A method for identifying solidification characteristics of an ultra-thick large-section nodular cast iron part

Abstract

The present application belongs to the technical field of super-thick large-section nodular cast iron parts, in particular to a super-thick large-section nodular cast iron part solidification characteristic identification method, which collects thick large-section nodular cast iron part hot spot center temperature time series data for a long time at high frequency, designs a Gaussian function weight calculation method, determines the temperature Gaussian function weighting range ≥ 5~10s, calculates the instantaneous temperature change rate by central difference, and the Gaussian function weighted average comfortable temperature change rate in the range of ≥180‑300s, fits the solidification process as a temperature change rate platform in a long time, effectively eliminates the fluctuation noise generated by long-time collection of molten iron temperature in actual production, adopts the temperature change platform change characteristics, combines the temperature time series data projection mode, forms a super-thick large-section nodular cast iron key solidification characteristic judgment and identification method, and provides a solidification theoretical basis for the development of nodular cast iron cast iron material research and optimization of production process.

Description

Technical Field

[0001] This invention belongs to the technical field of ultra-thick large-section ductile iron castings, and particularly relates to a method for identifying the solidification characteristics of ultra-thick large-section ductile iron castings. Background Technology

[0002] Accurately identifying the solidification and eutectic characteristics of ductile iron castings during the solidification process, including the onset of solidification, the beginning of eutectic nucleation, and the start and end of a large amount of eutectic formation, is of great practical guiding significance for optimizing the composition, spheroidization inoculation and cooling processes of ductile iron castings, as well as judging the spheroidization rate and shrinkage porosity tendency.

[0003] Ultra-thick, large-section ductile iron generally refers to ductile iron parts with a main wall thickness ≥ 200mm. These parts are widely used in the manufacture of key components for important equipment in machine tools, nuclear power, wind power, injection molding machines, and other fields. The castings have long solidification times, are highly susceptible to factory environmental influences, and generate significant noise in the collected temperature data. In production practice, to obtain castings with good performance and quality, foundries employ composite composition formulations, spheroidizing inoculation, and enhanced cooling processes. Different composition formulations lead to variations in the solidification characteristics of ductile iron parts. Currently, there is no method for identifying the key solidification characteristics of thick, large-section ductile iron parts after the complete execution of spheroidizing inoculation and enhanced cooling processes. Summary of the Invention

[0004] The purpose of this invention is to address the need for solidification process feature identification of ultra-thick, large-section ductile iron castings in production practice. This is achieved by continuously collecting time-series data of the center temperature of the hot spot in ultra-thick, large-section ductile iron castings over a long period at high frequency. A Gaussian function weighting calculation method is designed, determining the temperature Gaussian function weighting range as ≥5~10s. The instantaneous temperature change rate is calculated using the center difference, and the comfortable temperature change rate is calculated using the Gaussian function weighted average over a range of ≥180-300s. This method fits the solidification process as a temperature change rate plateau over a long period, effectively eliminating the fluctuation noise generated by long-term iron temperature collection in actual production. By utilizing the characteristics of the temperature change plateau and combining it with the temperature time-series data projection mode, a method for judging and identifying key solidification features of ultra-thick, large-section ductile iron is formed, providing a solidification theoretical basis for research on ductile iron materials and optimization of production processes.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for identifying solidification characteristics of ultra-thick, large-section ductile iron castings includes the following steps: S1. This invention collects temperature time-series data for thick, large ductile iron castings. Due to their large heat capacity, large sand content, and long solidification time, this invention employs a dual-station, 8-channel temperature measurement device. A K-type thermocouple temperature measurement component is arranged at the center of the casting's hot spot or the final solidification position of an equivalent test block. The ADAM4118 thermocouple input module acquires data at a frequency of 1 time / second. The temperature time-series data includes the thermocouple module signal value, temperature value, and signal acquisition time. The thermocouple temperature measurement component includes a K-type thermocouple, a glass tube, and a magnetic bead. The functions and usage of the temperature time-series data acquisition and visualization device are as follows: Figure 1 As shown.

[0006] The diameter of the K-type thermocouple is 0.5-1.0 mm.

[0007] The quartz glass tube has a wall thickness of 1.5mm, an outer diameter of 8mm, and an inner diameter of 5mm. The magnetic bead has an outer diameter of 4.5mm and two inner holes, each with a diameter of 1.0mm.

[0008] The thermocouple positive and negative electrodes are welded together using a spot welding machine, resulting in a secure joint that contacts the bottom of the glass tube.

[0009] The glass tube is fixed in the mold, which can be made of resin sand, water glass sand or metal mold.

[0010] The ADAM4118 thermocouple input module uses the Modbus protocol, reads values ​​ranging from 0 to 65535, and records and stores the collected data in a database or text file.

[0011] The data acquisition device is activated before casting to record data. Data acquisition can be stopped once the temperature drops below 1000℃.

[0012] The temperature value is calculated using the signal value from the thermocouple input module. Let V be the digital signal value read at a certain moment (point i). i Converted to temperature value T i Then the temperature value T i =V i *1370 / 65535 (rounded to 2 decimal places), the time value at point i is Time. i The collected temperature time series data includes temperature values ​​T. i and time value Time i ; S2. Design a Gaussian function weighted average method, where the Gaussian function weight distribution function is... Where WindowSize is the weighted average window size, -WindowSize / 2 ≤ x ≤ WindowSize / 2, e is the base of the natural logarithm, and W(x) represents the Gaussian weight of the distance from the center point X. The formula for the Gaussian weighted moving average is: Where n = WindowSize / 2, y(i) is the temperature-weighted average result at time point i, and T (i+j) W is the collected value at time point i+j. (j) It uses Gaussian weights.

[0013] S3. Temperature is processed using a Gaussian weighted average method, with a WindowSize of 5-10, to obtain the processed temperature time series, where the initial temperature T... m This is the highest temperature after filling; thereafter, the temperature begins to decrease, entering a continuous cooling and solidification stage. (T) m With Time0 valued at 0, the time-temperature change sequence Dataset1 is obtained; S4. The instantaneous temperature change rate R (°C / s) is calculated using the central difference method. The formula is R. i = (T i+1 –T i-1 ) / (Time i+1 -Time i-1 ), where Ri represents the instantaneous rate of temperature change at time point i, and T i-1、 T i+1 These represent the temperature values ​​at two adjacent points before and after time point i, respectively. i-1 Time i+1 These represent the time values ​​of two adjacent points before and after time point i; S5. Apply the Gaussian function weighted average of the instantaneous temperature change rate R from step S4, with a window size of 180-300 and an actual time range of ≥180~300s for the weighted average, to obtain a set Dataset2 containing the temperature change rate. S6. Based on the characteristics of primary crystallization and eutectic latent heat release during the solidification process of thick-section ductile iron, calculate and identify the temperature change rate plateau in S5. The temperature change rate plateaus have equal R values. The equal R values ​​include value fluctuations within the time interval < WindowSize. Obtain the instantaneous temperature change rate plateau set Dataset3, which is sorted by time sequence. S7. Iterate through the different platforms in Dataset3, if the temperature change rate P i R>P i-1 R and P i R>P i+1 R, then P i The platform indicates the end of the primary crystallization process and transitions to the eutectic stage, marked by P. i For PPCE The number is PCE; then P i+1 The platform represents the stage from the initial nucleation of the eutectic to the mass nucleation, labeled P. i+1 For P EN If the number is EN, then P i+2 The platform represents a stage of extensive eutectic solidification, labeled P. i+2 For P EU Its designation is EU; S8. Extract the platform set Dataset4 from Dataset3, whose numbers range from 0 to PCE (marked in step 6), and query P sequentially by time. i Platforms with a D value in the top 3, i.e., P i Dmax1,P i Dmax2, P i Dmax3, marked P i The platform corresponding to Dmax2 is P. L , will P L Projecting the platform endpoint PLStartIndex onto the Dataset2 index column, the value corresponding to the same index is the liquid phase solidification start temperature T. L ; S9. In step S7, P... EN Platform Start Point P EN Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the eutectic nucleation initiation temperature T. EN ; S10. In step S7, P... EU Platform Start Point P EU Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the temperature T at which a large number of eutectic phases begin. EU ; S11. In step S7, P EU Platform endpoint P EU When EndIndex is projected onto the Dataset2 index column, the value corresponding to the same index is the end temperature T of a large number of eutectic phases. ES This indicates that solidification is complete.

[0014] The advantages of this invention are as follows: This invention adopts a high-reliability temperature acquisition and analysis module and a temperature acquisition method that acquires temperature time series data of thick-section ductile iron parts at a high frequency of 1 second / time. It uses Gaussian function weighted averaging to process instantaneous temperature change (central difference) data, and weights the temperature change rate of ultra-large section ductile iron into an instantaneous temperature change plateau sequence. Combined with the crystallization law of ductile iron, a systematic feature point identification method is formed, which overcomes the noise interference in the long-term temperature acquisition process of the solidification process of thick-section ductile iron and effectively identifies temperature points with key characteristics such as the start of solidification, eutectic nucleation temperature, the start of a large amount of eutectic, the end of a large amount of eutectic, and the complete end of solidification. Attached Figure Description

[0015] Figure 1 This is a structural diagram and a flowchart of the method for acquiring temperature timing data of ductile iron parts in this invention.

[0016] Figure 2 This is a process flow diagram of the solidification feature identification method for ultra-thick, large-section ductile iron castings in this invention.

[0017] Figure 3 This is a diagram showing the results of solidification characteristic point analysis of a 400mm thick casting using the method of this invention.

[0018] Figure 4 This is a diagram showing the results of solidification characteristic point analysis of a 200mm thick casting using the method of this invention. Detailed Implementation Example

[0019] A method for identifying solidification characteristics of ultra-thick, large-section ductile iron castings includes the following steps: S1. Collecting temperature time-series data of thick, large ductile iron castings. Due to their large heat capacity, large sand intake, and long solidification time, this invention employs a dual-station, 8-channel temperature measurement device. A K-type thermocouple temperature measurement component is arranged at the center of the casting's hot spot or the final solidification position of an equivalent test block. The ADAM4118 thermocouple input module acquires data at a frequency of 1 time / second. The temperature time-series data includes the thermocouple module signal value, temperature value, and signal acquisition time. The thermocouple temperature measurement component 2 includes a K-type thermocouple, a glass tube, and a magnetic bead. The functions and usage of the temperature time-series data acquisition and visualization device are as follows: Figure 1 As shown, the acquisition device 1 is a dual-station 8-channel temperature measurement device, including a thermocouple temperature measurement component 2 set at the center of the casting 4, and an ADAM4118 thermocouple input module connected to the thermocouple temperature measurement component through a thermocouple compensation wire 3.

[0020] The diameter of the K-type thermocouple is 0.5-1.0 mm.

[0021] The quartz glass tube has a wall thickness of 1.5mm, an outer diameter of 8mm, and an inner diameter of 5mm. The magnetic bead has an outer diameter of 4.5mm, double holes, and an inner diameter of 1.0mm.

[0022] The thermocouple positive and negative electrodes are welded together using a spot welding machine, resulting in a secure joint that contacts the bottom of the glass tube.

[0023] The glass tube is fixed in mold 5, which can be made of resin sand, water glass sand or metal mold.

[0024] The ADAM4118 thermocouple input module uses the Modbus protocol, reads values ​​ranging from 0 to 65535, and records and stores the collected data in a database or text file.

[0025] The data acquisition device is activated before casting to record data. Data acquisition can be stopped once the temperature drops below 1000℃.

[0026] The temperature value is calculated using the signal value from the thermocouple input module. Let V be the digital signal value read at a certain moment (point i). i Converted to temperature value T i Then the temperature value T i =V i *1370 / 65535 (rounded to 2 decimal places), the time value at point i is Time. i The collected temperature time series data includes temperature values ​​T. i and time value Time i ; S2. Design a Gaussian function weighted average method. The Gaussian weight distribution function is Where WindowSize is the weighted average window size, -WindowSize / 2 ≤ x ≤ WindowSize / 2, e is the base of the natural logarithm, and W(x) represents the Gaussian weight of the distance from the center point X. The formula for the Gaussian weighted moving average is: Where n = WindowSize / 2, y(i) is the temperature-weighted average result at time point i, and T (i+j) W is the collected value at time point i+j. (j) It uses Gaussian weights.

[0027] S3. Temperature is processed using a Gaussian weighted average method, with a WindowSize of 5-10, to obtain the processed temperature time series, where the initial temperature T... m This is the highest temperature after filling; thereafter, the temperature begins to decrease, entering a continuous cooling and solidification stage. (T) m With Time0 valued at 0, the time-temperature change sequence Dataset1 is obtained; S4. The instantaneous temperature change rate R (°C / s) is calculated using the central difference method. The formula is R. i = (T i+1 –T i-1 ) / (Time i+1 -Time i-1 ), where R i T represents the instantaneous rate of temperature change at time point i. i-1、 T i+1 These represent the temperature values ​​at two adjacent points before and after time point i, respectively. i-1 Time i+1 These represent the time values ​​of two adjacent points before and after time point i; S5. Apply the Gaussian function from step 4 to the instantaneous temperature change rate R, with a window size of 180-300 and an actual time range of ≥180~300s for the weighted average, to obtain a set containing the temperature change rate, Dataset2. S6. Based on the characteristics of primary crystallization and eutectic latent heat release during the solidification process of thick-section ductile iron, calculate and identify the temperature change rate plateau in S5. The temperature change rate plateaus have equal R values. The equal R values ​​include value fluctuations within the time interval < WindowSize. Obtain the instantaneous temperature change rate plateau set Dataset3, which is sorted by time sequence. S7. Iterate through the different platforms in Dataset3, if the temperature change rate P i R>P i-1 R and P i R>P i+1 R, then P i The platform indicates the end of the primary crystallization process and transitions to the eutectic stage, marked by P. i For P PCE The number is PCE; then P i+1 The platform represents the stage from the initial nucleation of the eutectic to the mass nucleation, labeled P. i+1 For P EN If the number is EN, then P i+2 The platform represents a stage of extensive eutectic solidification, labeled P. i+2 For P EU Its designation is EU; S8. Extract the platform set Dataset4 from Dataset3, whose numbers range from 0 to PCE (marked in step 6), and query P sequentially by time. i Platforms with a D value in the top 3, i.e., P iDmax1,P i Dmax2, P i Dmax3, marked P i The platform corresponding to Dmax1 is P. L , will P L Projecting the platform endpoint PLStartIndex onto the Dataset2 index column, the value corresponding to the same index is the liquid phase solidification start temperature T. L ; S9. In step S7, P... EN Platform Start Point P EN Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the eutectic nucleation initiation temperature T. EN ; S10. In step S7, P... EU Platform Start Point P EU Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the temperature T at which a large number of eutectic phases begin. EU ; S11. In step S7, P EU Platform endpoint P EU When EndIndex is projected onto the Dataset2 index column, the value corresponding to the same index is the end temperature T of a large number of eutectic phases. ES This indicates that solidification is complete.

[0028] Application Example 1 In production practice, temperature recording and analysis were conducted on the core of a 400mm thick ductile iron casting. The thermocouple wire diameter was 0.8mm, and the continuous acquisition time during the solidification period was over 6.5 hours. The method described in the invention was used for identification. The temperature Gaussian function weighted average window was ≥5~10s (taken as 5s, 7s, and 10s respectively), and the instantaneous temperature change rate Gaussian weighted average window was 180-300s (taken as 180s, 240s, and 300s respectively). The calculation results using the method and parameters showed good consistency. The identification results when the temperature Gaussian function weighted average window and the instantaneous temperature change rate Gaussian weighted average window were 5s and 240s respectively were: solidification start temperature T L The eutectic nucleation temperature T is approximately 1210℃. EN The solidification temperature was 1121℃, the initial temperature of a large amount of eutectic was about 1118℃, the end temperature of a large amount of eutectic was about 1071℃, the solidification range of the casting was 140℃, and the solidification time was 5 hours and 47 minutes. The results are shown in Table 1.

[0029] Table 1 Application Example 2 Temperature measurements and analyses were performed on the core of ductile iron castings with a main wall thickness of 200mm during production. The thermocouple wire diameter was 0.5mm, and continuous data acquisition during the solidification phase lasted for more than 6.5 hours. The method described in the invention was used for identification. The temperature Gaussian function weighted average window was ≥5~10s (taken as 5s, 7s, and 10s respectively), and the instantaneous temperature change rate Gaussian weighted average window was 180-300s (taken as 180s, 240s, and 300s respectively). The calculation results using the described method and parameters showed good consistency. The identification results when the temperature Gaussian function weighted average window and the instantaneous temperature change rate Gaussian weighted average window were 5s and 240s respectively were as follows: Identification result: Solidification start temperature T L The eutectic nucleation temperature T is approximately 1213℃. EN The temperature at which a large amount of eutectic begins is 1122℃, T. EU At approximately 1118℃, a large amount of eutectic activity ceases. ES The temperature was approximately 1078℃, the solidification range of the casting was 135℃, and the solidification time was 4 hours and 36 minutes. The identification results are shown in Table 2, which show good consistency.

[0030] Table 2 Temperature Gaussian function weighted average window Gaussian weighted average window of instantaneous temperature change rate <![CDATA[Solidification start temperature T L (°C)]]> <![CDATA[Eutectic start nucleation temperature T EN (°C)]]> <![CDATA[The eutectic start temperature T EU (°C)]]> <![CDATA[End of a large amount of eutectic T ES (°C)]]> 5s 180s 1211 1122 1118 1078 5s 240s 1213 1122 1118 1078 5s 300s 1214 1122 1118 1078 10s 180s 1211 1122 1118 1078 7s 300s 1213 1122 1118 1078

Claims

1. A method for identifying the solidification characteristics of ultra-thick, large-section ductile iron castings, characterized in that, Includes the following steps: S1. Collect temperature time-series data for thick ductile iron castings. Start the data acquisition device before casting and record data. Stop acquisition after the temperature drops below 1000℃. The completed temperature time-series data includes the temperature value T. i and time value Time i ; S2. Design a Gaussian function weighted average method, where the Gaussian function weight distribution function is... Where WindowSize is the weighted average window size, -WindowSize / 2 ≤ x ≤ WindowSize / 2, e is the base of the natural logarithm, and W(x) represents the Gaussian weight of the distance from the center point X; the formula for the Gaussian weighted moving average is: Where n = WindowSize / 2, y(i) is the temperature-weighted average result at time point i, and T (i+j) It is the temperature value collected at time point i+j, W (j) It uses Gaussian weights; S3. Temperature is processed using a Gaussian weighted average method, with a WindowSize of 5-10, to obtain the processed temperature time series, where the initial temperature T... m This is the highest temperature after filling; thereafter, the temperature begins to decrease, entering a continuous cooling and solidification stage. (T) m With Time0 valued at 0, the time-temperature change sequence Dataset1 is obtained; S4. The instantaneous temperature change rate R (°C / s) is calculated using the central difference method. The formula is R. i = (T i+1 –T i-1 ) / (Time i+1 -Time i-1 ), where R i T represents the instantaneous rate of temperature change at time point i. i-1、 T i+1 These represent the temperature values ​​at two adjacent points before and after time point i, respectively. i-1 Time i+1 These represent the time values ​​of two adjacent points before and after time point i; S5. Apply the Gaussian function weighted average of the instantaneous temperature change rate R from step S4, with a window size of 180-300 and an actual time range of ≥180~300s for the weighted average, to obtain a set Dataset2 containing the temperature change rate. S6. Based on the characteristics of primary crystallization and eutectic latent heat release during the solidification process of thick-section ductile iron, calculate and identify the temperature change rate plateau in S5. The temperature change rate plateaus have equal R values. The equal R values ​​include value fluctuations within the time interval < WindowSize. Obtain the instantaneous temperature change rate plateau set Dataset3, which is sorted by time sequence. S7. Iterate through the different platforms in Dataset3, if the temperature change rate P... i R>P i-1 R and P i R>P i+1 R, then P i The platform indicates the end of the primary crystallization process and transitions to the eutectic stage, marked by P. i For P PCE The number is PCE; then P i+1 The platform represents the stage from the initial nucleation of the eutectic to the mass nucleation, labeled P. i+1 For P EN If the number is EN, then P i+2 The platform represents a stage of extensive eutectic solidification, labeled P. i+2 For P EU Its designation is EU; S8. Extract the platform set Dataset4 from Dataset3, whose numbers range from 0 to PCE (marked in step 6), and query P sequentially by time. i Platforms with a D value in the top 3, i.e., P i Dmax1,P i Dmax2, P i Dmax3, marked P i The platform corresponding to Dmax2 is P. L , will P L Projecting the platform endpoint PLStartIndex onto the Dataset2 index column, the value corresponding to the same index is the liquid phase solidification start temperature T. L ; S9. In step S7, P... EN Platform Start Point P EN Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the eutectic nucleation initiation temperature T. EN ; S10. In step S7, P... EU Platform Start Point P EU Projecting StartIndex onto the Dataset2 index column, the value corresponding to the same index is the temperature T at which a large number of eutectic phases begin. EU ; S1 1. In step S7, P EU Platform endpoint P EU When EndIndex is projected onto the Dataset2 index column, the value corresponding to the same index is the end temperature T of a large number of eutectic phases. ES This indicates that solidification is complete.

2. The method for identifying solidification characteristics of ultra-thick, large-section ductile iron castings as described in claim 1, characterized in that: The acquisition device in step S1 is a dual-station 8-channel temperature measurement device, including a thermocouple temperature measurement component set in the center of the casting, and an ADAM4118 thermocouple input module connected to the thermocouple temperature measurement component through a thermocouple compensation wire.

3. The method for identifying solidification characteristics of ultra-thick, large-section ductile iron castings as described in claim 2, characterized in that: The thermocouple temperature measuring assembly includes a K-type thermocouple, a quartz glass tube, and a magnetic bead; the quartz glass tube is fixed in the mold, the K-type thermocouple has a diameter of 0.5-1.0 mm; the quartz glass tube has a wall thickness of 1.5 mm, an outer diameter of 8 mm, and an inner diameter of 5 mm; the magnetic bead has an outer diameter of 4.5 mm and two inner holes, each with a diameter of 1.0 mm.

4. The method for identifying solidification characteristics of ultra-thick, large-section ductile iron castings as described in claim 3, characterized in that: The ADAM4118 thermocouple input module acquires data at a frequency of 1 time per second. The temperature time-series data includes the thermocouple module signal value, the temperature value, and the signal acquisition time. The temperature value is calculated using the thermocouple input module signal value. Let's assume the digital signal V is read at a certain time i. i Converted to temperature value T i Then the temperature value T i =V i *1370 / 65535, the time value at point i is Time. i .

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