A centrifugal casting process for manufacturing gaming chairs

Abstract

This invention relates to the field of metal processing technology and provides a centrifugal casting process for gaming chairs, comprising the following steps: Step S1, Process status assessment: collecting centrifugal rotation speed, casting temperature, filling time, and mold heating temperature to construct a process status assessment model and output a process stability index; Step S2, Material-equipment status assessment: collecting melt density equivalent, mold vibration amplitude, and mold center eccentricity relative to the main rotation axis to construct a material-equipment status assessment model and output a material consistency index; This invention, through the systematic integration of process status assessment, material-equipment status assessment, online molding quality assessment, and parameter closed-loop control, constructs a multi-source information fusion and intelligent decision-making framework covering the entire centrifugal casting process. This framework effectively solves the systemic defects in existing technologies, such as the difficulty in decoupling nonlinear coupling of process parameters, delayed quality feedback, and lack of scientific basis for control.

Description

Technical Field

[0001] This invention belongs to the field of metal processing technology, and in particular relates to a centrifugal casting process for processing gaming chairs. Background Technology

[0002] As a core piece of equipment in the modern esports industry, the manufacturing quality of the structural components of gaming chairs directly determines the product's durability, safety, and user experience. These structural components, especially key load-bearing parts such as the base and armrest supports, are generally made of lightweight, high-strength metal materials like aluminum alloys, formed using a centrifugal casting process. Centrifugal casting utilizes the centrifugal force generated by a rotating mold to drive molten metal to uniformly fill the cavity, thus forming a dense casting with excellent mechanical properties. This characteristic gives it an irreplaceable advantage in the manufacturing of key components for gaming chairs. However, in actual production, precise control of process parameters plays a decisive role in the quality of the casting.

[0003] Currently, the industry generally relies on the subjective experience of operators for parameter setting, employing offline planning and open-loop control modes. This makes it unable to adapt to dynamic changes in the production process, resulting in a lack of real-time optimization capabilities in the molding process. Specifically, existing technologies have significant shortcomings: The lack of a quantitative assessment mechanism for process status, the existence of complex nonlinear coupling relationships between key parameters such as centrifugal speed and pouring temperature, and the difficulty of constructing a comprehensive assessment model to quantify process stability using traditional methods, result in a lack of scientific basis for control decisions. The lack of coordinated monitoring of material properties and equipment operating status means that factors such as melt density equivalent and mold vibration amplitude can significantly interfere with the filling behavior and solidification process of molten metal. However, existing technologies have failed to establish a joint analysis framework, resulting in weak real-time perception of the molding process. Molding quality assessment is seriously lagging behind. Traditional methods rely on offline inspection methods after the casting has cooled, such as visual inspection, mechanical testing, or non-destructive testing, which cannot capture quality anomalies in real time during the casting process, thus missing the opportunity for early intervention.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a centrifugal casting process for manufacturing gaming chairs, thereby solving the aforementioned problems.

[0006] This invention is achieved through a centrifugal casting process for manufacturing gaming chairs, comprising the following steps: Step S1, Process Status Assessment: Collect centrifugal speed, pouring temperature, filling time and mold heating temperature, construct a process status assessment model, and output the process stability index; Step S2, Material-Equipment Condition Assessment: Collect melt density equivalent, mold vibration amplitude, and mold center eccentricity relative to the main rotation axis to construct a material-equipment condition assessment model and output the material consistency index; Step S3, Online evaluation of molding quality: Based on the process stability index, the material consistency index, the solidification front advance rate ratio, and the local undercooling ratio, an online evaluation model for molding quality is constructed, and the molding quality prediction index is output. Step S4, Speed ​​Control: Based on the molding quality prediction index and the current speed, construct a speed control model and output the optimized speed; Step S5, Heating Temperature Control: Based on the real-time cooling rate, the reference mold temperature, and the optimized rotation speed, a heating temperature control model is constructed, and the optimized heating temperature is output. Step S6: Adjust the process parameters of centrifugal casting according to the optimized rotation speed and the optimized heating temperature until the molding quality prediction index reaches the preset threshold.

[0007] In a further technical solution, in step S3, the solidification front advance rate ratio is obtained by embedding 2-3 thermocouple arrays at key positions in the mold cavity and calculating the advance speed by collecting temperature curve inflection points; the local undercooling ratio is obtained by comparing the solidification platform temperature recorded by thermocouples with the alloy liquidus temperature.

[0008] Further technical solutions, the process stability index is obtained through the following methods: The centrifugal speed index, casting temperature index, and mold heating temperature index were each subjected to S-shaped function transformation to obtain three S-shaped transformation values. The deviation of the filling time index from 1 is used as the filling time factor; Multiplying the three S-shaped transformation values ​​by the filling time factor yields the process stability index; Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

[0009] In a further technical solution, in step S2, the material consistency index is obtained in the following way: The material consistency index is obtained by multiplying the melt density equivalent index by the negative exponential decay term with the mold vibration amplitude index as the variable, and then by multiplying it by the negative exponential decay term with the eccentricity index as the variable. Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

[0010] In a further technical solution, in step S3, the molding quality prediction index is obtained through the following method: The basic quality factor is obtained by multiplying the preset first influence coefficient power of the process stability index with the preset second influence coefficient power of the material consistency index. Multiply the square of (1 minus the solidification front advance rate ratio index) and the square of the local undercooling ratio index by the corresponding preset influence coefficients, and then divide by the sum of the two preset influence coefficients to obtain the deviation term. Multiply the basic quality factor by (1 minus the deviation term) to obtain the molding quality prediction index; in The local supercooling ratio index is obtained by substituting it into the maximum-minimum normalization formula.

[0011] In a further technical solution, step S4, the optimized rotational speed is obtained through the following method: Calculate the directional sign of the deviation between the current speed and the optimal speed; The adjustment range is obtained by multiplying (1 minus the molding quality prediction index) and the control strength coefficient. Multiply the current speed by (1 plus the product of the adjustment range and the direction sign) to obtain the preliminary optimized speed; The initial optimized speed is limited to between the preset lower and upper speed limits to obtain the optimized speed.

[0012] In a further technical solution, step S5, the optimized heating temperature is obtained through the following method: Calculate the product of the real-time cooling rate index and the preset cooling rate compensation coefficient, and then add 1 to obtain the cooling rate compensation factor. Calculate 1 minus (the product of the preset speed influence coefficient and (1 minus the optimized speed index)) to obtain the speed compensation factor; Multiply the set heating temperature baseline value by the cooling rate compensation factor and the rotation speed compensation factor to obtain the preliminary optimized temperature; The initial optimized temperature is limited between the preset lower and upper limits of the heating temperature to obtain the optimized heating temperature; The real-time cooling rate index and the optimized rotation speed index are calculated by substituting the real-time cooling rate and the optimized rotation speed into the maximum-minimum normalization formula, respectively.

[0013] A further technical solution is that the maximum-minimum normalization formula is: (input value minus the set lower limit value) divided by (set upper limit value minus the set lower limit value) to obtain the normalized output value.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs a process status assessment model in step S1, incorporating key parameters such as centrifugal speed, pouring temperature, filling time, and mold heating temperature into a unified quantitative framework, and outputs a process stability index. In step S2, it constructs a material-equipment status assessment model, integrating real-time monitoring data such as melt density equivalent, mold vibration amplitude, and eccentricity, and outputs a material consistency index. This multi-level quantitative assessment system overcomes the shortcomings of traditional processes that rely on subjective experience and lack quantitative basis, achieving precise perception and scientific characterization of the process and equipment status.

[0015] 2. In step S3 of this invention, based on the process stability index, material consistency index, solidification front advance rate ratio, and local undercooling ratio, an online evaluation model for molding quality is constructed, and the molding quality prediction index is output in real time. This mechanism changes the traditional quality evaluation mode that relies on offline detection after casting cooling. It can dynamically capture quality anomalies during the casting process, providing timely and accurate decision-making basis for early intervention, and significantly reducing the risk of missed quality defects.

[0016] 3. In step S4 of this invention, a speed control model is constructed based on the molding quality prediction index and the current speed, and an optimized speed is output. In step S5, a heating temperature control model is constructed based on the real-time cooling rate, the reference mold temperature, and the optimized speed, and an optimized heating temperature is output. Through closed-loop adjustment in step S6, the molding quality prediction index is adjusted until it reaches a preset threshold. This closed-loop control mechanism overcomes the shortcomings of traditional open-loop control in adapting to dynamic changes and realizes online dynamic optimization of key process parameters.

[0017] 4. This invention constructs a multi-source information fusion and intelligent decision-making framework covering the entire centrifugal casting process through the systematic integration of process status assessment, material-equipment status assessment, online molding quality assessment, and closed-loop parameter control. This framework effectively solves the systemic defects in existing technologies, such as the difficulty in decoupling nonlinear coupling of process parameters, lag in quality feedback, and lack of scientific basis for control. It significantly improves the internal uniformity of the structure and the consistency of mechanical properties of key structural components of gaming chairs, reduces the scrap rate, and achieves comprehensive optimization of product quality and production efficiency. Attached Figure Description

[0018] Figure 1 A schematic diagram illustrating the steps of centrifugal casting molding process for manufacturing gaming chairs. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] like Figure 1 As shown, a centrifugal casting molding process for processing gaming chairs is provided in one embodiment of the present invention, including the following steps: Step S1, Process Status Assessment: Collect centrifugal speed, pouring temperature, filling time and mold heating temperature, construct a process status assessment model, and output the process stability index; Step S2, Material-Equipment Condition Assessment: Collect melt density equivalent, mold vibration amplitude, and mold center eccentricity relative to the main rotation axis to construct a material-equipment condition assessment model and output the material consistency index; Step S3, Online evaluation of molding quality: Based on the process stability index, the material consistency index, the solidification front advance rate ratio, and the local undercooling ratio, an online evaluation model for molding quality is constructed, and the molding quality prediction index is output. Step S4, Speed ​​Control: Based on the molding quality prediction index and the current speed, construct a speed control model and output the optimized speed; Step S5, Heating Temperature Control: Based on the real-time cooling rate, the reference mold temperature, and the optimized rotation speed, a heating temperature control model is constructed, and the optimized heating temperature is output. Step S6: Adjust the process parameters of centrifugal casting according to the optimized rotation speed and the optimized heating temperature until the molding quality prediction index reaches the preset threshold.

[0022] In this embodiment, centrifugal casting is a manufacturing method that uses centrifugal force to inject molten metal into a rotating mold cavity, aiming to produce castings with a dense structure and excellent mechanical properties. This process is widely used in the manufacture of key load-bearing components such as gaming chair bases and armrest supports.

[0023] The process status assessment model is a computational framework used to analyze key process parameters such as centrifugal speed, casting temperature, filling time, and mold heating temperature. By comprehensively analyzing these parameters, the model outputs a process stability index to quantify the stability of the current process operation.

[0024] The material-equipment condition assessment model aims to collect data such as melt density equivalent, mold vibration amplitude, and mold center eccentricity relative to the main rotation axis. Based on this data, a model is constructed, outputting a material consistency index. This index is used to evaluate the uniformity of the molten material and the stability of equipment operation.

[0025] The online casting quality assessment model constructs a predictive model based on the aforementioned process stability index, material consistency index, solidification front advance rate ratio, and local undercooling ratio, outputting a casting quality prediction index. This index aims to predict the final quality of the casting in real time.

[0026] The speed control model constructs a control strategy based on the molding quality prediction index and the current speed, outputting an optimized speed. This model aims to dynamically adjust the centrifugal speed to optimize the molding quality of the casting.

[0027] The heating temperature control model constructs a control strategy based on the real-time cooling rate, the reference mold temperature, and the optimized rotation speed, outputting an optimized heating temperature. This model aims to precisely control the mold's heating temperature to influence the solidification process of the molten metal.

[0028] Optimized rotation speed is calculated based on current process conditions and predictive models to determine the mold rotation speed most beneficial for achieving the desired casting quality. Optimized heating temperature is calculated based on factors such as solidification kinetics and process stability to determine the mold heating temperature most beneficial for achieving the desired casting quality.

[0029] In a preferred embodiment of the present invention, in step S3, the solidification front advance rate ratio is obtained by embedding 2-3 thermocouple arrays at key positions in the mold cavity and collecting temperature curve inflection points to calculate the advance speed; the local undercooling ratio is obtained by comparing the solidification platform temperature recorded by thermocouples with the alloy liquidus temperature.

[0030] In this embodiment, in step S3 of the centrifugal casting molding process for gaming chairs, this application proposes a specific measurement method to address the inaccuracy and untimely online real-time measurement of the solidification front advance rate ratio and the local undercooling ratio. This method achieves accurate measurement of the solidification front advance rate ratio by embedding 2-3 thermocouple arrays at key locations in the mold cavity. Specifically, when molten metal is injected into the mold cavity and begins to solidify, the thermocouple arrays collect real-time temperature change data at their locations. As the solidification front advances, different thermocouples sequentially sense a sharp drop in temperature, resulting in inflection points on the temperature curves. By analyzing the time difference between these inflection points and the known distance between the thermocouples, the system can accurately calculate the advancement speed of the solidification front, thereby obtaining the solidification front advance rate ratio.

[0031] Simultaneously, this scheme utilizes thermocouples to record the solidification plateau temperature and compares it with a pre-determined alloy liquidus temperature to measure the local undercooling ratio in real time. During metal solidification, due to the release of latent heat, the temperature experiences a relatively stable plateau period, which thermocouples can accurately capture. Comparing this real-time measured solidification plateau temperature with the inherent liquidus temperature of the alloy allows for the quantification of the degree of undercooling in local areas. Through the above measurement methods, this application can provide accurate and real-time data on the solidification front advance rate ratio and local undercooling ratio for the online molding quality evaluation model. These data, as important input parameters of the online molding quality evaluation model, work together with the process stability index and material consistency index to make the calculation of the molding quality prediction index more accurate and reliable. This online, real-time measurement method overcomes the lag of traditional offline detection, making dynamic monitoring and quality prediction of the molding process possible, providing a solid data foundation for subsequent speed control and heating temperature control, thereby achieving closed-loop optimization of the entire centrifugal casting molding process.

[0032] In a preferred embodiment of the present invention, the process stability index is calculated in step S1 as follows:

[0033] in The influence coefficient of centrifugal speed. The coefficient representing the influence of pouring temperature. The coefficient representing the influence of mold heating temperature. , , , The centrifugal speed index. The pouring temperature index, The filling time index, The mold heating temperature index. This is the process stability index; , , , The method for obtaining the values ​​is as follows: the centrifugal speed, pouring temperature, filling time, and mold heating temperature are respectively substituted into the maximum and minimum normalization formulas for calculation.

[0034] In this embodiment, the process stability index This index is used to quantitatively assess the combined influence of various process parameters during centrifugal casting. Its purpose is to intuitively reflect the stability of the current process; a higher value generally indicates a more stable process. This index can be calculated using a pre-set mathematical model or through a comprehensive assessment system combining fuzzy logic and expert experience.

[0035] Centrifugal speed influence coefficient Influence coefficient of pouring temperature and the influence coefficient of mold heating temperature These are weighting factors used to adjust the influence of corresponding process parameters on the process stability index. These coefficients allow the model to flexibly adjust the importance of different parameters in the evaluation based on actual material properties, casting geometry, or specific process requirements. These influence coefficients can be obtained through regression analysis of historical production data, empirical settings based on domain expert knowledge, or iterative optimization and learning through machine learning algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.).

[0036] In the centrifugal casting process of gaming chair bases, raw data such as centrifugal speed, casting temperature, filling time, and mold heating temperature can be collected in real time using sensors. For example, centrifugal speed can be obtained using a speed sensor mounted on the shaft, casting temperature and mold heating temperature can be monitored using a thermocouple array, and filling time can be measured with the assistance of a timer or vision system. (Centrifugal speed index) Pouring temperature index Filling time index and mold heating temperature index This refers to dimensionless values ​​obtained by normalizing the original collected process parameters such as centrifugal speed, pouring temperature, filling time, and mold heating temperature. The solution in this application introduces a specific mathematical calculation model to accurately quantify the process stability index. This model incorporates four key process parameters: centrifugal speed, casting temperature, filling time, and mold heating temperature. First, these raw parameters are converted into dimensionless exponents using a max-min normalization formula. , , , This eliminates the dimensional differences between different parameters, ensuring data consistency and comparability. Next, the model applies the centrifugal speed index... Pouring temperature index and mold heating temperature index Using the Sigmoid function for processing, this non-linear processing method can more accurately capture the sensitive influence of these parameters on process stability in different value ranges. That is, the influence is small when the parameter is close to the optimal value, while the influence increases rapidly when it deviates from the optimal value, which is consistent with the actual process characteristics.

[0037] For filling time index The model adopts The model intuitively reflects the physical law that a shorter filling time leads to higher process stability. Furthermore, the model incorporates the influence coefficient of centrifugal speed. Influence coefficient of pouring temperature Influence coefficient of mold heating temperature These coefficients allow for flexible adjustment of the relative importance of each parameter to process stability based on specific material properties and casting requirements. Finally, by multiplying and combining these processed parameter terms, the process stability index is ensured. It can comprehensively reflect the coupling effect of all key parameters. This multiplicative structure means that a significant deviation of any parameter will lead to a decrease in the overall process stability index, thus achieving a precise quantitative assessment of the overall process status. This precise process stability index... As a reliable input for subsequent online evaluation of molding quality (step S3), it significantly improves the accuracy and effectiveness of online monitoring and closed-loop control of the entire centrifugal casting molding process, and solves the problem of inaccurate and unreliable process status evaluation in traditional methods.

[0038] In a preferred embodiment of the present invention, the material consistency index is calculated in step S2 as follows: ; in The influence coefficient of mold vibration. The eccentricity influence coefficient is... , It is the equivalent density index of the solution. The vibration amplitude index of the mold. The eccentricity index, The material consistency index; in , , The method for obtaining the values ​​is as follows: the melt density equivalent, the mold vibration amplitude, and the eccentricity of the mold center relative to the main rotation axis are substituted into the maximum and minimum normalization formulas for calculation.

[0039] In this embodiment, the material consistency index is a comprehensive quantitative indicator used to evaluate the degree of matching and stability between the melt material properties and the equipment operating conditions during centrifugal casting. Its concept lies in integrating multiple factors affecting casting quality, such as melt density, mold vibration, and eccentricity, into a single value to facilitate subsequent molding quality assessment and process parameter control. This index can reflect the material's fluidity, filling capacity, and the stability of its solidification behavior under specific equipment conditions. For example, a high material consistency index indicates good melt properties and equipment operating conditions, which is conducive to obtaining high-quality castings; conversely, it may indicate potential quality defects. The melt density equivalent index, mold vibration amplitude index, and eccentricity index are dimensionless values ​​of the original physical quantities after normalization. The purpose of normalization is to eliminate dimensional and order-of-magnitude differences between different physical quantities, enabling them to be compared and calculated within a unified mathematical model.

[0040] Among them, melt density equivalent index The density characteristic of molten metal is a key factor affecting the filling and solidification process of molten metal. It can be obtained by substituting the real-time measured melt density value into the maximum-minimum normalization formula for calculation, or by performing a linear mapping through a preset density range.

[0041] Mold vibration amplitude index This reflects the vibration intensity of the mold during centrifugal casting. Excessive vibration amplitude may lead to molten metal splashing, unstable mold filling, or surface defects in the casting. It can be obtained by using an accelerometer mounted on the mold to collect vibration data in real time and substituting the peak or root mean square value into a maximum-minimum normalization formula for calculation, or by directly outputting a normalized signal from the vibration sensor.

[0042] Eccentricity Index This indicates the degree of deviation of the mold center from the rotation axis. Eccentricity leads to uneven distribution of centrifugal force, affecting the uniform filling and solidification of the melt, and may even cause equipment imbalance. It can be obtained by real-time monitoring of the relative position of the mold's geometric center to the rotation axis using a laser displacement sensor or vision system, and then calculating the eccentricity distance using a maximum-minimum normalization formula, or by using a combination of encoder and displacement sensor calculations.

[0043] Mold vibration influence coefficient And eccentricity influence coefficient These are weighting parameters in the model, used to quantify the negative impact of mold vibration and eccentricity on the material consistency index. They are positive values, indicating that the greater the vibration and eccentricity, the more significant the negative impact on material consistency. These coefficients can be calibrated and adjusted using historical production data, expert experience, or optimization algorithms to better fit the model to actual production conditions.

[0044] The centrifugal casting process for gaming chair manufacturing disclosed in this application addresses the lack of precise mathematical expression for material properties and equipment condition assessment in traditional processes by introducing a quantitative material consistency index calculation method in step S2. The core of this solution lies in converting three key parameters—melt density equivalent, mold vibration amplitude, and eccentricity of the mold center relative to the main rotation axis—into a dimensionless melt density equivalent index through maximum-minimum normalization. Mold vibration amplitude index and eccentricity index This conversion ensures the comparability of different physical quantities in the model, avoiding evaluation biases caused by dimensional differences. Based on this, the material consistency index... The calculation is performed in product form. This formula cleverly incorporates the melt density equivalent index. As a fundamental term, it reflects the dominant role of the material's inherent properties. Simultaneously, through exponential functions... and This is used to characterize the negative impact of mold vibration and eccentricity on material consistency. The exponential function, with its non-linear decay characteristic, more accurately reflects how material consistency decreases non-linearly as vibration amplitude and eccentricity increase. Mold vibration influence coefficient. And eccentricity influence coefficient As a weight, the index can be adjusted based on actual production experience or optimization results to accurately capture the specific impact of different factors on material consistency. This calculation method allows the material consistency index to comprehensively reflect the intrinsic quality of the melt material and the operational stability of the centrifugal casting equipment. When the melt density equivalent is high, the mold vibration is small, and the eccentricity is small, the index value approaches 1, indicating that the material and equipment are in good condition; conversely, when any negative factor increases, the index value will decrease significantly, thus accurately predicting potential molding quality problems. As an important input to the subsequent online molding quality assessment model (step S3), this index works synergistically with the process stability index to provide comprehensive and accurate data support for casting quality prediction. This enables the entire centrifugal casting molding process to achieve refined perception and quantitative assessment of the material and equipment conditions, laying a solid foundation for subsequent closed-loop control.

[0045] In a preferred embodiment of the present invention, the molding quality prediction index is calculated in step S3 as follows:

[0046] in The coefficient representing the influence of process stability. The coefficient representing the influence of material consistency. , , This is the coefficient affecting the solidification rate. The influence coefficient of local undercooling ratio. , , This is the process stability index. This is the material consistency index. The solidification front advance rate ratio index, The local undercooling ratio index. This is a molding quality prediction index; in , The method for obtaining these values ​​is as follows: the ratio of solidification front advance rate and the ratio of local undercooling are substituted into the maximum and minimum normalization formulas for calculation.

[0047] In this embodiment, the molding quality prediction index This is a comprehensive index used to quantify the forming quality of castings during centrifugal casting. The index is typically set between 0 and 1, with higher values ​​indicating better forming quality and lower values ​​indicating potential quality defects. Its purpose is to provide a real-time, quantitative quality assessment result to facilitate subsequent process parameter adjustments and optimization. This index can serve as a decision-making basis, guiding the system to automatically adjust process parameters, or providing early warning information to operators.

[0048] Process stability influence coefficient Used to adjust the process stability index In the molding quality prediction index The weight used in the calculation. This coefficient ranges from [0.5, 2]. For example, when production requires high process stability, the weight can be appropriately increased. The value of the process stability index is used to make the process stability index... Changes in molding quality prediction index This will have a greater impact; conversely, if the process stability has a relatively small impact on quality under certain conditions, it can be reduced. The value of . Based on single-factor experiments, other parameters can be kept constant while the centrifuge speed or pouring temperature is changed to determine the mechanical properties of the casting (such as tensile strength) and fit the results. ∝ Using the least squares method to estimate If experimental data is lacking, settings can be made based on experience and adjusted later through online learning.

[0049] Material consistency influence coefficient Used to adjust the material consistency index In the molding quality prediction index The weight in the calculation. This coefficient also ranges from [0.5, 2], and its effect is related to the process stability influence coefficient. Similarly, the aim is to dynamically adjust the weight of material properties and equipment conditions in the overall quality assessment based on their impact on casting quality. For example, when there are significant fluctuations in melt composition or equipment operating conditions, the weight of these factors in the overall quality assessment can be increased. This emphasizes the importance of material consistency for quality; if the materials and equipment are in stable condition, the reduction can be appropriately increased. . Orthogonal experiments can be designed to simultaneously perturb the melt density (e.g., different recycled material ratios) and the vibration amplitude (e.g., unbalanced loads), measure the porosity or density uniformity of the casting, and then obtain the results via regression. .

[0050] Solidification rate influence coefficient Used to adjust the solidification front advance rate ratio index In the molding quality prediction index Weighting in the calculation. This coefficient ranges from [0.5, 3] and its function is to quantify the impact of the solidification front advance rate deviating from the ideal value on the casting quality. For example, in some alloy systems, the solidification rate is crucial to the grain structure and mechanical properties; in this case, a larger weighting can be set. This value is used to ensure that even small deviations in the solidification rate can significantly reduce the molding quality prediction index. In scenarios where the sensitivity to solidification rate is low, the concentration can be appropriately reduced. . The secondary dendrite arm spacing (SDAS) of the casting at different feed rates can be calculated by arranging thermocouples in the mold, thus establishing SDAS∝(1−R). 2 relation, Take the normalized sensitivity coefficient.

[0051] Local undercooling ratio influence coefficient Used to adjust the local supercooling ratio index In the molding quality prediction index The weighting in the calculation. This coefficient also ranges from [0.5, 3], and its function is to quantify the impact of local undercooling deviation from the ideal value on casting quality. Local undercooling is a key factor affecting the formation of defects such as shrinkage cavities and porosity in castings. This can be addressed by adjusting... Based on the solidification characteristics of different alloys and the product's sensitivity to defects, the effect of local undercooling on the forming quality prediction index can be flexibly controlled. The effect. For example, for alloys prone to shrinkage cavities, it can increase This is to strengthen its weight in quality assessment. The relationship between undercooling and grain size and shrinkage tendency can be determined through differential thermal analysis or observation of the casting microstructure. Take the sensitivity normalized value.

[0052] Process stability index This index measures the overall stability of process parameters such as centrifugal speed, pouring temperature, filling time, and mold heating temperature. It is typically calculated using a pre-built process condition assessment model; a higher value indicates a more stable process condition, which is more conducive to obtaining high-quality castings. The index can be obtained based on machine learning models trained on historical data, such as support vector machines or neural networks, or through comprehensive judgment using expert system rules.

[0053] Material consistency index This index measures the overall consistency between melt material properties and equipment operating conditions, including melt density equivalent, mold vibration amplitude, and eccentricity of the mold center relative to the main rotation axis. It is typically calculated using a pre-built material-equipment condition assessment model; a higher value indicates a more stable material and equipment condition, which is more conducive to smooth filling and solidification of the molten metal. This index can be obtained using sensor data fusion methods, such as Kalman filtering, or through real-time monitoring and evaluation of various parameters using statistical process control (SPC) methods.

[0054] solidification front advance rate ratio index This index measures the ratio between the solidification front advance speed and the ideal or reference speed during the solidification process of a casting. It reflects the dynamic characteristics of the molten metal solidification process and has a significant impact on the uniformity of the casting's microstructure and the formation of defects. Its acquisition typically involves embedding sensors, such as thermocouple arrays, at key locations within the mold cavity to monitor temperature changes in real time. The advance speed of the solidification front is calculated using the inflection points of the temperature curve, compared with a preset reference speed to obtain the ratio, and finally normalized.

[0055] Local supercooling ratio index Local undercooling is an indicator that measures the ratio between local undercooling and ideal or reference undercooling during the solidification process of a casting. Local undercooling is a key factor affecting crystal nucleation and grain size; excessive undercooling can lead to fine grains or non-equilibrium solidification. It is typically obtained by recording the solidification plateau temperature using thermocouples, comparing it with the alloy's liquidus temperature to calculate the local undercooling, then comparing it with a preset reference undercooling to obtain the ratio, and finally normalizing the result.

[0056] The solution in this application constructs a molding quality prediction index. The computational model enables online and dynamic evaluation of casting quality during centrifugal casting. This model incorporates the process stability index... Material consistency index The ratio of the solidification front advance rate to the index and the local supercooling ratio index Organic integration is achieved. Specifically, the molding quality prediction index... The calculation is divided into two main parts. The first part is... This part uses the process stability index and material consistency index The exponentially weighted product comprehensively reflects the macroscopic stability of the centrifugal casting process and the reliability of the input materials and equipment conditions. Among these, the influence coefficient... and The relative importance of process stability and material consistency can be flexibly adjusted based on actual production experience or specific alloy systems. Part Two is... This section focuses on evaluating the microscopic quality performance of castings during solidification. This is achieved by introducing a solidification front advance rate ratio index. The ratio of local supercooling to index By applying a squared penalty to the degree of deviation from the ideal state, anomalies in the solidification process can be sensitively captured. Influence coefficient and This is used to adjust the weights of the solidification rate and local undercooling on the final quality. Finally, these two parts are multiplied together to obtain the molding quality prediction index. It can simultaneously reflect the combined impact of macroscopic process conditions and microscopic solidification behavior on casting quality. This layered and weighted design ensures the comprehensiveness and accuracy of quality assessment.

[0057] This online molding quality assessment model is closely integrated with the overall process of centrifugal casting molding for gaming chair manufacturing. In step S1, the process stability index output by the process status assessment model is used. And the material consistency index output by the material-equipment condition assessment model in step S2. , is the molding quality prediction index The calculations provide fundamental macroscopic inputs. Meanwhile, the solidification front propagation rate is higher than... Compared with local supercooling As a solidification process parameter for real-time monitoring, after being converted into exponential form using the maximum-minimum normalization formula, it is compared with... and joint participation The calculation yields a molding quality prediction index $Q$, which serves as a key basis for speed control in step S4 and heating temperature control in step S5. This allows subsequent process parameter adjustments to be based on real-time and comprehensive quality assessment results, thereby achieving closed-loop optimization and dynamic control of the centrifugal casting process and significantly improving the quality consistency and production efficiency of castings.

[0058] In a preferred embodiment of the present invention, the method for calculating the optimized rotational speed in step S4 is as follows:

[0059] in To adjust the intensity coefficient, , The current rotational speed, This is a molding quality prediction index. The variable representing the direction of the deviation between the current speed and the optimal speed. Used to represent Positive and negative directions, This is the upper limit of the rotational speed. This is the lower limit of the rotational speed. To optimize the rotational speed.

[0060] In this embodiment, the intensity coefficient is adjusted. Used to quantify the aggressiveness or responsiveness of speed adjustment, its value range is: For example, when When the value is small, the speed adjustment will be relatively smooth, which is suitable for scenarios with high requirements for process stability or small fluctuations; when A larger value results in faster speed adjustments, suitable for scenarios requiring quick responses to changes in quality. This coefficient can be set based on historical data analysis, expert experience, or optimized through trial production. Current speed This refers to the actual operating speed of the centrifugal casting equipment. This parameter is usually acquired in real time by the equipment's built-in speed sensor and serves as the basis for subsequent optimization calculations. Molding quality prediction index. This is a real-time estimated value for measuring the forming quality of centrifugally cast parts. A higher value indicates better forming quality. Obtaining this index relies on a comprehensive evaluation of multiple parameters, including process stability index, material consistency index, solidification front advance rate ratio, and local undercooling ratio. As a feedback signal, it directly reflects the product quality level under the current process conditions. The variable representing the direction of deviation between the current rotational speed and the optimal rotational speed is... Used to indicate the current speed The direction of deviation from the ideal or optimal speed. For example, its sign can be determined by comparing the current speed with a preset optimal speed range, or by analyzing the molding quality prediction index. The direction of speed adjustment can be inferred from the changing trend. It is a symbolic function used to extract variables. The positive and negative directions are used to ensure that the direction of speed adjustment matches the actual requirements. Maximum speed limit. and lower limit of speed The rotational speed ranges for safe operation and process feasibility of centrifugal casting equipment are defined. These ranges are typically preset based on factors such as the equipment's design limits, the physical properties of the material being processed, and the geometry of the casting. Optimizing the rotational speed is crucial. It is the ideal rotational speed obtained through calculation, used to guide the adjustment of centrifugal casting equipment.

[0061] The solution proposed in this application constructs a molding quality prediction index. A rotation speed control model was developed to achieve closed-loop optimization of centrifugal casting process parameters. The core of this model lies in utilizing the current rotation speed... As a benchmark, and in combination with the control intensity coefficient Molding quality prediction index And the variable representing the direction of deviation between the current speed and the optimal speed. and its symbolic function An adjustment factor is dynamically calculated. Specifically, when the molding quality prediction index... When the value deviates from the ideal value, the adjustment factor will be adjusted according to... The degree of deviation and The direction relative to the current rotational speed Perform a multiplicative correction. For example, if A lower value indicates poor molding quality. The term will increase, making the adjustment factor more effective. The corrective effect is enhanced, thereby prompting the rotational speed to be adjusted in a direction that is conducive to improving quality. Simultaneously, through... This ensures that the direction of speed adjustment is consistent with actual needs, avoiding blind or reverse adjustments. Furthermore, the calculated optimal speed... It will also be through functions and The function is subject to boundary constraints to ensure that it always stays within the preset lower speed limit. and the upper limit of speed This dynamic speed control mechanism, based on real-time quality feedback, effectively prevents equipment damage or process failure caused by excessively high or low speeds, ensuring the safety and stability of the production process. It enables the centrifugal casting process to self-optimize according to the actual molding state, significantly improving the process's response speed and adaptability.

[0062] In a preferred embodiment of the present invention, the method for calculating the optimized heating temperature in step S5 is as follows: .

[0063] in The set heating temperature reference value, This is the cooling rate compensation coefficient. , This is the real-time cooling rate index. The influence coefficient of rotational speed. , To optimize the speed index, This is the upper limit of the heating temperature. This is the lower limit of the heating temperature. To optimize the heating temperature.

[0064] in , The method for obtaining this value is as follows: substitute the real-time cooling rate and the optimized rotation speed into the maximum and minimum normalization formulas for calculation.

[0065] In this embodiment, the calculation method aims to dynamically and accurately determine the optimal heating temperature required for the mold during centrifugal casting through a mathematical model. Its core lies in comprehensively considering the real-time thermal state of the mold (cooling rate) and the influence of centrifugal rotation speed on the solidification process, thereby achieving refined control over the casting quality.

[0066] Among them, the set heating temperature reference value It is an initial or ideal heating temperature that is pre-set based on the type of alloy being cast, the geometry of the casting, the mold material, and the desired solidification characteristics. Its determination can be based on empirical data, process manuals, numerical simulations, or preliminary test results.

[0067] Cooling rate compensation coefficient The value is used to quantify the impact of real-time cooling rate on the mold heating temperature requirement. It is usually obtained through experimental calibration, numerical simulation or theoretical calculation based on the thermophysical properties of the material to ensure that appropriate compensating heat can be provided when the mold is actually cooled.

[0068] Real-time cooling rate index It reflects the actual rate of temperature drop of the mold during the casting process. The real-time cooling rate can be obtained by continuously monitoring the temperature by arranging temperature sensors on or inside the mold and calculating the rate of temperature change over time. Subsequently, the real-time cooling rate is converted into a dimensionless exponent by undergoing maximum-minimum normalization.

[0069] Speed ​​Influence Coefficient This coefficient characterizes the degree of influence of centrifugal speed variation on the mold heating temperature requirement. Centrifugal speed affects the filling behavior of molten metal, the advancement of the solidification front, and the stress distribution inside the casting. Therefore, this coefficient is needed to adjust the heating temperature to adapt to different speed conditions. Its value can be determined through experimental analysis, fluid dynamics simulation, or solidification process simulation.

[0070] Optimize speed index This is the optimized rotational speed calculated using a rotational speed control model based on the molding quality prediction index and the current rotational speed, after being normalized to maximum and minimum values. It represents the recommended centrifugal rotational speed under the current process conditions to achieve the best molding quality. (Upper limit of heating temperature) and lower limit value Maximum and minimum allowable limits for mold heating temperature are set. These limits are to prevent overheating of the mold, which could lead to deterioration of material properties, equipment damage, or defects in the casting, and to prevent insufficient filling or premature solidification of the molten metal due to excessively low temperatures. These upper and lower limits are typically determined based on the heat resistance of the mold material, the casting temperature range of the alloy, and the safe operating parameters of the equipment. Optimize heating temperature. The final target temperature, calculated using the above method, is used to adjust the mold heating device in real time. This temperature value directly instructs the heating system to adjust and maintain the mold in its optimal thermal state. The maximum-minimum normalization formula is a commonly used data preprocessing method to transform raw data of different dimensions or ranges into a unified interval, forming a dimensionless exponent. This process helps eliminate dimensional differences between different parameters, enabling effective calculation and comparison within a unified mathematical model, thus improving the model's stability and accuracy. Optimize rotational speed. When the temperature is lowered, the filling capacity of the molten metal decreases, and the mold temperature needs to be appropriately reduced to promote rapid solidification and prevent flow defects.

[0071] This calculation method achieves dynamic and precise control of the mold heating temperature through a comprehensive mathematical model. Its working principle is based on first using a preset heating temperature reference value... Based on this, the benchmark value represents the mold temperature required under ideal conditions. Building upon this, the model introduces a compensation mechanism for the real-time cooling rate, using a cooling rate compensation coefficient. and real-time cooling rate index product This dynamically adjusts the reference temperature. When the mold cools down rapidly, this parameter increases, thereby raising the target heating temperature to compensate for heat loss and ensure mold temperature stability. Simultaneously, considering the critical influence of centrifugal speed on the solidification process, the model also introduces a speed influence coefficient. and optimize speed index , through item Further temperature adjustments are made. This adjustment allows the heating temperature to adaptively adjust according to changes in the optimized rotation speed; for example, in some cases, increasing the optimized rotation speed may require a corresponding adjustment of the mold temperature to optimize solidification behavior. Finally, the entire calculation result will be processed... functions and The function is subject to boundary constraints to ensure optimal heating temperature. Always maintain the preset upper limit of heating temperature. and lower limit value This ensures the stability of the process and the safety of the equipment. The method for optimizing the heating temperature is closely integrated with the closed-loop control process of the entire centrifugal casting molding process. Among these, the real-time cooling rate index... This is derived from online monitoring of the actual thermal state of the mold, and the optimized rotation speed index... This output is from the speed control model in the preceding step S4, reflecting the linkage between process parameters. In this way, the proposed solution effectively integrates the real-time thermal state of the mold and the optimized centrifugal speed into the control of the heating temperature, forming a dynamically responsive and coordinated control mechanism. This makes the adjustment of the heating temperature no longer an isolated empirical judgment, but a precise calculation based on real-time feedback of multiple parameters, thereby significantly improving the stability of the entire centrifugal casting process and the forming quality of the casting, and ultimately helping the forming quality prediction index in step S6 reach the preset threshold.

[0072] In a preferred embodiment of the present invention, the maximum-minimum value formula is as follows: ,in These are the original input parameters. The lower limit value of the input parameter is set. The upper limit value of the input parameters is set. This is the output value.

[0073] In this embodiment, the output parameters This refers to the raw data or measured values ​​to be normalized. These parameters can be various real-time monitoring data from the centrifugal casting process used in the manufacturing of gaming chairs, such as centrifugal speed, casting temperature, filling time, mold heating temperature, melt density equivalent, mold vibration amplitude, eccentricity of the mold center relative to the main rotating shaft, solidification front advance rate ratio, local undercooling ratio, real-time cooling rate, and optimized speed. Its function is to incorporate actual physical quantities or measurement results into the normalization calculation. The set lower limit value of the input parameters... Indicates the corresponding output parameters The minimum expected or minimum permissible value. This lower limit is usually preset based on process experience, equipment performance limitations, or product quality requirements. For example, for centrifugal speed, a minimum safe speed or minimum effective speed can be set as... Regarding casting temperature, a minimum casting temperature can be set to ensure melt fluidity. The upper limit value of the set input parameters is also available. Indicates the corresponding output parameters The maximum expected or maximum permissible value. This upper limit is also preset based on process experience, equipment performance limitations, or product quality requirements. For example, for centrifugal speed, a maximum safe speed or maximum effective speed can be set as... For pouring temperature, a maximum pouring temperature can be set to avoid overheating or material degradation. Output value This refers to the standardized value obtained after processing with the maximum-minimum normalization formula. This output value is typically between 0 and 1, representing the original parameters. Within its preset range [ , The relative positions within the range are mapped. In this way, parameters with different dimensions and numerical ranges are uniformly mapped to a standardized interval, facilitating unified calculation and comparison in subsequent mathematical models.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A centrifugal casting process for processing an e-sports chair, characterized by, Includes the following steps: Step S1, Process Status Assessment: Collect centrifugal speed, pouring temperature, filling time and mold heating temperature, construct a process status assessment model, and output the process stability index; Step S2, Material-Equipment Condition Assessment: Collect melt density equivalent, mold vibration amplitude, and mold center eccentricity relative to the main rotation axis to construct a material-equipment condition assessment model and output the material consistency index; Step S3, Online evaluation of molding quality: Based on the process stability index, the material consistency index, the solidification front advance rate ratio, and the local undercooling ratio, an online evaluation model for molding quality is constructed, and the molding quality prediction index is output. Step S4, Speed ​​Control: Based on the molding quality prediction index and the current speed, construct a speed control model and output the optimized speed; Step S5, Heating Temperature Control: Based on the real-time cooling rate, the reference mold temperature, and the optimized rotation speed, a heating temperature control model is constructed, and the optimized heating temperature is output. Step S6: Adjust the process parameters of centrifugal casting according to the optimized rotation speed and the optimized heating temperature until the molding quality prediction index reaches the preset threshold.

2. The e-sports chair processing centrifugal casting forming process according to claim 1, characterized in that, In step S3, the solidification front advance rate ratio is obtained by embedding 2-3 thermocouple arrays at key positions in the mold cavity and collecting temperature curve inflection points to calculate the advance speed; the local undercooling ratio is obtained by comparing the solidification platform temperature recorded by thermocouples with the alloy liquidus temperature.

3. The e-sports chair processing centrifugal casting forming process according to claim 1, characterized in that, The process stability index is obtained through the following methods: The centrifugal speed index, casting temperature index, and mold heating temperature index were each subjected to S-shaped function transformation to obtain three S-shaped transformation values. The deviation of the filling time index from 1 is used as the filling time factor; Multiplying the three S-shaped transformation values ​​by the filling time factor yields the process stability index; Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

4. The e-sports chair processing centrifugal casting forming process according to claim 1, characterized in that, In step S2, the material consistency index is obtained in the following way: The material consistency index is obtained by multiplying the melt density equivalent index by the negative exponential decay term with the mold vibration amplitude index as the variable, and then by multiplying it by the negative exponential decay term with the eccentricity index as the variable. Each index is calculated by substituting the measured values ​​into the maximum-minimum normalization formula.

5. The e-sports chair processing centrifugal casting forming process according to claim 1, characterized in that, In step S3, the molding quality prediction index is obtained in the following way: The basic quality factor is obtained by multiplying the preset first influence coefficient power of the process stability index with the preset second influence coefficient power of the material consistency index. Multiply the square of (1 minus the solidification front advance rate ratio index) and the square of the local undercooling ratio index by the corresponding preset influence coefficients, and then divide by the sum of the two preset influence coefficients to obtain the deviation term. Multiply the basic quality factor by (1 minus the deviation term) to obtain the molding quality prediction index; in The local supercooling ratio index is obtained by substituting it into the maximum-minimum normalization formula.

6. The centrifugal casting molding process for processing gaming chairs according to claim 5, characterized in that, In step S4, the optimized rotational speed is obtained in the following way: Calculate the directional sign of the deviation between the current speed and the optimal speed; The adjustment range is obtained by multiplying (1 minus the molding quality prediction index) and the control strength coefficient. Multiply the current speed by (1 plus the product of the adjustment range and the direction sign) to obtain the preliminary optimized speed; The initial optimized speed is limited to between the preset lower and upper speed limits to obtain the optimized speed.

7. The centrifugal casting molding process for processing gaming chairs according to claim 1, characterized in that, In step S5, the optimized heating temperature is obtained through the following method: Calculate the product of the real-time cooling rate index and the preset cooling rate compensation coefficient, and then add 1 to obtain the cooling rate compensation factor. Calculate 1 minus (the product of the preset speed influence coefficient and (1 minus the optimized speed index)) to obtain the speed compensation factor; Multiply the set heating temperature baseline value by the cooling rate compensation factor and the rotation speed compensation factor to obtain the preliminary optimized temperature; The initial optimized temperature is limited between the preset lower and upper limits of the heating temperature to obtain the optimized heating temperature; The real-time cooling rate index and the optimized rotation speed index are calculated by substituting the real-time cooling rate and the optimized rotation speed into the maximum-minimum normalization formula, respectively.

8. The centrifugal casting molding process for processing gaming chairs according to any one of claims 2-7, characterized in that, The maximum-minimum normalization formula is: (input value minus the set lower limit) divided by (set upper limit minus the set lower limit) to obtain the normalized output value.

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