable range, and the insert performance is further optimized based on the assessment results and process requirements to increase the insert service life.

[0044] In this embodiment, the basic properties of the insert material are analyzed, such as thermodynamic analysis of its component properties, to obtain performance data, thereby identifying its performance gaps. Based on the performance gaps and improvement directions, an optimization plan for the insert is proposed. In this embodiment, the optimization plan includes material modification and surface enhancement.

[0045] Among them, material modification can improve performance through cost optimization or process improvement. If, according to the simulation results, it is analyzed that the influence of the R angle has a greater impact on the current insert life, laser cladding can be used to increase local strength and enhance failure force, thereby reducing wear and increasing service life. At the same time, the laser cladding method can be used repeatedly. When the local part is worn again, local laser cladding can be performed again, reducing production and maintenance costs while increasing the service life of the insert. Surface enhancement can be based on the application of coatings that match the working conditions to select surface optimization treatment. For example, according to the attached Figure 2 As shown in the graph, material A can be surface treated to material B, such as with a nano-multilayer coating to reduce friction or a self-lubricating composite coating to increase heat resistance, to enhance performance, reduce wear, and thus increase insert life. This provides a precise and reliable insert optimization method based on quantitative data, achieving the most precise optimization results possible while reducing optimization costs and strength.

[0046] SB3, verify the performance of the improved insert, give the verification results, and adjust the optimization plan based on the results.

[0047] The performance of the optimized and improved inserts is tested again to achieve rapid verification and dynamic optimization cycle updates, so that the inserts can better adapt to different processing requirements under different working conditions, reduce the insert wear rate, increase the insert service life, ensure product production quality, and effectively reduce production costs.

[0048] Option 3 Provided is an application method for thermoforming mold inserts, which is applied to the above-mentioned selection method for thermoforming mold inserts, and is used to optimize and fine-tune product production according to the insert selection scheme, as well as replace and maintain the inserts; as shown in the attached Figure 6 As shown, the following steps are included: SC1 automatically recommends insert matching solutions and predicts high-wear areas based on product design data and production requirements. It also compares the comprehensive benefits of different materials and processes to obtain the most suitable configuration solution and evaluate the expected life of the insert.

[0049] In this example, an intelligent matching system rapidly determines the optimal insert solution based on product design data such as material thickness, geometric complexity, and molding difficulty, as well as production requirements such as cycle time and projected output. The system automatically analyzes key product characteristics such as material thickness, deep-draw ratio, and corner radius, and uses pre-configured CAE simulation templates to predict potential high-wear areas. Furthermore, incorporating a cost analysis model, it compares the comprehensive benefits of different materials (e.g., A vs. B), heat treatment processes, and surface treatment options, ultimately outputting an insert configuration that balances performance and cost, along with an accompanying life expectancy assessment report.

[0050] SC2 collects insert working data in real time and dynamically evaluates the insert wear status; when the warning value is reached, it triggers a maintenance warning and pushes optimization suggestions.

[0051] During the mass production phase, IoT sensors collect real-time insert operating data. In this embodiment, this data includes temperature, pressure, and vibration. Historical maintenance data is also collected and combined with a predictive model to dynamically assess wear status. When monitoring data reaches a preset threshold, such as a local temperature exceeding 600°C or wear reaching 0.15mm, the system automatically triggers a tiered maintenance alert and delivers optimization recommendations (such as adjusting lubrication frequency or stocking up in advance for insert replacement or maintenance). For example, during actual production, a significant amount of oxide scale accumulates inside the mold. When this volume reaches a certain level, it increases the friction coefficient between the part and the mold, thereby increasing insert wear and reducing its service life. Therefore, when a certain amount of oxide scale is detected, a maintenance recommendation is triggered, and cleaning methods such as air blowing can be recommended to reduce insert wear and increase service life. In this embodiment, all data is synchronized to a visual dashboard, enabling transparent management of insert health and real-time visualization of each insert's wear rate, facilitating effective insert management.

[0052] SC3 analyzes the correlation between actual wear data and product defects, and reversely optimizes production processes and product designs.

[0053] Based on correlation analysis between actual wear data and product defects, insert wear maps are overlaid with defect locations for reverse optimization of production processes and product designs. For example, when a risk assessment indicates a risk, or when the risk value is within the specified range but high, process parameters can be optimized by adjusting parameters such as blank holder force and lubrication to reduce insert load. Alternatively, design modifications such as corner radius and material thickness can be made to reduce insert wear and optimize product design. If data indicates abnormal insert wear rates at a particular corner radius, adjustments can be made to the product design (increasing the radius) and stamping parameters (reducing speed or increasing lubrication). Optimization results are fed back to the intelligent matching system, forming a closed loop of data collection, analysis, and optimization, continuously improving insert efficiency and reducing overall costs. This quantitative analysis of insert performance allows for one-way optimization of product production, increases insert life, and avoids waste caused by excess material performance. Furthermore, new project insert plans can be rapidly developed, improving overall efficiency and enabling the coordinated optimization of mold, process, and product design.

[0054] This implementation creatively constructs an intelligent, comprehensive approach across the entire mold material selection, optimization, and application chain, fundamentally transforming the traditional, extensive, trial-and-error approach to mold development. By leveraging a multi-parameter coupled material selection model, we quantitatively analyze the synergistic relationships among properties like hardness, thermal conductivity, and wear resistance, resolving the pain point of traditional material selection, where a single metric often overlooks the impact of other properties. This enables precise recommendations for the most appropriate material combinations for specific working conditions, further enhancing material matching.

[0055] Secondly, an innovative mechanism for collaborative optimization of products and molds has been established, breaking the long-standing "separate management" dilemma in the industry. Through real-time data construction, product design changes can automatically trigger mold solution adjustments, and mold wear data can reversely guide product optimization, forming a two-way intelligent closed loop. This improves overall development efficiency and effectively reduces the occurrence of unexpected failures. By continuously monitoring the usage status of inserts, it can not only warn of potential failures, but also autonomously generate optimization solutions. When abnormal wear is detected, the system can simultaneously provide solutions from three dimensions: material modification, process adjustment, and product design, achieving a leap from passive maintenance to active prevention, extending the overall service life of the mold, and reducing maintenance costs.

[0056] The above is only an embodiment of the present invention, and the common knowledge such as the specific technical solutions and / or characteristics in the solution are not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the description can be used to interpret the content of the claims.

Claims

1. A method for selecting a thermoforming mold insert, characterized in that: The following steps are involved: Step A1, obtaining key factors affecting insert life; analyzing the influence relationship between key factors and insert life, and establishing a prediction model for each key factor and insert life; Step A2: Simulate the thermoforming process, calculate the wear stress on different mold inserts, and generate a regional location map. Use the prediction model to evaluate and select inserts whose thermoforming part production process risk values ​​are within a set range. And form the insert parameters under the corresponding process; Step A3: Conduct multiple performance tests on different materials, obtain a material performance comparison report, and match the optimal processing technology; Step A4: Integrate the above data, establish a decision tree for process and insert material selection, and form insert material selection standards.

2. The method for selecting a thermoforming mold insert according to claim 1, wherein: In step A1, key factors of the insert are extracted by obtaining mass production data, material performance data and process data; the mass production data includes mold wear records, replacement frequency, and failure mode; the material performance data includes insert material, hardness, thermal conductivity, and thermal expansion coefficient; and the process data includes stamping speed, temperature, pressure, and surface treatment method.

3. The method for selecting a thermoforming mold insert according to claim 1, wherein: The key factors are analyzed through correlation analysis of the correlation between each parameter and the wear amount, and the parameters with correlation greater than the set threshold are screened as key factors; the key factors include the normal load of the contact point, sliding distance, sliding speed, R size, material thickness, temperature and friction coefficient; and the influence relationship between the key factors and the load is analyzed.

4. The method for selecting a thermoforming mold insert according to claim 1, wherein: In step A2, high pressure areas are treated as potential high wear areas; the thermal strain distribution is calculated by coupling the temperature field and the structural field; and areas where the friction force is greater than a set value are marked as high wear areas.

5. The method for selecting a thermoforming mold insert according to claim 1, wherein: In step A3, performance tests include high-temperature friction and wear tests, thermal cycle tests, and surface treatment evaluations to obtain a material performance ranking table for process matching.

6. The method for selecting a thermoforming mold insert according to claim 3, wherein: In step A4, it also includes modifying the insert material selection rules in combination with economic parameters; the economic parameters include material cost and processing difficulty.

7. A method for optimizing thermoforming mold inserts, characterized in that: The method for selecting a thermoforming mold insert according to any one of claims 1 to 6 is used to optimize insert performance according to insert selection criteria; comprising the following steps: Step B1: Select target inserts based on the established standards; compare the performance gap between existing inserts and standard performance to identify key improvement areas; Step B2: Obtain basic performance data of the insert material and provide an optimization plan based on the improvement direction; Step B3: Verify the performance of the improved insert, provide the verification results, and adjust the optimization plan based on the results.

8. The optimization method for thermoforming mold inserts according to claim 7, characterized in that: The optimization scheme includes material modification and surface enhancement.

9. Application method for thermoforming mold inserts, characterized in that: A method for selecting inserts for a thermoforming mold as described in any one of claims 1 to 6, for optimizing and fine-tuning product production according to an insert selection scheme, as well as replacing and maintaining inserts; comprising the following steps: Step C1: Based on product design data and production requirements, automatically recommend insert matching solutions and predict high-wear areas. It also compares the comprehensive benefits of different materials and processes to determine the most suitable configuration and assess the expected lifespan of the inserts. Step C2: Real-time collection of insert working data and dynamic assessment of insert wear status; when the warning value is reached, a maintenance warning is triggered and optimization suggestions are pushed; Step C3: Analyze the correlation between actual wear data and product defects, and reversely optimize the production process and product design.

10. The application method for thermoforming mold inserts according to claim 9, characterized in that: The operating data includes temperature, pressure and vibration.

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