Temperature monitoring method and system for forging of forge piece

Abstract

The invention relates to the field of temperature monitoring, in particular to a temperature monitoring method and system for forge piece forging, and the method comprises the steps: obtaining temperature data at different moments when a forge piece is forged, constructing a temperature sequence through the temperature data at different moments, and calculating the isolation degree of each temperature data; the temperature sequence is classified to obtain a plurality of sub-temperature sequences, the change index of each sub-temperature sequence is calculated, and the change index represents the volatility of the sub-temperature sequences; and taking the data in the sub-temperature sequences of which the change indexes are greater than a preset index threshold as the temperature data of the oxide skin. According to the invention, the temperature data can be distinguished to obtain the temperature data of the oxide skin and the temperature data of the forge piece surface.

Description

Technical Field

[0001] The present invention relates to the field of temperature monitoring, and particularly to a temperature monitoring method and system for forging of forgings. Background Art

[0002] The temperature during the forging process directly affects the plasticity and fluidity of metal materials. If the temperature is too low, the material may become brittle, resulting in cracks during forging or failure to achieve the required shape; if the temperature is too high, it may cause excessive softening of the metal, affecting the forming quality. Precise control of the temperature helps to control the microstructure of the metal, ensuring that the mechanical properties and microstructure of the forgings meet the design requirements. Too high or too low temperature may lead to grain coarsening or other adverse structures of the material, affecting the final mechanical properties. Therefore, when analyzing the forging quality of forgings, it is necessary to refer to the temperature data during the forging process to improve the processing technology.

[0003] Zhang Yucun, A Temperature Detection Method for Hot Forgings with Oxide Scale Based on Infrared Spectroscopy, School of Electrical Engineering, Yanshan University, 2012. The paper proposed a special infrared temperature measurement method through laboratory research on hot workpieces wrapped with thick oxide scale. This method directly measures the temperature and emissivity of the oxide scale surface, and then deduces the true temperature of the surface of the forgings wrapped inside through the heat exchange situation between the oxide scale, the forgings, and the external environment. This method solves the problem of temperature measurement error caused by the interference of oxide scale covering for ordinary infrared thermometers, and finally verifies the feasibility of this method through experiments.

[0004] During the forging process of forgings, the temperature of the forgings is measured by a temperature measuring instrument. Since oxide scale is generated on the surface of the forgings during forging, and the temperature of the oxide scale is usually lower than the temperature of the forging surface. When measuring the temperature of the forging surface, if there is oxide scale on the forging surface, the measured temperature data at this time is the temperature data of the oxide scale, not the temperature of the forging surface. Therefore, part of the measured temperature data is the temperature data of the forging surface, and part is the temperature data of the oxide scale, which is not conducive to the analysis of the forging quality of the forgings. Summary of the Invention

[0005] In order to solve the problem that the measured temperature data contains the temperature data of the oxide scale and is not convenient for analyzing the forging quality, the present invention provides a temperature monitoring method and system for forging of forgings.

[0006] In a first aspect, the present invention provides a temperature monitoring method for forging of forgings, adopting the following technical solution: Obtain the temperature data at different moments during the forging of the forgings, construct a temperature sequence using the temperature data at different moments, calculate the absolute value of the difference between each temperature data in the temperature sequence and the adjacent temperature data, and calculate the isolation degree of each temperature data. The isolation degree is positively correlated with the absolute value of the difference. Classify the temperature sequence to obtain multiple sub - temperature sequences, calculate the change index of each sub - temperature sequence, and the change index represents the volatility of the sub - temperature sequence; use the data in the sub - temperature sequences with change indices greater than the preset index threshold as the temperature data of the scale.

[0007] Obtain temperature data by monitoring the temperature during forging of the forging, and then distinguish the temperature data of the scale and the temperature data of the forging surface, which is convenient for distinguishing the temperature data and improving the forging process.

[0008] Preferably, the expression of the isolation degree is:

[0009] In the formula, represents the isolation degree of the temperature data at the i - th moment, represents the temperature data at the i - th moment, represents the temperature data at the (i - 1) - th moment, represents the temperature data at the (i + 1) - th moment.

[0010] By calculating the isolation degree of the temperature data, the difference between the temperature data and the adjacent temperature data can be initially understood, providing a theoretical basis for judging the category to which the temperature data belongs.

[0011] Preferably, the method further includes calculating the change factor of each sub - temperature sequence. The calculation method is as follows: Fit the data points in the sub - temperature sequence to obtain a fitting curve, calculate the absolute value of the slope of each data point, and normalize the slope. Further calculate the mean value of the absolute value of the slope and the variance of the absolute value of the slope of the data points in the sub - temperature sequence; take the ratio of the product of the absolute value variance and the absolute value mean to the number of data points as the change factor of the sub - temperature sequence.

[0012] Based on the characteristic that the volatility of the temperature data of the scale is different from that of the forging surface temperature data, calculate the change factor from multiple dimensions, so that the change factor can reflect the volatility of the sub - temperature sequence and is convenient for distinguishing the sub - temperature sequence.

[0013] Preferably, the calculation method of the change index is: Calculate the isolation degree of each temperature data, sort the obtained multiple isolation degrees in descending order to obtain an isolation degree sequence, intercept the previous multiple data in the isolation degree sequence, and calculate the mean value of the intercepted data; the expression of the change index is:

[0014] In the formula, represents the change index of the n - th sub - temperature sequence, represents the preset weight coefficient, Represents the change factor of the nth sub-temperature sequence, Represents the mean value of the intercepted data in the isolation degree sequence.

[0015] By calculating the change index of the sub-temperature sequence, the difference between adjacent sub-temperature sequences can be understood, which is convenient for classifying the sub-temperature sequences.

[0016] Preferably, the least squares method is used to fit the data points in the sub-temperature sequence to obtain a fitting curve.

[0017] Preferably, the calculation method of the weight coefficient is: construct an objective function :

[0018] In the formula, , , respectively represent the change indexes of the (n - 1)th, nth, and (n + 1)th sub-temperature sequences. Solve the maximum value of the objective function to obtain the value of the weight coefficient.

[0019] Preferably, a swarm intelligence algorithm is used to solve the maximum value of the objective function.

[0020] By constructing an objective function to obtain the weight coefficient, the accuracy of the calculation result of the change index is improved, so that the change characteristics of the data points in the sub-temperature sequence can be accurately measured by the change index.

[0021] Preferably, the method further includes the step of converting the temperature data of the scale into the temperature data of the forging surface.

[0022] Preferably, the method for classifying the temperature sequence into multiple sub-temperature sequences is: using the optimal segmentation method to perform ordered sample clustering on the temperature sequence to obtain multiple clustering clusters, and taking the temperature data in each clustering cluster as a sub-temperature sequence.

[0023] In a second aspect, the present invention provides a temperature monitoring system for forging forgings, adopting the following technical solution: A temperature monitoring system for forging forgings, including: a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned temperature monitoring method for forging forgings is implemented.

[0024] Generate a computer program for the above-mentioned temperature monitoring method for forging forgings, and store it in the memory to be loaded and executed by the processor. Thus, a system is made according to the memory and the processor, which is convenient to use.

[0025] The present invention has the following technical effects: The present invention monitors the temperature during forging of forgings to obtain temperature data, and then differentiates the temperature data to obtain the temperature data of the scale and the temperature data of the forging surface, which is convenient for differentiating the temperature data and improving the forging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of a temperature monitoring method for forging of forgings according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] An embodiment of the present invention discloses a temperature monitoring method for forging of forgings. Referring to Figure 1 , the method includes the following steps, specifically as follows: S1: Obtain temperature data at different times during forging of the forging.

[0029] Use an infrared sensor to collect the temperature data of the forging during forging in real time. The sampling frequency is once every 2 seconds. The collected temperature data is the temperature data of the forging surface or the temperature data of the scale. Use the obtained temperature data at different times to construct a temperature sequence.

[0030] S2: Calculate the isolation degree of each temperature data in the temperature sequence.

[0031] The expression of the isolation degree is:

[0032] In the formula, represents the isolation degree of the temperature data at the i-th moment, represents the temperature data at the i-th moment, represents the temperature data at the (i - 1)-th moment, represents the temperature data at the (i + 1)-th moment.

[0033] Scale is a corrosion product formed by the oxidation of steel at high temperature during forging. It is usually attached and adhered to the surface of the forging. Its attachment time is mainly affected by the impact of the forging press. Therefore, its attachment time may be one sampling moment or multiple sampling moments.

[0034] Since the iron element reacts with oxygen instantaneously when the scale is generated, releasing heat, the temperature of the scale is higher than that of the forging surface. Then, the heat dissipation rate of the scale is greater than that of the forging surface, resulting in the temperature of the scale being lower than that of the forging surface subsequently. Therefore, during the forging process of the forging, if there is scale on the surface of the forging at the i-th moment and there is no scale on the surface of the forging at the (i - 1)-th and (i + 1)-th moments, and the temperature collected is the temperature of the forging surface, the isolation degree of the temperature data at the i-th moment is greater than that at the (i - 1)-th and (i + 1)-th moments. It can be understood that if the isolation degree at the i-th moment is greater than that at the (i - 1)-th and (i + 1)-th moments simultaneously, and the temperature data at the i-th moment is greater than the temperature data at the (i - 1)-th and (i + 1)-th moments, then the temperature data obtained at the i-th moment is the temperature data of the forging surface. At this time, the temperature data at the i-th moment is denoted as the marked temperature.

[0035] S3: Divide the temperature sequence into multiple sub-temperature sequences.

[0036] If the attachment time of the scale on the forging surface is relatively long, then multiple consecutive temperature data collected during the attachment period are all temperature data of the scale surface. Therefore, it is necessary to screen the temperature data of multiple scales in the temperature sequence. It should be noted that when the attachment time of the scale on the forging surface is relatively long, the temperature of the scale surface shows a trend of first rising briefly and then falling overall.

[0037] Calculate the mean values of the temperature data on the left and right sides of the marked temperature in the temperature sequence, and substitute the mean values into the temperature sequence instead of the marked temperature to reduce the influence of the marked temperature on dividing the sub-temperature sequence. Similarly, calculate the mean values of the temperature data on the left and right sides of the noise data points, and substitute the mean values into the temperature sequence instead of the marked temperature to reduce the influence of the noise data points on dividing the sub-temperature sequence. Use the optimal segmentation method to perform ordered sample clustering on the temperature sequence to obtain multiple clustering clusters, and use the temperature data within each clustering cluster as a sub-temperature sequence.

[0038] S4: Calculate the change factor of each sub-temperature sequence.

[0039] Use the least squares method to fit the data points in the sub-temperature sequence to obtain a fitting curve, calculate the absolute value of the slope of each data point, use the Min - Max normalization algorithm to normalize the slope, and further calculate the mean value of the absolute value of the slope and the variance of the absolute value of the slope of the data points in the sub-temperature sequence. Take the ratio of the product of the absolute value variance and the absolute value mean to the number of data points as the change factor of the sub-temperature sequence.

[0040] The expression of the change factor is:

[0041] In the formula, represents the change factor of the nth sub-temperature sequence represents the average absolute value of the slopes of the data points in the nth sub-temperature sequence represents the variance of the absolute values of the slopes of the data points in the nth sub-temperature sequence represents the number of data points in the nth sub-temperature sequence

[0042] During the forging process of the forging, both the forging and the scale are simultaneously exposed to the air. The temperature change rate of the forging is less than that of the scale. In other words, the speed is greater than the temperature drop rate of the forging. Therefore, a larger average absolute value of the slope can further reflect that the data in the corresponding sub-temperature sequence is the temperature data of the scale. On the contrary, a smaller average absolute value of the slope further reflects that the data in the corresponding sub-temperature sequence is the temperature data of the forging surface. At the same time, due to the small mass of the scale and fast heat dissipation, the temperature data of the scale has large fluctuations, that is, the variance of the absolute values of the slopes of the data points in the corresponding sub-sequence is large, while the temperature data of the forging surface has small fluctuations, and the variance of the absolute values of the slopes of the data points in the corresponding sub-sequence is small. When the forging is forged, the forging is impacted by external forces, making the time from the generation to the shedding of the scale shorter. Therefore, it also means that the length of the corresponding sub-sequence is shorter.

[0043] In summary, when the change factor of the sub-sequence is large, it can be preliminarily indicated that the data points in the corresponding sub-sequence are the temperature data of the scale, and vice versa, they are the temperature data of the forging surface.

[0044] S5: Calculate the change index of each sub-temperature sequence.

[0045] S51: For each sub-temperature sequence, calculate the isolation degree of each temperature data, normalize the isolation degree using the Min-Max normalization algorithm, sort the processed multiple isolation degrees in descending order to obtain the isolation degree sequence, intercept the previous multiple data in the isolation degree sequence, and calculate the average value of the intercepted data.

[0046] The expression of the change index is as follows

[0047] In the formula represents the change index of the nth sub-temperature sequence represents the preset weight coefficient, and its value range is (0, 1) represents the change factor of the nth sub-temperature sequence represents the average value of the intercepted data in the isolation degree sequence. Exemplarily, intercept the first 3 data in the isolation degree sequence and calculate the average value of the 3 data

[0048] During the forging process of forgings, the temperature data of the oxide scale shows a process of first rising briefly and then dropping rapidly. Therefore, if the data in the sub-temperature sequence is the temperature data of the oxide scale, the corresponding isolation degree sequence of the sub-temperature sequence shows that the first few data are relatively large as a whole, and the last few data are relatively small as a whole. The average value of the intercepted data is relatively large; if the data in the sub-temperature sequence is the temperature data of the forging surface, the corresponding isolation degree sequence of the sub-temperature sequence is relatively stable as a whole, and the average value of the intercepted data is relatively small. Therefore, when the change index is relatively large, it further indicates that the data points in the corresponding subsequence are the temperature data of the oxide scale. On the contrary, they are the temperature data of the forging surface.

[0049] S52: Calculate the weight coefficient.

[0050] Construct the objective function , and the expression is:[[]]

[0051] In the formula, , , represent the change indexes of the (n - 1)th, nth, and (n + 1)th sub-temperature sequences. Use the swarm intelligence algorithm to solve the maximum value of the objective function, and take the value of the weight coefficient when the objective function is at its maximum as the final value of the weight coefficient. The objective function represents the overall difference between the change indexes of the sub-temperature sequence and the adjacent sub-temperature sequences. Solving the maximum value of the objective function is to magnify the difference between the change indexes of the sub-temperature sequences, so as to easily distinguish the temperature data of the oxide scale from the temperature data of the forging surface.

[0052] Exemplarily, use the particle swarm optimization algorithm to solve the maximum value of the objective function. Among them, the number of particles is 50, the maximum number of iterations is 100, the inertia weight is 0.7, and the acceleration constant is 2.0.

[0053] S6: Convert the temperature data of the oxide scale to obtain the temperature data of the forging surface.

[0054] When the change index of the sub-temperature sequence is greater than the preset index threshold, it indicates that the volatility of the data points in the sub-temperature sequence is relatively large, which conforms to the change characteristics of the temperature data of the oxide scale. Therefore, take the data in the sub-temperature sequence with a change index greater than the preset index threshold as the temperature data of the oxide scale, and the rest are the temperature data of the forging surface. The index threshold is selected manually according to the actual situation. Exemplarily, the index threshold is 0.3.

[0055] After obtaining the temperature data of the oxide scale, convert the temperature data of the oxide scale to obtain the temperature data of the forging surface, which is used for the analysis of the forging quality and forging process of the forgings.

[0056] The expression is: , where represents the temperature data of the forging surface at the a-th moment, represents the temperature data of the scale at the a-th moment, represents the temperature conversion function. The temperature conversion function and the conversion of the temperature data of the scale to obtain the temperature data of the forging surface are prior arts. For details, see the documents cited in the background art. The calculation process will not be elaborated here.

[0057] An embodiment of the present invention also discloses a temperature monitoring system for forging forgings, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for monitoring the temperature of forging forgings according to the present invention is implemented.

[0058] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0059] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A temperature monitoring method for forging forgings, characterized in that, Including the steps of: Obtaining temperature data at different moments during forging of the forging, constructing a temperature sequence using the temperature data at different moments, calculating the absolute value of the difference between each temperature data in the temperature sequence and the adjacent temperature data, calculating the isolation degree of each temperature data, and the isolation degree is positively correlated with the absolute value of the difference; Classifying the temperature sequence to obtain multiple sub-temperature sequences, calculating the change index of each sub-temperature sequence, and the change index represents the volatility of the sub-temperature sequence; taking the data in the sub-temperature sequences with the change index greater than the preset index threshold as the temperature data of the scale.

2. The temperature monitoring method for forging of forgings according to claim 1, characterized in that, The expression of the isolation degree is: In the formula, represents the isolation degree of the temperature data at the i-th moment, represents the temperature data at the i-th moment, represents the temperature data at the (i - 1)-th moment, represents the temperature data at the (i + 1)-th moment.

3. A temperature monitoring method for forging of forgings according to claim 1, characterized in that, The method further includes calculating the change factor of each sub-temperature sequence. The calculation method is: fitting the data points in the sub-temperature sequence to obtain a fitting curve, calculating the absolute value of the slope of each data point, and normalizing the slope, and further calculating the mean value of the absolute value of the slope and the variance of the absolute value of the slope of the data points in the sub-temperature sequence; taking the ratio of the product of the absolute value variance and the absolute value mean to the number of data points as the change factor of the sub-temperature sequence.

4. A temperature monitoring method for forging of forgings according to claim 3, characterized in that, The calculation method of the change index is: calculating the isolation degree of each temperature data, sorting the obtained multiple isolation degrees in descending order to obtain an isolation degree sequence, intercepting the previous multiple data in the isolation degree sequence, and calculating the mean value of the intercepted data; the expression of the change index is: Wherein, represents the change index of the nth sub-temperature sequence, represents a preset weight coefficient, represents the change factor of the nth sub-temperature sequence, represents the mean value of the intercepted data in the isolation degree sequence.

5. A temperature monitoring method for forging of forgings according to claim 3, characterized in that, Using the least squares method to fit the data points in the sub-temperature sequence to obtain a fitting curve.

6. A temperature monitoring method for forging of forgings according to claim 4, characterized in that, The calculation method of the weight coefficient is as follows: construct an objective function : wherein , , respectively represent the change indices of the (n-1)-th, n-th, and (n+1)-th sub-temperature sequences, and by solving for the maximum value of the objective function, the value of the weight coefficient is obtained.

7. A temperature monitoring method for forging of forgings according to claim 6, characterized in that, Using a swarm intelligence algorithm to solve the maximum value of the objective function.

8. A temperature monitoring method for forging forgings according to claim 1, characterized in that, The method further includes the step of converting the temperature data of the scale to obtain the temperature data of the forging surface.

9. A temperature monitoring method for forging of forgings according to claim 1, characterized in that, The method for classifying the temperature sequence to obtain multiple sub-temperature sequences is: using the optimal segmentation method to perform ordered sample clustering on the temperature sequence to obtain multiple clustering clusters, and taking the temperature data within each clustering cluster as a sub-temperature sequence.

10. A temperature monitoring system for forging forgings, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a temperature monitoring method for forging a forging according to any one of claims 1-9 is implemented.

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