Casting processing parameter optimization method and system based on real-time data analysis

Abstract

The present invention discloses a casting processing parameter optimization method and system based on real-time data analysis, which relates to the field of computer control technology, including collecting casting surface temperature parameters and casting processing surface profile data; calculating the casting cooling rate according to the casting surface temperature parameters, and dividing the casting surface processing area based on the casting cooling rate; analyzing the casting processing surface profile data, calculating the casting surface roughness value, and comparing the casting surface roughness value with a preset roughness threshold; when the casting surface roughness value is greater than the preset roughness threshold, sending a processing compensation instruction to a CNC machine tool; based on the temperature difference between the fast and slow cooling areas, respectively setting the corresponding tool speed parameters to complete the casting surface finishing. The present invention accurately identifies the temperature gradient distribution characteristics of the casting surface, realizes the precise division of the fast and slow cooling areas, and adopts a multi-dimensional surface roughness evaluation system to achieve precise control of processing parameters.

Description

Technical Field

[0001] The invention relates to the technical field of computer control, in particular to a casting processing parameter optimization method and system based on real-time data analysis. Background Art

[0002] With the development of intelligent manufacturing and computer control technology, the field of casting processing has gradually shifted from traditional manual control to computer-controlled automated precision processing. Traditional casting processing methods mainly rely on general processing parameters (such as feed speed, cutting depth and tool speed) for rough processing and finishing. However, due to the temperature gradient and uneven hardening effect generated during the cooling process of the casting, it is difficult for traditional methods to optimize the processing according to the cooling rate of different areas, resulting in uneven surface roughness and hardness of the casting, affecting the consistency of processing quality. In addition, most traditional methods lack real-time temperature and contour data feedback, and often require trial cutting and debugging to gradually optimize processing parameters, resulting in low processing efficiency, unstable surface quality and other problems.

[0003] In the prior art, some computer-controlled processing methods have begun to use temperature sensors and contour measurement equipment to obtain the temperature and surface morphology data of castings to improve the accuracy and real-time performance of processing control. However, these technologies have certain limitations in the application of temperature data for cooling rate analysis and regional division, and usually fail to make full use of the dynamic data of cooling rate to set processing parameters in different areas. In addition, the existing roughness monitoring technology has a data feedback lag problem, resulting in the system being difficult to adjust the processing parameters in time even if the surface roughness is detected to exceed the preset standard, and it is impossible to perform differentiated processing in the fast cooling area and the slow cooling area. Therefore, the prior art still has shortcomings in cooling rate difference management, real-time feedback control and processing parameter optimization. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes a casting processing parameter optimization method and system based on real-time data analysis.

[0005] Therefore, the present invention provides a casting processing parameter optimization method based on real-time data analysis, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a casting processing parameter optimization method based on real-time data analysis, which comprises:

[0008] Collect casting surface temperature parameters and casting processing surface profile data;

[0009] Calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area;

[0010] Analyzing the casting surface profile data, calculating the casting surface roughness value, and comparing the casting surface roughness value with a preset roughness threshold;

[0011] When the surface roughness value of the casting is greater than the preset roughness threshold, a machining compensation instruction is sent to the CNC machine tool to adjust the tool feed speed and cutting depth;

[0012] Based on the temperature difference between the fast cooling area and the slow cooling area, corresponding tool speed parameters are set respectively to complete the surface finishing of the casting.

[0013] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis of the present invention, the calculation of the casting cooling rate includes:

[0014] Grouping casting surface temperature parameters continuously collected in the data buffer area according to time series;

[0015] For any temperature measurement point Calculate the temperature change rate ;

[0016] The temperature change rate is subjected to data smoothing to obtain a processed temperature change rate. ;

[0017] Calculate casting cooling rate based on temperature change rate after treatment ;

[0018] The cooling rate of the casting is compared with a preset cooling rate threshold. When the cooling rate is greater than the cooling rate threshold, the point is marked as a fast cooling area, otherwise it is marked as a slow cooling area.

[0019] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis of the present invention, the temperature change rate is calculated as shown in the following formula:

[0020] ,

[0021] in, for The temperature value at the moment, is the time interval, is the temperature compensation coefficient, and its calculation formula is:

[0022] ,

[0023] in, is the material characteristic coefficient, is the reference temperature;

[0024] The calculation of the temperature change rate after the treatment is shown in the following formula:

[0025] ,

[0026] in, is the weight coefficient, satisfying:

[0027] ,

[0028] in, is the data point number, is the filter window length, is the standard deviation parameter, For the window The temperature change rate value at each location;

[0029] The casting cooling rate is calculated as follows:

[0030] ,

[0031] in, For point The distance to the nearest border, is the standardized parameter, is the weight coefficient.

[0032] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis described in the present invention, wherein: the casting surface roughness value is obtained by weighted calculation based on the arithmetic mean deviation value, the maximum profile height value, the profile kurtosis value and the skewness value;

[0033] The calculation formula of the casting surface roughness value is as follows:

[0034] ,

[0035] Among them, the quality correction factor for:

[0036] ,

[0037] Where λ is the correction factor, is the data credibility evaluation value, calculated as follows:

[0038] ,

[0039] in, is the standard deviation of the roughness parameter, is the average value, is the arithmetic mean deviation, is the maximum profile height value, is the profile kurtosis value, is the skewness value, , , , are the weights of the arithmetic mean deviation value, the maximum contour height value, the contour kurtosis value, and the skewness value, respectively. , , , They are the standardized benchmark values ​​of the arithmetic mean deviation, maximum profile height, profile kurtosis, and skewness, respectively.

[0040] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis of the present invention, the calculation of the arithmetic mean deviation value includes calculating the absolute value of the vertical distance from all sampling points within each evaluation length to the baseline and obtaining the average value;

[0041] The maximum profile height value is calculated by finding the vertical distance from the highest point to the lowest point within the evaluation length;

[0042] The profile kurtosis value and the skewness value characterize the statistical characteristics of the casting surface micro-morphology.

[0043] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis of the present invention, the calculation of the profile kurtosis value is shown in the following formula:

[0044] ,

[0045] Among them, the sampling weight for:

[0046] ,

[0047] in, is the sampling adjustment coefficient, is the number of effective sampling points, is the arithmetic mean deviation;

[0048] The calculation of the skewness value is as follows:

[0049] ,

[0050] Among them, the neighborhood impact factor for:

[0051] ,

[0052] in, is the influence coefficient, is the local height change rate, is the maximum local height change rate, is the baseline fitting function in The value at the point, Discrete sampling points The filtered height value at the location.

[0053] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis of the present invention, when the surface roughness value of the casting is greater than the preset roughness threshold, an over-limit condition is determined;

[0054] The over-limit conditions include mild, moderate and severe, and each level corresponds to different compensation strategies and control strengths.

[0055] As a preferred solution of the casting processing parameter optimization method based on real-time data analysis described in the present invention, the compensation strategy and control strength include:

[0056] When the limit is slightly exceeded, the feed speed is only slightly reduced, and the feed speed is controlled between 95% and 90% of the original value to keep the cutting depth unchanged, and the compensation cycle is short, which is one-fourth of the standard processing time;

[0057] When moderate overrun occurs, adjust the feed speed and cutting depth. The feed speed is reduced to 80% to 90% of the original value, the cutting depth is reduced by 5% to 10%, and the compensation cycle is extended to one third of the standard processing time.

[0058] When the limit is seriously exceeded, the feed speed is significantly reduced to 70% to 80% of the original value, the cutting depth is reduced by 10% to 15%, and the compensation cycle is extended to one-half of the standard processing time.

[0059] In a second aspect, an embodiment of the present invention provides a casting processing parameter optimization system based on real-time data analysis, which includes:

[0060] Data acquisition module, used to collect casting surface temperature parameters and casting processing surface profile data;

[0061] A cooling division module, used for calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area;

[0062] A compensation module is used to analyze the casting surface profile data, calculate the casting surface roughness value, and compare the casting surface roughness value with a preset roughness threshold; when the casting surface roughness value is greater than the preset roughness threshold, send a machining compensation instruction to the CNC machine tool to adjust the tool feed speed and cutting depth;

[0063] The parameter setting module is used to set corresponding tool speed parameters based on the temperature difference between the fast cooling area and the slow cooling area to complete the surface finishing of the casting.

[0064] The beneficial effects of the present invention are: the temperature and surface profile data of the casting process are collected in real time through a high-precision sensor array, the cooling rate is accurately calculated and the area is divided in combination with an improved mathematical model, and the surface quality is comprehensively evaluated by a multi-parameter fusion method, and a complete closed-loop adaptive compensation control system is established. The significant advantages of this solution are: it can accurately identify the temperature gradient distribution characteristics of the casting surface and realize the accurate division of fast and slow cooling areas; through the improved cubic spline interpolation algorithm, the smooth transition of the regional boundary is guaranteed; the multi-dimensional surface roughness evaluation system is adopted, combined with the graded compensation strategy, to achieve precise control of the processing parameters; finally, through the differentiated tool speed parameter setting, the processing quality problem of different cooling areas is effectively solved, and the surface processing accuracy and consistency of the casting are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work; among them:

[0066] Figure 1 Flow chart of the casting processing parameter optimization method based on real-time data analysis. DETAILED DESCRIPTION

[0067] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0068] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0070] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0071] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0072] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0073] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a casting processing parameter optimization method based on real-time data analysis, including:

[0074] S1: collecting casting surface temperature parameters and casting processing surface profile data, obtaining the casting surface temperature parameters through an infrared sensor, and collecting the casting processing surface profile data using a laser scanner;

[0075] The casting surface temperature parameters are obtained by an infrared sensor array installed on the head of the processing equipment. The infrared sensor array includes 8 infrared temperature sensors arranged at equal intervals along the X-axis direction. The temperature measurement range of each infrared temperature sensor is 0-800°C and the temperature measurement accuracy is ±1°C.

[0076] The casting surface contour data is collected by a laser scanner fixed on the machine tool spindle. The scanning frequency of the laser scanner is 200Hz, the scanning accuracy is 0.01mm, and the scanning width is 100mm. The collected data of the infrared sensor array and the laser scanner are transmitted to the data acquisition module through the industrial bus. The sampling frequency of the data acquisition module is 1kHz. The data acquisition module digitally processes the casting surface temperature parameters and the casting surface contour data and stores them in the data buffer area. During the casting processing, the infrared sensor array and the laser scanner move synchronously with the machine tool spindle, keeping the measurement distance from the casting surface constant at 50mm.

[0077] S2: calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area;

[0078] The casting surface temperature parameters continuously collected in the data buffer area are grouped according to time series, with a time interval of 0.5 seconds for each group. , its temperature change rate The calculation can be expressed as follows:

[0079] ,

[0080] in, is the temperature value at time t, The time interval is 0.5 seconds, is the temperature compensation coefficient, and its calculation formula is:

[0081] ,

[0082] in, is the material characteristic coefficient, ranging from 0.1 to 0.3. is the reference temperature, set to 200°C;

[0083] In order to eliminate random fluctuations in the temperature data collection process, a weighted moving average filtering algorithm is used to smooth the temperature change rate. The calculation formula is:

[0084] ,

[0085] in, is the weight coefficient, satisfying:

[0086] ,

[0087] in, is the data point number, The filter window length is 5. The standard deviation parameter is 1.2. For the window The temperature change rate value at each location.

[0088] Furthermore, the cooling rate of the casting is calculated based on the temperature change rate after smoothing. :

[0089] ,

[0090] in, is the distance from point P(x,y) to the nearest boundary, Take 100mm as the standard parameter, The weight coefficient is 0.15.

[0091] The casting cooling rate Compared with the preset cooling rate threshold of 15℃ / s, when When the cooling rate is greater than the threshold, the point is marked as a fast cooling area, otherwise it is marked as a slow cooling area. At the junction of the two areas, a boundary point is formed, that is, if there are points of two different areas around the point, the point is marked as a boundary point, and its coordinates (xi, yi) are recorded. All the found boundary points are connected in sequence to form a boundary point set {(xi, yi)}.

[0092] For the determined regional boundary point set {(xi,yi)}, the improved cubic spline interpolation algorithm is used to generate the regional boundary line, and the interpolation function S(x) satisfies:

[0093] ,

[0094] Among them, the coefficient , , , Solve with the following constraints:

[0095] ,

[0096] ,

[0097] ,

[0098] ,

[0099] To further improve the smoothness of the boundary curve, the tension coefficient λ is introduced to correct the second-order derivative:

[0100] ,

[0101] Among them, the value range of λ is 0.1~0.3.

[0102] The regional information of the fast cooling area and the slow cooling area is stored in the data processing module in the form of a coordinate mapping table. Each grid point contains information such as position coordinates, temperature change rate, and area type identification. The grid spacing is set to 0.5 mm to ensure the accuracy of subsequent processing parameter adjustments.

[0103] It should be noted that the improved cubic spline interpolation algorithm first preprocesses the input boundary point data, sorts all boundary points from small to large according to the horizontal coordinate value, calculates the spacing value and the difference quotient value between adjacent points, and the difference quotient value is used to characterize the change trend between adjacent points; then an improved tridiagonal equation group is established, which is characterized by introducing a local tension control term, and the local tension control term adopts an adaptive calculation method based on exponential decay. The size of the tension coefficient decreases with the increase of the slope of the local curve, thereby providing better shape control capabilities in areas with larger curvature.

[0104] In the stage of solving the piecewise coefficients, the algorithm calculates the spline function coefficients of each interval and introduces a curvature constraint mechanism. By real-time monitoring the curvature value of the curve, when the curvature exceeds the preset threshold, the local tension coefficient is automatically adjusted for correction, effectively preventing the occurrence of overfitting. At the connection of the intervals, the local smoothing kernel function is used to perform weighted averaging on the coefficients to ensure the continuity of the high-order derivatives of the curve at the nodes and improve the overall smoothness.

[0105] In view of the special situation at the endpoints, the algorithm adopts an improved boundary condition processing method. By setting the second-order derivative value at the endpoint to half of the second-order derivative value of the adjacent point, a smooth transition of the curve at the endpoint is achieved. At the same time, the algorithm also establishes an iterative optimization mechanism. By calculating the fitting error and comparing it with the allowable error, when the error exceeds the allowable value, the tension coefficient is automatically adjusted and recalculated until the accuracy requirements are met or the maximum number of iterations is reached.

[0106] During the entire interpolation process, the algorithm achieves precise control of the local shape of the curve through a dynamically adjusted tension control system, and adopts a curvature-based constraint mechanism to ensure the smoothness and numerical stability of the curve. Through a multi-level optimization strategy, the algorithm's noise resistance and fitting accuracy are significantly improved, so that the generated regional boundary lines not only maintain good smoothness, but also accurately reflect the distribution characteristics of the original boundary points.

[0107] S3: analyzing the casting processing surface profile data, calculating the casting surface roughness value, and comparing the casting surface roughness value with a preset roughness threshold;

[0108] Firstly, the original contour data collected by the laser scanner were preprocessed, and the Gaussian filtering algorithm was used to eliminate random noise. The filter window size was set to 7 data points, and the standard deviation parameter was 1.5. The filtered contour data were divided into several evaluation lengths according to the ISO 4287 standard. Each evaluation length was 4 mm, and the overlap rate of adjacent evaluation lengths was 25% to ensure the continuity of the data. The contour data within each evaluation length were morphologically separated, and the least squares method was used to fit the baseline to remove the macroscopic shape deviation.

[0109] In the roughness parameter calculation stage, the arithmetic mean deviation value Ra is calculated first. The specific calculation method is: for all sampling points within each evaluation length, the absolute value of the vertical distance to the baseline is calculated, and the average value is obtained; at the same time, the maximum profile height value Rz is calculated by obtaining the vertical distance from the highest point to the lowest point within the evaluation length; as shown in the following formula:

[0110] ,

[0111] Among them, the dynamic evaluation factor for:

[0112] ,

[0113] in, The weight coefficient is 0.2. is the local curvature, is the maximum curvature value, is the height value after filtering, For evaluation length.

[0114] The calculation formula of the maximum profile height value Rz is:

[0115] ,

[0116] in, is the highest point height, is the lowest point depth, is the direction compensation coefficient:

[0117] ,

[0118] in, The compensation coefficient is 0.15. is the angle between the contour tangent and the horizontal plane.

[0119] In order to improve the comprehensiveness of roughness evaluation, the profile kurtosis value Rku and skewness value Rsk are also calculated to characterize the statistical characteristics of the surface micromorphology. As shown in the following formula:

[0120] ,

[0121] Among them, the sampling weight S(xi) is:

[0122] ,

[0123] in, The sampling adjustment coefficient is 0.25. is the number of effective sampling points, is the arithmetic mean deviation.

[0124] The calculation of skewness value Rsk takes into account the influence of neighborhood:

[0125] ,

[0126] Among them, the neighborhood influence factor M(xi) is:

[0127] ,

[0128] in, The influence coefficient is taken as 0.2. is the local height change rate, is the maximum local height change rate, is the baseline fitting function in The value at the point, Discrete sampling points The filtered height value at the location.

[0129] In the parameter combination evaluation stage, the comprehensive roughness evaluation index SRI (Surface Roughness Index) is established. This index comprehensively considers the influence of the four parameters Ra, Rz, Rku and Rsk, and is calculated by weighted summation. The weight coefficient is determined by the hierarchical analysis method, where the weight of Ra is 0.4, the weight of Rz is 0.3, the weight of Rku is 0.2, and the weight of Rsk is 0.1. At the same time, considering the different dimensions of different parameters, each parameter is standardized before calculation. As shown in the following formula:

[0130] ,

[0131] Among them, the quality correction factor Q(x) is:

[0132] ,

[0133] Among them, λ is the correction coefficient and takes 0.1, and D(x) is the data credibility evaluation value:

[0134] ,

[0135] in, is the standard deviation of the roughness parameter, is the average value, is the arithmetic mean deviation, is the maximum profile height value, is the profile kurtosis value, is the skewness value, , , , are the weights of the arithmetic mean deviation value, the maximum contour height value, the contour kurtosis value, and the skewness value, respectively. , , , They are the standardized benchmark values ​​of the arithmetic mean deviation, maximum profile height, profile kurtosis, and skewness, respectively.

[0136] Furthermore, for common functional surfaces, the threshold of the comprehensive roughness index SRI is set to 3.2; for key functional surfaces, the threshold is set to 1.6; for precision matching surfaces, the threshold is set to 0.8. When the SRI value of a certain area exceeds the corresponding threshold, the system automatically marks the area as a pending area and records the degree of excess.

[0137] S4: When the surface roughness value of the casting is greater than the preset roughness threshold, a machining compensation instruction is sent to the CNC machine tool to adjust the tool feed speed and cutting depth;

[0138] When the surface roughness value of the casting exceeds the preset roughness threshold, the system first conducts a quantitative assessment of the over-limit situation, and divides the degree of over-limit into three levels: mild, moderate and severe by calculating the deviation ratio between the actual roughness value and the threshold. Each level corresponds to a different compensation strategy and control intensity to ensure the pertinence and effectiveness of the compensation measures.

[0139] In the stage of calculating compensation parameters, the system comprehensively considers the influence of three dimensions: workpiece material properties, processing conditions and environmental factors. Material properties consider the hardness, yield strength, thermal conductivity and microstructure characteristics of the material; working conditions include the current cutting speed, feed rate, cutting depth, tool wear state and cooling method; environmental factors include the influence of ambient temperature, humidity and vibration. Based on these parameters, a compensation calculation model is constructed to determine the specific adjustment amount of feed speed and cutting depth.

[0140] For mild overruns, the system uses a mild compensation strategy: only a small reduction in feed rate is made, generally controlled between 95% and 90% of the original value, and the cutting depth is kept constant. The compensation cycle is short, about a quarter of the standard processing time, so that the compensation effect can be quickly evaluated and adjusted in time.

[0141] When a moderate overrun occurs, the system initiates a more powerful compensation measure: the feed rate and cutting depth are adjusted simultaneously. The feed rate is reduced to 80% to 90% of the original value, and the cutting depth is reduced by 5% to 10%. The compensation cycle is extended to one-third of the standard processing time, and the real-time monitoring mode is activated to continuously track the processing status.

[0142] In the face of serious over-limit situations, the system takes the most powerful compensation measures: the feed speed is greatly reduced to 70% to 80% of the original value, and the cutting depth is reduced by 10% to 15%. At the same time, the compensation cycle is extended to half of the standard processing time, and the enhanced monitoring mode is activated.

[0143] During the compensation process, the system continuously collects and analyzes processing data, including cutting force, vibration, temperature and other information, and evaluates the compensation effect through these real-time data. If the compensation effect is not ideal, the system will automatically adjust the compensation strategy, including changing the compensation parameters, adjusting the compensation cycle or switching the compensation mode.

[0144] To prevent over-compensation, the system sets upper and lower limits for parameter adjustment and change rate limits. The feed rate adjustment range does not exceed plus or minus 30% of the initial value, and the cutting depth adjustment range does not exceed plus or minus 20% of the initial value. At the same time, the parameter change range between two adjacent compensations is also strictly limited to ensure the stability of the machining process.

[0145] S5: Based on the temperature difference between the fast cooling area and the slow cooling area, corresponding tool speed parameters are set respectively to complete the surface finishing of the casting.

[0146] Furthermore, this embodiment also provides a casting processing parameter optimization system based on real-time data analysis, including:

[0147] Data acquisition module, used to collect casting surface temperature parameters and casting processing surface profile data;

[0148] A cooling division module, used for calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area;

[0149] A compensation module is used to analyze the casting surface profile data, calculate the casting surface roughness value, and compare the casting surface roughness value with a preset roughness threshold; when the casting surface roughness value is greater than the preset roughness threshold, send a machining compensation instruction to the CNC machine tool to adjust the tool feed speed and cutting depth;

[0150] The parameter setting module is used to set corresponding tool speed parameters based on the temperature difference between the fast cooling area and the slow cooling area to complete the surface finishing of the casting.

[0151] This embodiment also provides a computer device, which is suitable for the case of a casting processing parameter optimization method based on real-time data analysis, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the casting processing parameter optimization method based on real-time data analysis proposed in the above embodiment.

[0152] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0153] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing casting processing parameters based on real-time data analysis as proposed in the above embodiment is implemented.

[0154] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0155] Embodiment 2 is the second embodiment of the present invention. This embodiment provides a casting processing parameter optimization method based on real-time data analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0156] This test selected a ductile iron cylinder block as the test object, the size of the cylinder block is 800mm×600mm×400mm, and the material is QT450-10. The test was carried out on a five-axis CNC machining center DMG MORI DMU 80 eVo linear, with a maximum spindle speed of 24000r / min and a positioning accuracy of ±0.003mm.

[0157] The machine tool spindle is equipped with a thermoMETER CTL series infrared temperature sensor array from MICRO-EPSILON of Germany. The array consists of eight temperature sensors arranged at a pitch of 12.5 mm along the X-axis, covering a range of 100 mm. It is also equipped with a Keyence LJ-V7300 laser profiler, which uses blue laser technology and has higher measurement accuracy and anti-interference capabilities. The data acquisition system uses Siemens ET200SP distributed I / O modules, which have high-speed real-time data acquisition and processing capabilities.

[0158] During the test, the cylinder body was first rough-machined to remove about 2mm of the surface rough stock. Then it entered the finishing stage. Before the finishing began, the infrared temperature sensor array and laser profiler were started for real-time data acquisition. The initial processing parameters were set as follows: spindle speed 6000r / min, feed speed 2000mm / min, cutting depth 0.2mm. A φ16mm coated carbide tool was selected, and the tool model was SECO JS554.

[0159] To ensure the accuracy of the measurement data, a constant detection distance of 50 mm is maintained during the machining process, and the tool trajectory is compensated in real time through the machine tool control system to ensure that the sensor always maintains the best measurement angle with the workpiece surface. The data acquisition frequency is set to 1kHz, and the collected temperature data and contour data are digitally processed and stored in the data cache of the industrial computer.

[0160] Four groups of control experiments were designed, using the traditional fixed parameter processing method and the adaptive parameter adjustment method of the present invention respectively. Each group of experiments was repeated 3 times to ensure the reliability of the data. During the processing, the system calculates the cooling rate of the workpiece surface in real time, and divides the processing area into a fast cooling area and a slow cooling area according to the preset threshold of 15°C / second. For the fast cooling area, the system reduces the spindle speed to 4500r / min to reduce the generation of cutting heat; for the slow cooling area, the original spindle speed is maintained at 6000r / min. The specific experimental data are shown in Table 1 below:

[0161] Table 1 Experimental data table

[0162] Test groups Average surface roughness Ra(μm) Maximum profile height Rz(μm) Comprehensive roughness index SRI Maximum surface temperature (℃) Cooling rate (℃ / s) Processing efficiency improvement rate (%) Surface integrity scoring Traditional processing group-1 3.2 16.5 3.8 386 12.3 0 7.2 Traditional processing group-2 3.4 17.2 3.9 392 11.8 0 7.0 Traditional processing group-3 3.3 16.8 3.7 389 12.1 0 7.1 Adaptive Processing Group-1 1.8 9.2 1.9 325 15.6 15.3 8.8 Adaptive Processing Group-2 1.7 8.9 1.8 318 15.9 16.1 9.0 Adaptive Processing Group-3 1.9 9.4 2.0 322 15.7 15.8 8.9

[0163] By comparing the experimental data, the following conclusions can be drawn:

[0164] After adopting the adaptive machining method of the present invention, the surface roughness Ra value of the workpiece is significantly reduced from the average 3.3μm of traditional machining to about 1.8μm, an improvement of 45.5%. The maximum profile height Rz is also reduced from an average of 16.8μm to 9.2μm, a decrease of 45.2%. This shows that the method of the present invention can effectively improve the surface machining quality of the workpiece. The comprehensive roughness index SRI is reduced from 3.8 to 1.9, indicating that the overall surface quality is comprehensively improved.

[0165] The maximum surface temperature of the adaptive processing group was controlled at around 325°C, which was about 64°C lower than the 389°C of the traditional processing group, a decrease of 16.5%. More importantly, the cooling rate was effectively controlled, increasing from an average of 12.1°C / s to 15.7°C / s, an increase of 29.8%. This precise temperature control effectively prevented thermal deformation and residual stress on the workpiece surface.

[0166] Despite the adoption of a dynamic parameter adjustment strategy, the overall processing efficiency of the adaptive processing group has increased by an average of 15.7%. This is mainly due to the system's ability to accurately adjust processing parameters according to different cooling areas, avoiding over-cutting or under-cutting in traditional processing.

[0167] The surface integrity score of the adaptive processing group reached 8.9 points (out of 10 points), which is 25.4% higher than the 7.1 points of the traditional processing group. This comprehensive score takes into account multiple factors such as surface roughness, metallographic structure, residual stress, etc., which fully proves the superiority of the method of the present invention in ensuring processing quality.

[0168] The present invention realizes intelligent adjustment of processing parameters by real-time monitoring of surface temperature and profile data, which not only significantly improves processing quality but also improves processing efficiency. In particular, when processing fast cooling areas and slow cooling areas, the temperature control and surface quality problems that are difficult to take into account in traditional processing methods are effectively solved through differentiated parameter control strategies. The experimental data fully verifies the innovation and practical value of the present invention.

[0169] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A casting processing parameter optimization method based on real-time data analysis, characterized in that: include: Collect casting surface temperature parameters and casting processing surface profile data; Calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area; Analyzing the casting surface profile data, calculating the casting surface roughness value, and comparing the casting surface roughness value with a preset roughness threshold; When the surface roughness value of the casting is greater than the preset roughness threshold, a machining compensation instruction is sent to the CNC machine tool to adjust the tool feed speed and cutting depth; Based on the temperature difference between the fast cooling area and the slow cooling area, corresponding tool speed parameters are set respectively to complete the surface finishing of the casting; The calculation of the casting cooling rate includes: The casting surface temperature parameters continuously collected in the data buffer area are grouped according to time series; For any temperature measurement point Calculate the temperature change rate ; The temperature change rate is subjected to data smoothing to obtain a processed temperature change rate. ; Calculate casting cooling rate based on temperature change rate after treatment ; The cooling rate of the casting is compared with a preset cooling rate threshold. When the cooling rate is greater than the cooling rate threshold, the point is marked as a fast cooling area, otherwise it is marked as a slow cooling area; The temperature change rate is calculated as follows: ， in, for The temperature value at the moment, is the time interval, is the temperature compensation coefficient, and its calculation formula is: ， in, is the material characteristic coefficient, is the reference temperature; The calculation of the temperature change rate after the treatment is shown in the following formula: ， in, is the weight coefficient, satisfying: ， in, is the data point number, is the filter window length, is the standard deviation parameter, For the window The temperature change rate value at each location; The casting cooling rate is calculated as follows: ， in, For point The distance to the nearest border, is the standardized parameter, is the weight coefficient.

2. The casting processing parameter optimization method based on real-time data analysis according to claim 1, characterized in that: The casting surface roughness value is obtained by weighted calculation based on the arithmetic mean deviation value, the maximum profile height value, the profile kurtosis value and the skewness value; The calculation formula of the casting surface roughness value is as follows: ， Among them, the quality correction factor for: ， Where λ is the correction coefficient, is the data credibility evaluation value, calculated as follows: ， in, is the standard deviation of the roughness parameter, is the average value, is the arithmetic mean deviation, is the maximum profile height value, is the profile kurtosis value, is the skewness value, , , , are the weights of the arithmetic mean deviation value, the maximum contour height value, the contour kurtosis value, and the skewness value, respectively. , , , They are the standardized benchmark values ​​of the arithmetic mean deviation, maximum profile height, profile kurtosis, and skewness, respectively.

3. The casting processing parameter optimization method based on real-time data analysis according to claim 2, characterized in that: The calculation of the arithmetic mean deviation value includes calculating the absolute value of the vertical distance from all sampling points within each evaluation length to the baseline and finding the average value; The maximum profile height value is calculated by finding the vertical distance from the highest point to the lowest point within the evaluation length; The profile kurtosis value and the skewness value are statistical properties that characterize the microscopic morphology of the casting surface.

4. The casting processing parameter optimization method based on real-time data analysis according to claim 3, characterized in that: The calculation of the profile kurtosis value is shown in the following formula: ， Among them, the sampling weight for: ， in, is the sampling adjustment coefficient, is the number of effective sampling points, is the arithmetic mean deviation; The calculation of the skewness value is as follows: ， Among them, the neighborhood impact factor for: ， in, is the influence coefficient, is the local height change rate, is the maximum local height change rate, is the baseline fitting function in The value at the point, Discrete sampling points The filtered height value at the location.

5. The casting processing parameter optimization method based on real-time data analysis according to claim 4, characterized in that: When the surface roughness value of the casting is greater than the preset roughness threshold, determining an over-limit condition; The over-limit conditions include mild, moderate and severe, and each level corresponds to different compensation strategies and control strengths.

6. The casting processing parameter optimization method based on real-time data analysis according to claim 5, characterized in that: The compensation strategies and control strengths include: When the limit is slightly exceeded, the feed speed is only slightly reduced, and the feed speed is controlled between 95% and 90% of the original value to keep the cutting depth unchanged, and the compensation cycle is short, which is one-fourth of the standard processing time; When moderate overrun occurs, adjust the feed speed and cutting depth. The feed speed is reduced to 80% to 90% of the original value, the cutting depth is reduced by 5% to 10%, and the compensation cycle is extended to one third of the standard processing time. When the limit is seriously exceeded, the feed speed is significantly reduced to 70% to 80% of the original value, the cutting depth is reduced by 10% to 15%, and the compensation cycle is extended to one-half of the standard processing time.

7. A casting processing parameter optimization system based on real-time data analysis, based on the casting processing parameter optimization method based on real-time data analysis according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to collect casting surface temperature parameters and casting processing surface profile data; A cooling division module, used for calculating the casting cooling rate according to the casting surface temperature parameter, and dividing the casting surface processing area based on the casting cooling rate, dividing the casting surface into a fast cooling area and a slow cooling area; A compensation module is used to analyze the casting surface profile data, calculate the casting surface roughness value, and compare the casting surface roughness value with a preset roughness threshold; when the casting surface roughness value is greater than the preset roughness threshold, send a machining compensation instruction to the CNC machine tool to adjust the tool feed speed and cutting depth; The parameter setting module is used to set corresponding tool speed parameters based on the temperature difference between the fast cooling area and the slow cooling area to complete the surface finishing of the casting.

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