Conjoined cylinder cover sand core grinding method

Abstract

The invention relates to the technical field of sand core production, and discloses a conjoined cylinder cover sand core coping method which comprises the steps that parameters of redundant materials of a sand core are obtained according to an initial image, and the initial image is obtained by shooting the bottom face of the sand core based on a visual detection area; a grinding instruction is generated according to the parameters of the redundant materials of the sand core, and the grinding instruction is adjusted according to sensor data; the grinding instruction is used for enabling a mechanical arm to be matched with a sand core grinding device to grind redundant materials of the sand core; the sensor data type at least comprises sensing data of the mechanical arm and sensing data of the sand core grinding device; images of the bottom face of the sand core in the grinding process are obtained according to a preset time interval, and the grinding state of the sand core is determined according to the images. The problems that sand core high points exist at the bottom of the sand core, efficiency is low and precision is poor are solved.

Description

Technical Field

[0001] The present application belongs to the technical field of sand core production, and specifically relates to a method for grinding sand cores of a one-piece cylinder head. Background Art

[0002] Sand core is a material used to make cores in casting production, and is composed of casting sand, sand binder, etc. In the existing technology, the production of one-piece cylinder head sand core requires a series of steps. The chassis core and the upper cover core are manufactured separately and assembled after manufacturing. Among them, the bottom surface of the chassis core and the upper cover core is a flat structure.

[0003] In the production process of one-piece cylinder head sand cores, due to the core making machine and the mold, there is a tendency for excess sand core material to appear on the bottom surface of the chassis core and the upper cover core, i.e., the existence of high points in the sand cores. This will cause the geometric dimensions to be out of tolerance during the sand core assembly process, resulting in the generation of sand core scrap.

[0004] However, the sand core is in direct contact with the pallet, and the problem of the high points of the sand core cannot be solved directly by manual grinding. The existing solution is: manually lift the sand core through the KBK hoist to make the sand core separate from the transfer pallet; then, the worker uses the grinding wheel to grind the high points of the bottom surface of the chassis core and the upper cover core; after the grinding is completed, the worker uses the KBK hoist to lift the sand core back to the transfer pallet; finally, the pallet needs to be released manually.

[0005] The inventors found that the above method of manually hoisting the sand core for grinding has low grinding efficiency, which affects the production line rhythm; the manual grinding of the sand core has low precision and is easy to damage the sand core, causing quality fluctuations. Summary of the invention

[0006] The present application provides a method for grinding a sand core of a one-piece cylinder head, so as to solve the problems of high points of the sand core at the bottom of the sand core, low efficiency and poor precision.

[0007] The technical solution adopted in this application is:

[0008] A method for grinding a sand core of a one-piece cylinder head is applied to a system including a mechanical arm and a sand core grinding device, the method comprising:

[0009] Obtaining parameters of the excess material of the sand core according to an initial image, wherein the initial image is obtained by photographing the bottom surface of the sand core based on a visual inspection area;

[0010] Generate a grinding instruction according to the parameters of the excess material of the sand core, and adjust the grinding instruction according to the sensor data; the grinding instruction is used to make the robot arm cooperate with the sand core grinding device to grind the excess material of the sand core; the sensor data type at least includes the sensor data of the robot arm and the sensor data of the sand core grinding device;

[0011] The image of the bottom surface of the sand core during the grinding process is acquired at preset time intervals, and the grinding state of the sand core is determined according to the image.

[0012] According to an embodiment of the present application, the parameters of the excess material of the sand core are obtained based on the initial image, and the initial image is obtained by photographing the bottom surface of the sand core based on the visual inspection area, specifically:

[0013] Use an industrial camera to photograph the bottom surface of the sand core in the visual inspection area and pre-process the original image, including grayscale conversion, noise removal, and image enhancement;

[0014] Use image segmentation technology to separate the bottom surface of the sand core from the background; use edge detection algorithm to extract the boundary information of the excess material on the bottom surface of the sand core; use region growing and watershed transformation methods to identify the specific area of ​​excess material;

[0015] Calculate the geometric parameters of the excess material to assess its size and distribution; analyze the physical properties of the excess material such as density and color differences to distinguish different types of excess material;

[0016] Output the relevant parameters of excess materials, including location coordinates, size, and quantity information.

[0017] According to an embodiment of the present application, the generating of the grinding instruction according to the parameters of the excess material of the sand core is specifically:

[0018] Use computer vision technology to identify excess material on the sand core, including location, shape, and size information;

[0019] Obtain the precise location and shape of excess material based on image processing algorithms;

[0020] According to the location information of the excess materials, plan the movement trajectory of the robot arm to ensure that all the excess materials are covered;

[0021] Considering the structural characteristics and grinding requirements of the sand core, the grinding path is designed;

[0022] Set the grinding wheel pressure and speed parameters according to the properties of the excess material;

[0023] Convert the grinding path and parameters into an instruction sequence executable by the robot arm; the instruction sequence includes the movement sequence, speed, acceleration of the robot arm and the working status information of the grinding wheel;

[0024] According to an embodiment of the present application, adjusting the grinding instruction according to the sensor data is specifically:

[0025] Collect data from the force sensor and position sensor installed on the robot arm; and obtain real-time feedback from the current sensor and temperature sensor on the sand core grinding device;

[0026] Use data fusion technology to integrate data from different sensors; analyze sensor data to check if any abnormalities occur;

[0027] Dynamically adjust grinding instructions based on sensor data, including adjusting the speed and pressure of the grinding wheel;

[0028] When an abnormality is detected, the grinding path is modified or grinding is suspended in time.

[0029] According to one embodiment of the present application, the sensor data of the robotic arm includes a position sensor and a torque sensor of the robotic arm, which are used to monitor the position and motion state of the robotic arm;

[0030] The torque sensor is used to detect the force applied to the robot arm during the grinding process to prevent excessive torque from causing damage to the equipment or excessive grinding.

[0031] According to one embodiment of the present application, the sensor data of the sand core grinding device includes a current sensor and a temperature sensor, which are used to monitor the operating state of the grinding wheel;

[0032] The current sensor determines whether the grinding wheel is working properly and the contact condition between the grinding wheel and the sand core;

[0033] The temperature sensor detects the temperature change of the grinding wheel during the grinding process to prevent the grinding effect from being affected by overheating.

[0034] According to an embodiment of the present application, the image of the bottom surface of the sand core during the grinding process is acquired at a preset time interval, and the grinding state of the sand core is determined according to the image, specifically:

[0035] The camera is triggered to capture images of the bottom surface of the sand core at intervals of 10 seconds; based on feature changes in the image, the removal of excess material on the bottom surface of the sand core is evaluated, and whether there are areas that have not been cleaned or newly generated excess material is checked; and whether the surface of the sand core has achieved the expected smoothness and finish;

[0036] According to the analysis result of the image, the grinding instruction is dynamically adjusted, including adjusting the grinding wheel rotation speed, grinding pressure and grinding path.

[0037] A computer program product containing instructions, when running on a device, enables the device to execute the steps in a method for sand core grinding of a one-piece cylinder head.

[0038] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in a method for grinding sand cores of a one-piece cylinder head.

[0039] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the sand core grinding method for a one-piece cylinder head are implemented.

[0040] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows:

[0041] This application realizes intelligent control of the sand core grinding process by combining visual detection and sensor data. First, based on the initial image obtained by visual detection, the system can accurately identify and quantify the specific parameters of the excess material on the bottom surface of the sand core, such as position, shape and size, which provides a data basis for subsequent precise grinding. Then, the system generates preliminary grinding instructions based on these parameters, and in the actual grinding process, through real-time collection of sensor data from the robot arm and the grinding device, dynamically adjusts the grinding instructions, ensuring the accuracy and adaptability of the grinding action, and effectively avoiding the overcutting or undercutting problems that may exist in traditional grinding methods.

[0042] In addition, the solution mentioned that the images of the grinding process are obtained at preset time intervals, and the current grinding status of the sand core is evaluated accordingly, which not only realizes the visual management of the grinding process, but also enables the system to detect and correct any deviation from the predetermined target in the first place, ensuring the quality consistency of the final product. Therefore, this technical solution significantly improves the automation level and process accuracy of sand core grinding, reduces production costs, and improves production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0044] Figure 1 A flow chart of a method for sand core grinding of a one-piece cylinder head provided in this embodiment. DETAILED DESCRIPTION

[0045] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features in each embodiment may be combined with each other without conflict.

[0047] In the present application, unless otherwise clearly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0048] Example 1

[0049] like Figure 1 As shown, a method for sand core grinding of a one-piece cylinder head is applied to a system including a robotic arm and a sand core grinding device, and the method comprises:

[0050] The parameters of the excess material of the sand core are obtained according to the initial image, and the initial image is obtained by photographing the bottom surface of the sand core based on the visual inspection area.

[0051] Specifically, image acquisition: Use industrial cameras or other imaging devices to photograph the bottom surface of the sand core in the visual inspection area to obtain the initial image. These cameras usually have high resolution to ensure the clarity of the image, so as to better identify the details of the bottom surface of the sand core.

[0052] Image preprocessing: Preprocess the captured original image, including denoising, contrast enhancement, grayscale conversion and other steps to improve the image quality. Preprocessing helps subsequent algorithms to extract useful information more accurately.

[0053] Feature extraction:

[0054] Use image processing algorithms to extract the features of excess material on the bottom surface of the sand core. Common algorithms include edge detection, threshold segmentation, morphological operations, etc. For example, Canny edge detection can help find the outline of excess material, threshold segmentation can distinguish between material and background, and morphological operations (such as opening and closing operations) can further remove noise and highlight the shape of the material.

[0055] Using the extracted images, pattern recognition techniques (such as template matching and feature point matching) are used to identify the specific location of the excess material. Machine learning or deep learning methods, such as convolutional neural networks (CNNs), may also be used to further improve the accuracy of recognition.

[0056] Quantification of excess material parameters:

[0057] Dimension measurement: Use the pixel information in the image, combined with the camera's calibration parameters, to calculate the dimensions of the excess material, such as length, width, etc. It may also be necessary to consider the image's scaling to convert to actual size units.

[0058] Position determination: Based on the coordinate system in the image, the position information of the excess material relative to the sand core is determined. This position information is crucial for planning the grinding path of the robot arm.

[0059] Shape analysis: Analyze the shape characteristics of the excess material, such as area, perimeter, convex hull and other geometric properties. This information helps to understand the complexity of the excess material and thus determine the difficulty and time required for grinding.

[0060] Specifically, this process can be divided into:

[0061] Use an industrial camera to photograph the bottom surface of the sand core in the visual inspection area to obtain the original image.

[0062] The original image is preprocessed by grayscale conversion, noise removal, image enhancement, etc.

[0063] Image segmentation technology is used to separate the bottom surface of the sand core from the background.

[0064] The edge detection algorithm is used to extract the boundary information of the excess material on the bottom surface of the sand core.

[0065] Region growing and watershed transform methods were used to identify specific areas of excess material.

[0066] Calculate the geometric parameters of the excess material and evaluate the size and distribution of the excess material.

[0067] Analyze the physical properties of excess materials such as density and color differences to distinguish different types of excess materials.

[0068] Output the relevant parameters of excess materials, including location coordinates, size, and quantity information.

[0069] For example, image acquisition

[0070] Use an industrial camera: Take a picture of the bottom surface of the sand core in the visual inspection area to obtain an initial image. Industrial cameras usually have high resolution to ensure image clarity, so as to better identify the details of the bottom surface of the sand core.

[0071] Image preprocessing

[0072] Preprocessing steps:

[0073] Grayscale conversion: Convert color images to grayscale images to simplify subsequent processing.

[0074] Noise Removal: Use filters (such as median filtering or Gaussian filtering) to remove noise from the image.

[0075] Image enhancement: Enhance image contrast through techniques such as histogram equalization or contrast stretching.

[0076] Excess material identification

[0077] Feature extraction

[0078] Image segmentation: Use image segmentation technology to separate the bottom surface of the sand core from the background and highlight the bottom surface area of ​​the sand core.

[0079] Edge detection: Apply edge detection algorithms (such as Canny edge detection) to extract boundary information of excess material on the bottom surface of the sand core.

[0080] Region Growing / Watershed Transform: Use region growing or watershed transform methods to identify specific areas of excess material.

[0081] Object Recognition

[0082] Feature extraction: After feature extraction, use pattern recognition technology (such as template matching and feature point matching) to identify the specific location of excess materials.

[0083] Machine Learning / Deep Learning: Machine learning or deep learning methods, such as convolutional neural networks (CNNs), may also be used to further improve recognition accuracy.

[0084] Quantification of excess material parameters

[0085] Dimension measurement: Use the pixel information in the image and the camera’s calibration parameters to calculate the dimensions of the excess material, such as length, width, etc.

[0086] Position determination: Determine the position information of the excess material relative to the sand core based on the coordinate system in the image.

[0087] Shape analysis: Analyze the shape characteristics of excess materials, such as area, perimeter, convex hull and other geometric properties.

[0088] Physical property analysis: Analyze the physical properties of excess materials such as density and color differences to distinguish different types of excess materials.

[0089] Output redundant material parameters

[0090] Output parameters: Output relevant parameters of excess materials, including location coordinates, size, quantity information, etc.

[0091] Specific Examples

[0092] Assume that there is an initial image of the bottom surface of the sand core, and there are some redundant materials in the image (for example, some raised parts of the bottom surface of the sand core).

[0093] Image acquisition:

[0094] The bottom surface of the sand core was photographed using an industrial camera to obtain an image with a resolution of 1024x768 pixels.

[0095] Image preprocessing:

[0096] Convert the image to grayscale.

[0097] Use median filtering to remove noise from the image.

[0098] Enhance image contrast through histogram equalization.

[0099] Feature extraction:

[0100] Image segmentation technology is used to separate the bottom surface of the sand core from the background.

[0101] The Canny edge detection algorithm is used to extract the boundary information of the redundant materials.

[0102] Use region growing techniques to identify specific areas of excess material.

[0103] Object Recognition:

[0104] The template matching technology is used to identify the specific location of the excess material through the image after feature extraction.

[0105] Convolutional neural network (CNN) is used to further improve the recognition accuracy.

[0106] Quantification of excess material parameters:

[0107] Measure the dimensions of the excess material, such as 10 mm in length and 5 mm in width.

[0108] Determine the location of the excess material, such as the coordinates in the image (x=200, y=300).

[0109] Analyze the shape characteristics of the excess material, such as an area of ​​30 square millimeters.

[0110] Analyze excess material for color differences, such as being darker than surrounding areas.

[0111] Output excess material parameters:

[0112] Output the location coordinates, size, quantity information, etc. of the excess material, such as the location coordinates (x=200, y=300), the size is 10mm x 5mm, and the quantity is 1.

[0113] Furthermore, image acquisition and preprocessing can also be

[0114] Multi-angle shooting: In addition to shooting from a fixed angle, multiple cameras can be added to shoot the bottom surface of the sand core from different angles at the same time to obtain more comprehensive information.

[0115] 3D reconstruction: Use structured light or laser scanning technology, combined with multi-view images, to generate a 3D model of the bottom surface of the sand core, providing more three-dimensional redundant material information.

[0116] Furthermore, feature extraction and recognition can be performed

[0117] Deep learning model: Use deep learning models (such as convolutional neural networks, CNN) for feature extraction and classification to improve the accuracy of redundant material identification.

[0118] Feature fusion: Combine multiple feature extraction algorithms (such as SIFT, SURF, HOG, etc.) to comprehensively analyze the texture, color and shape characteristics of redundant materials.

[0119] Furthermore, it is possible to quantify redundant material parameters

[0120] Advanced geometric analysis: Introduce more complex geometric analysis tools (such as curvature, gradient direction histogram, etc.) to more finely describe the shape characteristics of excess materials.

[0121] Statistical analysis: Conduct statistical analysis on the quantity and distribution density of excess materials to assess the complexity of the overall grinding work.

[0122] Furthermore, grinding instructions can be generated

[0123] Intelligent path planning: Based on the location and shape information of the excess material, use advanced path planning algorithms (such as A* search, Dijkstra algorithm, etc.) to plan the optimal grinding path.

[0124] Multi-stage grinding strategy: According to the height and hardness of the excess material, a multi-stage grinding strategy is formulated, first rough grinding and then fine grinding, to improve the grinding efficiency and quality.

[0125] Furthermore, sensor data fusion can be

[0126] Multi-sensor fusion: In addition to the sensors of the robotic arm and grinding device, vibration sensors, acoustic wave sensors, etc. can also be added to monitor the grinding status in multiple dimensions.

[0127] Intelligent feedback control: Using machine learning technology, the changing trend of the grinding process is predicted based on historical data, and the grinding parameters are adjusted in advance.

[0128] Furthermore, the grinding instructions can be adjusted dynamically.

[0129] Real-time correction: The grinding instructions are corrected in real time according to the sensor data to ensure that the best grinding conditions are always maintained during the grinding process.

[0130] Anomaly detection: Develop a special anomaly detection algorithm to promptly detect and handle abnormal situations that may occur during the grinding process.

[0131] Furthermore, the grinding status can be evaluated

[0132] Multi-frequency monitoring: During the grinding process, images are acquired at a higher frequency (e.g. once every 5 seconds) to evaluate the grinding status in real time.

[0133] Intelligent judgment: Introduce artificial intelligence technology to automatically judge whether the grinding has achieved the expected effect, and automatically adjust the grinding strategy when necessary.

[0134] A grinding instruction is generated according to the parameters of the excess material of the sand core, and the grinding instruction is adjusted according to the sensor data; the grinding instruction is used to enable the robot arm to cooperate with the sand core grinding device to grind the excess material of the sand core; the sensor data type at least includes the sensor data of the robot arm and the sensor data of the sand core grinding device.

[0135] Specifically, the grinding instruction generates

[0136] Computer vision recognition: Use computer vision technology to identify excess material on the sand core, including location, shape, size and other information.

[0137] Precise location and shape: The precise location and shape of excess material can be obtained based on image processing algorithms.

[0138] Robot arm motion trajectory planning: According to the location information of excess materials, the robot arm's motion trajectory is planned to ensure that the robot arm covers all excess material areas.

[0139] Grinding path design: Consider the structural characteristics of the sand core and the grinding requirements, and design the grinding path.

[0140] Grinding parameter setting: Set the grinding parameters such as pressure and speed of the grinding wheel according to the properties of the excess material.

[0141] Instruction sequence conversion: The grinding path and parameters are converted into an instruction sequence executable by the robot, including the movement sequence, speed, acceleration of the robot, and the working status information of the grinding wheel.

[0142] Sensor data adjustment grinding instructions

[0143] Data collection: Data is collected based on the force sensor and position sensor equipment on the robot arm; at the same time, real-time feedback is obtained based on the current sensor and temperature sensor on the sand core grinding device.

[0144] Data fusion: Use data fusion techniques to combine data from different sensors.

[0145] Data analysis: Analyze sensor data to check if any abnormalities occur.

[0146] Dynamic adjustment: Dynamically adjust the grinding instructions according to the sensor data, including adjusting the speed and pressure of the grinding wheel.

[0147] Abnormal handling: When an abnormality is detected, modify the grinding path or suspend grinding in time.

[0148] The whole process ensures the accuracy and adaptability of the grinding action, effectively avoiding the overcutting or undercutting problems existing in traditional grinding methods. The intelligent control of the sand core grinding process is achieved through the combination of visual inspection and sensor data.

[0149] For example, to generate grinding instructions

[0150] Identify excess material:

[0151] Use computer vision technology, such as image processing algorithms, to identify excess material on the sand core. This information includes the location, shape, size, etc. of the excess material.

[0152] Get the exact position and shape:

[0153] The image processing algorithm is used to obtain the precise location and shape of the excess material. For example, if the excess material is a protrusion, its center coordinates, height, diameter, and other information need to be known.

[0154] Planning the robot arm motion trajectory:

[0155] According to the location information of the excess material, the robot arm's motion trajectory is planned to ensure that the grinding wheel on the robot arm can cover all the excess material. For example, if the excess material is distributed in several different locations of the sand core, then the path of the robot arm to reach these locations in sequence needs to be planned.

[0156] Design grinding path:

[0157] Considering the structural characteristics of the sand core and the grinding requirements, the grinding path is designed. This step involves how the grinding wheel contacts the excess material and how to move to achieve the best grinding effect.

[0158] Set grinding parameters:

[0159] Set the grinding parameters such as pressure and speed of the grinding wheel according to the properties of the excess material. For example, if the excess material is hard, greater pressure and lower speed may be required.

[0160] Converted into a sequence of instructions:

[0161] The grinding path and parameters are converted into an executable instruction sequence for the robot, which includes the movement sequence, speed, acceleration of the robot, and the working status information of the grinding wheel.

[0162] Adjust grinding instructions based on sensor data

[0163] Collect sensor data:

[0164] Collect data from the force sensor and position sensor on the robot arm, as well as the current sensor and temperature sensor on the sand core grinding device. These data can provide real-time feedback on the state of the robot arm and the working condition of the grinding wheel during the grinding process.

[0165] Data Fusion:

[0166] Use data fusion technology to combine data from different sensors to gain a comprehensive understanding of the grinding process.

[0167] Analyzing sensor data:

[0168] Analyze the sensor data to check if any abnormal conditions occur. For example, if the current sensor shows a sudden increase in current, it may be that the grinding wheel hits a hard object; if the temperature sensor shows that the temperature is too high, it may be that the grinding wheel and the sand core generate too much heat due to friction.

[0169] Dynamic adjustment of grinding instructions:

[0170] Dynamically adjust grinding instructions based on sensor data, including adjusting parameters such as grinding wheel speed and pressure. For example, if the grinding wheel encounters increased resistance, it may be necessary to slow down the grinding wheel speed and increase the pressure.

[0171] Abnormal situation handling:

[0172] When an abnormality is detected, the grinding path should be modified or grinding should be suspended in time. For example, if the temperature is detected to be too high, the grinding wheel should be stopped immediately to avoid damage to the equipment or affect the grinding effect due to overheating.

[0173] Example

[0174] Assume that there is excess material on the bottom of the sand core, located at (X=100, Y=200), and the size is a cylindrical protrusion with a diameter of 20 mm. The process of generating a grinding instruction is as follows:

[0175] Identify excess material:

[0176] The image processing algorithm identified that there was a circular protrusion at the coordinates (X=100, Y=200) on the bottom surface of the sand core, with a diameter of about 20 mm.

[0177] Get the exact position and shape:

[0178] Through further image processing, the height of the excess material was accurately measured to be 3 mm.

[0179] Planning the robot arm motion trajectory:

[0180] Plan the robot arm to move from the current position to the coordinates (X=100, Y=200) and prepare to start grinding.

[0181] Design grinding path:

[0182] The grinding wheel is designed to rotate along the edge of the excess material to gradually reduce the height of the excess material until it is flat.

[0183] Set grinding parameters:

[0184] The grinding wheel was set to rotate at 60 rpm and the applied pressure was 5 Newtons.

[0185] Converted into a sequence of instructions:

[0186] Generate a command sequence: first move to (X=100, Y=200), then start the grinding wheel, rotate it at 60 rpm, and apply a pressure of 5 Newtons.

[0187] Next, suppose that during the grinding process, the sensor detects that the resistance encountered by the grinding wheel is greater than expected, and the current sensor shows that the current is out of the normal range.

[0188] Collect sensor data:

[0189] The collected data from the robot position sensor show that the robot has reached the specified position, and the current sensor data show that the current exceeds the normal range.

[0190] Data Fusion:

[0191] Integrate these data to analyze the current status of the robotic arm.

[0192] Analyzing sensor data:

[0193] It was found that the current was beyond the normal range, and it was speculated that the grinding wheel encountered greater resistance.

[0194] Dynamic adjustment of grinding instructions:

[0195] Dynamically adjust the grinding instructions, reduce the grinding wheel speed to 50 revolutions per minute, increase the pressure to 6 Newtons, and continue grinding.

[0196] Abnormal situation handling:

[0197] If the current continues to rise and exceeds the preset safety threshold, the grinding is suspended, the hardness of the excess material is re-evaluated, and the grinding strategy is adjusted if necessary.

[0198] In this way, the system can intelligently generate and adjust grinding instructions to ensure that excess material in the sand core is effectively ground away.

[0199] Furthermore, intelligent grinding path planning can be performed: based on the generated grinding instructions, the grinding path of the robot arm can be further optimized according to the geometric parameters of the excess material of the sand core (such as position, shape, size) and the structural characteristics of the sand core to ensure that the grinding path is both efficient and will not damage the sand core. For example, a path planning algorithm (such as the A* algorithm) can be used to find the optimal path.

[0200] Furthermore, a multi-level grinding strategy can be developed: according to the hardness, location and shape of the excess material, a multi-level grinding strategy can be developed. For example, coarse grinding is used to remove most of the excess material, and then fine grinding is used to achieve the final smoothness requirement.

[0201] Furthermore, real-time feedback control can be achieved: closed-loop control is achieved using sensor data. When it is detected that the contact force between the sand core and the grinding wheel is too large, the grinding force can be adjusted or the grinding path can be changed in real time to prevent damage to the sand core.

[0202] The image of the bottom surface of the sand core during the grinding process is acquired at preset time intervals, and the grinding state of the sand core is determined according to the image.

[0203] Specifically, image acquisition

[0204] Timed shooting: The industrial camera or other imaging device is triggered to capture images of the bottom surface of the sand core at a preset time interval (e.g. every 10 seconds). This timed shooting ensures continuous monitoring during the grinding process.

[0205] Image storage: The captured images will be stored, usually with a timestamp, so that the grinding status at each point in time can be traced back for subsequent analysis.

[0206] Image preprocessing

[0207] Image correction: Correct the acquired image to ensure image clarity and contrast to facilitate subsequent feature extraction.

[0208] Image segmentation: Use image segmentation technology to separate the bottom surface of the sand core from the background, highlighting the features of the bottom surface of the sand core for further analysis.

[0209] Feature extraction

[0210] Edge detection: Use edge detection algorithms (such as Canny edge detection) to identify the outline of excess material on the bottom of the sand core.

[0211] Morphological operations: Apply morphological operations (such as opening and closing) to remove noise and highlight the shape of redundant materials.

[0212] Region Growing: Use a region growing algorithm to identify specific areas of excess material.

[0213] Feature analysis: Analyze the geometric parameters of excess material, such as position, size, shape, etc., as well as physical properties, such as color, density, etc.

[0214] Grinding condition assessment

[0215] Grinding progress: Evaluate the removal of excess material from the bottom of the core based on feature changes in the image, and check for areas that have not been ground clean or new excess material.

[0216] Surface quality: Determine whether the surface of the sand core has achieved the expected smoothness and finish.

[0217] Anomaly detection: Identify anomalies that may occur during the grinding process, such as over-grinding or under-grinding.

[0218] Dynamic adjustment of grinding instructions

[0219] Adjust grinding parameters: Dynamically adjust grinding parameters such as grinding wheel speed, grinding pressure and grinding path according to image analysis results.

[0220] Grinding path optimization: If it is found that some areas are not ground enough, the grinding frequency of the area can be increased or the grinding time can be extended; if the risk of over-grinding is found, the grinding intensity can be reduced or the grinding method can be changed.

[0221] Timely correction: Once a deviation from the predetermined target is detected, the grinding instructions are adjusted in a timely manner to ensure the quality consistency of the final product.

[0222] Practical Examples

[0223] Timed shooting: Assume that an image of the bottom surface of the sand core is taken every 10 seconds.

[0224] Image analysis: After each shot, an image processing algorithm is used to analyze the state of excess material on the bottom surface of the sand core.

[0225] Adjust grinding instructions: According to the analysis results, dynamically adjust the grinding instructions to ensure the accuracy and adaptability of the grinding process.

[0226] Continuous monitoring: During the entire grinding process, the condition of the bottom surface of the sand core is continuously monitored to ensure that the grinding effect meets the expected standards.

[0227] Through the above steps, real-time monitoring can be achieved during the grinding process, and the grinding instructions can be dynamically adjusted according to the image analysis results, thereby ensuring the efficiency and quality consistency of the grinding process. This method not only realizes the visual management of the grinding process, but also can detect and correct any deviation from the predetermined target in the first place, thus ensuring the quality of the final product.

[0228] For example, image acquisition

[0229] Time-lapse photography:

[0230] Set the camera's timing shooting mechanism, for example, to take an image of the bottom surface of the sand core every 10 seconds.

[0231] The camera is installed above the sand core grinding device to ensure that the entire sand core bottom surface is covered.

[0232] Image Storage:

[0233] Each image captured is saved in a specified folder and is timestamped for identification.

[0234] Image Processing

[0235] Image preprocessing:

[0236] Each captured image is preprocessed, including but not limited to denoising, contrast enhancement, grayscale conversion and other steps, to improve image quality.

[0237] Image segmentation technology is used to separate the bottom surface of the sand core from the background to facilitate subsequent feature extraction.

[0238] Feature extraction:

[0239] Apply an edge detection algorithm (such as Canny edge detection) to identify the outline of excess material on the bottom surface of the sand core.

[0240] Use threshold segmentation technique to distinguish between material and background.

[0241] Perform morphological operations (such as opening and closing) to remove noise and highlight the shape of the material.

[0242] Excess material identification:

[0243] Through the image after feature extraction, pattern recognition technology (such as template matching, feature point matching) is used to identify the specific location of the excess material.

[0244] Use machine learning or deep learning methods (such as convolutional neural networks, CNN) to improve recognition accuracy.

[0245] Status Assessment

[0246] Excess material assessment:

[0247] Calculate the geometric parameters of excess material, such as area, perimeter, etc., to assess the size and distribution of excess material.

[0248] Analyze the physical properties of the excess material, such as color and density, to distinguish between different types of excess material.

[0249] Output the relevant parameters of excess materials, including location coordinates, size, and quantity information.

[0250] Grinding status judgment:

[0251] Evaluate the removal of excess material from the bottom of the core based on feature changes in the image.

[0252] Check for areas that were not ground cleanly or new excess material.

[0253] Determine whether the surface of the sand core has achieved the expected smoothness and finish.

[0254] Dynamic adjustment of grinding instructions

[0255] Grinding instruction adjustment:

[0256] According to the image analysis results, the grinding instructions are dynamically adjusted, such as adjusting the grinding wheel speed, grinding pressure and grinding path.

[0257] If you find that some areas are not ground enough, you can increase the number of grinding times in that area or extend the grinding time.

[0258] If you find a risk of over-grinding, you can reduce the grinding intensity or change the grinding method.

[0259] Real-time monitoring and alarm:

[0260] Monitor image changes during the grinding process in real time, and issue an alarm immediately if any abnormality is detected (such as cracks in the grinding area, uneven grinding, etc.).

[0261] Provides a visual monitoring interface to display the grinding status and key parameters, facilitating timely intervention by operators.

[0262] Example

[0263] Assume that during the grinding process, a camera is triggered every 10 seconds to capture an image of the bottom surface of the sand core. Based on the changes in the features in these images, the removal of excess material on the bottom surface of the sand core can be evaluated, and whether there are areas that have not been ground clean or newly generated excess material can be checked to determine whether the surface of the sand core has achieved the expected smoothness and finish. If it is found that a certain area is not ground enough, the grinding instructions will be adjusted to increase the number of grinding times or extend the grinding time of the area. If it is found that the grinding is excessive, the grinding force will be adjusted or the grinding method will be changed. In this way, the efficiency and quality consistency of the grinding process can be ensured.

[0264] Practical steps

[0265] To set a timer shot:

[0266] The camera was set to capture an image of the bottom surface of the sand core every 10 seconds.

[0267] Image preprocessing:

[0268] Preprocess the captured images, including grayscale conversion, noise removal, image enhancement, etc.

[0269] Feature extraction and recognition:

[0270] Image segmentation technology is used to separate the bottom surface of the sand core from the background.

[0271] The edge detection algorithm is used to extract the boundary information of the excess material on the bottom surface of the sand core.

[0272] Region growing and watershed transform methods were used to identify specific areas of excess material.

[0273] Quantification of excess material parameters:

[0274] Calculate the geometric parameters of the excess material and evaluate the size and distribution of the excess material.

[0275] Analyze the physical properties of excess materials such as density and color differences to distinguish different types of excess materials.

[0276] Output excess material parameters:

[0277] Including location coordinates, size, and quantity information.

[0278] Dynamic adjustment of grinding instructions:

[0279] According to the image analysis results, the grinding instructions are dynamically adjusted to ensure the accuracy and adaptability of the grinding action.

[0280] Real-time monitoring and alarm:

[0281] Monitor image changes during the grinding process. Once an abnormality is found, immediately adjust the grinding instructions or pause the grinding.

[0282] Furthermore, real-time image analysis and feedback are also available:

[0283] Establish a real-time image analysis system that can automatically detect the removal of excess material from the bottom of the sand core and dynamically adjust the grinding instructions based on the analysis results. For example, if the image shows that excess material in a certain area has not been completely removed, the system can adjust the robot arm's grinding path or increase the number of grinding times.

[0284] Furthermore, the image quality can be improved:

[0285] More advanced image processing algorithms, such as image enhancement techniques (e.g. local contrast enhancement, histogram equalization, etc.), are introduced to improve image quality and ensure that changes in the sand core surface can be accurately captured under any lighting conditions.

[0286] Furthermore, the application of machine learning models can also be:

[0287] Use deep learning models (such as convolutional neural networks, CNN) to identify excess material on the surface of sand cores, improve the accuracy of excess material identification, and further optimize the grinding effect through model training.

[0288] According to an embodiment of the present application, the parameters of the excess material of the sand core are obtained based on the initial image, and the initial image is obtained by photographing the bottom surface of the sand core based on the visual inspection area, specifically:

[0289] Use an industrial camera to photograph the bottom surface of the sand core in the visual inspection area and pre-process the original image, including grayscale conversion, noise removal, and image enhancement;

[0290] Use image segmentation technology to separate the bottom surface of the sand core from the background; use edge detection algorithm to extract the boundary information of the excess material on the bottom surface of the sand core; use region growing and watershed transformation methods to identify the specific area of ​​excess material;

[0291] Calculate the geometric parameters of the excess material to assess its size and distribution; analyze the physical properties of the excess material such as density and color differences to distinguish different types of excess material;

[0292] Output the relevant parameters of excess materials, including location coordinates, size, and quantity information.

[0293] Specifically, image acquisition

[0294] First, an industrial camera is used to capture the bottom surface of the sand core in the visual inspection area to obtain an initial image. The industrial camera here is usually high-resolution, which can ensure clear images to better identify the details of the bottom surface of the sand core.

[0295] Image preprocessing

[0296] Next, the acquired original image is preprocessed to improve the image quality and lay a good foundation for subsequent feature extraction. The preprocessing steps include:

[0297] Grayscale conversion: Convert color images to grayscale images to simplify the image processing process.

[0298] Noise removal: Remove random noise in the image through filters (such as median filtering, Gaussian filtering, etc.).

[0299] Image Enhancement: Enhances image contrast to highlight details on the underside of the core, making it easier to identify excess material during subsequent processing.

[0300] Image segmentation and feature extraction

[0301] Image Segmentation

[0302] Use image segmentation technology to separate the bottom surface of the sand core from the background. Common segmentation methods are:

[0303] Threshold segmentation: The image is divided into foreground (bottom of sand core) and background by setting the threshold.

[0304] Edge detection: such as Canny edge detection, used to identify the edge of the bottom surface of the sand core.

[0305] Edge Detection

[0306] Apply edge detection algorithms (such as Canny edge detection) to extract the boundary information of the excess material on the bottom surface of the sand core. Edge detection can help find the outline of the excess material and provide a basis for further processing.

[0307] Region Identification

[0308] Use region growing and watershed transform methods to identify specific areas of excess material. These methods can help determine which areas are excess material and which areas are normal core bottom surfaces.

[0309] Parameter Quantization

[0310] Calculation of geometric parameters

[0311] Calculate the geometric parameters of the excess material, such as area, perimeter, center coordinates, etc., to assess the size and distribution of the excess material. This information helps plan the grinding path of the robot arm.

[0312] Physical property analysis

[0313] Analyze the physical characteristics of the excess material, such as density, color difference, etc., to distinguish different types of excess material. This step helps to identify different types of excess material and provide a basis for subsequent processing.

[0314] Parameter output

[0315] Finally, the relevant parameters of the excess materials are output, including but not limited to:

[0316] Position coordinates: The coordinate position of the excess material in the image.

[0317] Size: Geometric dimensions such as area and length of excess material.

[0318] Quantity information: quantity statistics of excess materials.

[0319] Through the above steps, the parameters of the excess material in the sand core can be accurately obtained from the initial image taken in the visual inspection area, providing necessary information support for the subsequent generation of grinding instructions.

[0320] According to an embodiment of the present application, the generating of the grinding instruction according to the parameters of the excess material of the sand core is specifically:

[0321] Use computer vision technology to identify excess material on the sand core, including location, shape, and size information;

[0322] Obtain the precise location and shape of excess material based on image processing algorithms;

[0323] According to the location information of the excess materials, plan the movement trajectory of the robot arm to ensure that all the excess materials are covered;

[0324] Considering the structural characteristics and grinding requirements of the sand core, the grinding path is designed;

[0325] Set the grinding wheel pressure and speed parameters according to the properties of the excess material;

[0326] The grinding path and parameters are converted into an instruction sequence executable by the robot arm; the instruction sequence includes the movement sequence, speed, acceleration of the robot arm and the working status information of the grinding wheel.

[0327] Specifically, computer vision recognition

[0328] First, computer vision technology is used to identify excess material on the sand cores. This step includes:

[0329] Position identification: Determine the specific location of excess material on the sand core.

[0330] Shape Analysis: Analyze the shape characteristics of excess material.

[0331] Size measurement: measure the size of excess material, such as length, width, etc.

[0332] Image processing algorithms to obtain precise position and morphology

[0333] Based on image processing algorithms, the precise location and shape information of excess materials can be obtained:

[0334] Feature extraction: Use edge detection, threshold segmentation and other techniques to extract the features of redundant materials.

[0335] Morphological operations: Use morphological operations such as opening and closing to further clean up the noise in the image and highlight the shape of redundant materials.

[0336] Planning the motion trajectory of the robot arm

[0337] According to the location information of the excess material, plan the movement trajectory of the robot arm:

[0338] Trajectory planning: Ensure that the robot's motion trajectory can cover all excess materials to avoid omissions.

[0339] Covering strategy: Design a covering strategy so that the robot arm can evenly cover all areas of excess material during the grinding process.

[0340] Design grinding path

[0341] Considering the structural characteristics and grinding requirements of the sand core, the specific grinding path is designed:

[0342] Path strategy selection: According to the distribution of excess material, select the appropriate grinding path strategy, such as linear reciprocating, spiral scanning or contour following.

[0343] Path parameter calculation: calculate the specific parameters of the grinding path, such as the starting point, end point, turning point, etc. of the path.

[0344] Safety boundary setting: Set the safety boundary to ensure that the robot arm does not exceed the safety range during the grinding process to avoid collision or damage to the equipment.

[0345] Setting grinding parameters

[0346] According to the properties of the excess material, set the grinding parameters such as pressure and speed of the grinding wheel:

[0347] Pressure setting: According to the hardness and thickness of the excess material, set the pressure applied by the grinding wheel to ensure the grinding effect.

[0348] Speed ​​setting: According to the distribution of excess material and grinding requirements, set the grinding wheel speed to ensure grinding efficiency and quality.

[0349] Converted into a command sequence executable by the robot

[0350] Convert the grinding path and parameters into a command sequence that can be executed by the robot:

[0351] Instruction sequence: Generates an instruction sequence containing the robot arm's movement sequence, speed, acceleration, and grinding wheel working status information.

[0352] Instruction generation: Ensure that the instruction sequence can be understood and executed by the robot arm to achieve effective grinding of excess materials.

[0353] Specific steps example

[0354] Computer vision recognition

[0355] Use an industrial camera to shoot the bottom surface of the sand core in the visual inspection area to obtain the original image. Then preprocess the original image, including grayscale conversion, noise removal, image enhancement, etc. After that, use image segmentation technology to separate the bottom surface of the sand core from the background, apply edge detection algorithm to extract the boundary information of the excess material on the bottom surface of the sand core, and use regional growth and watershed transformation methods to identify the specific area of ​​the excess material.

[0356] Image processing algorithms to obtain precise position and morphology

[0357] The precise location and shape of the excess material can be obtained by extracting features from the processed image. For example, the Canny edge detection algorithm is used to identify the outline of the excess material, the threshold segmentation is used to distinguish the material from the background, and the morphological operation is used to further clean up the noise and highlight the shape of the material.

[0358] Planning the motion trajectory of the robot arm

[0359] Plan the robot's motion trajectory based on the location information of the excess material. For example, design a linear reciprocating or spiral scanning path to ensure that the robot can cover all the excess material.

[0360] Design grinding path

[0361] Considering the structural characteristics and grinding requirements of the sand core, design a specific grinding path. For example, select a spiral scanning path and set the path's starting point, end point, inflection point and other parameters.

[0362] Setting grinding parameters

[0363] According to the properties of the excess material, set the grinding parameters such as the pressure and speed of the grinding wheel. For example, according to the hardness and thickness of the excess material, set the pressure applied by the grinding wheel to a moderate value, and set the speed of the grinding wheel to 60-100 rpm.

[0364] Converted into a command sequence executable by the robot

[0365] Convert the grinding path and parameters into a command sequence that can be executed by the robot. For example, generate a command sequence that contains the robot's movement sequence, speed, acceleration, and grinding wheel working status information, and ensure that the command sequence can be understood and executed by the robot.

[0366] Through the above steps, the grinding instructions for the excess material of the sand core can be effectively generated, and the efficiency and accuracy of the grinding process can be ensured.

[0367] According to an embodiment of the present application, adjusting the grinding instruction according to the sensor data is specifically:

[0368] Collect data from the force sensor and position sensor installed on the robot arm; and obtain real-time feedback from the current sensor and temperature sensor on the sand core grinding device;

[0369] Use data fusion technology to integrate data from different sensors; analyze sensor data to check if any abnormalities occur;

[0370] Dynamically adjust grinding instructions based on sensor data, including adjusting the speed and pressure of the grinding wheel;

[0371] When an abnormality is detected, the grinding path is modified or grinding is suspended in time.

[0372] Specifically, sensor data is collected

[0373] Robotic arm sensor data

[0374] Force sensor: Installed on the robot arm, used to monitor the force applied to the robot arm during the grinding process. These data can reflect the contact force between the robot arm and the sand core, helping to avoid damage to the sand core or over-grinding due to excessive force.

[0375] Position sensors: used to monitor the position and motion status of the robot arm. These sensors can ensure that the robot arm moves along the predetermined path and maintains the correct posture during the grinding process.

[0376] Core grinding device sensor data

[0377] Current sensor: Installed on the sand core grinding device, used to monitor the working status of the grinding wheel. The current sensor can determine whether the grinding wheel is working properly and the contact between the grinding wheel and the sand core. The current change can be used to determine whether the grinding wheel encounters resistance, so as to adjust the grinding parameters.

[0378] Temperature sensor: used to monitor the temperature change of the grinding wheel during the grinding process. The temperature sensor can prevent the grinding wheel from failing or affecting the grinding effect due to overheating.

[0379] Data fusion and analysis

[0380] Data Fusion

[0381] Data integration: Use data fusion technology to integrate data from different sensors to form a unified data set. Data fusion technology can help eliminate the uncertainty of single sensor data and improve the reliability and accuracy of the overall data.

[0382] Data analysis

[0383] Anomaly detection: Analyze sensor data to check if any abnormal conditions have occurred. For example, if the force detected by the force sensor suddenly increases, it may mean that there is a hard object on the surface of the sand core; if the current sensor detects an abnormal increase in current, it may mean that the grinding wheel encounters greater resistance.

[0384] Dynamic adjustment of grinding instructions

[0385] Grinding instruction adjustment

[0386] Grinding wheel speed adjustment: Dynamically adjust the grinding wheel speed based on sensor data. For example, if the temperature sensor detects that the temperature is too high, the grinding wheel speed can be appropriately reduced to reduce frictional heat.

[0387] Grinding pressure adjustment: Grinding pressure is adjusted according to the data of the force sensor. If the force sensor detects that the applied force is too large, the pressure can be appropriately reduced to prevent damage to the sand core.

[0388] Grinding path adjustment

[0389] Grinding path modification: When an abnormal situation is detected, the grinding path can be modified or grinding can be suspended in time. For example, if the position sensor detects that the robot arm has deviated from the predetermined path, the path can be replanned; if the temperature sensor detects overheating, grinding can be suspended and continued after the temperature drops.

[0390] Specific steps example

[0391] Collecting sensor data

[0392] Force sensor: collects the force conditions of the robot arm during the grinding process in real time.

[0393] Position sensor: monitors the position change of the robot arm.

[0394] Current sensor: monitors the working status of the grinding wheel.

[0395] Temperature sensor: monitors the temperature change of the grinding wheel.

[0396] Data fusion and analysis

[0397] Data integration: Integrate the data from force sensors, position sensors, current sensors, and temperature sensors.

[0398] Abnormality detection: Check whether there are any abnormal conditions, such as sudden increase in power, abnormal current, high temperature, etc.

[0399] Dynamic adjustment of grinding instructions

[0400] Speed ​​adjustment: If the temperature sensor detects that the temperature is too high, reduce the grinding wheel speed.

[0401] Pressure adjustment: If the force sensor detects that too much force is applied, reduce the grinding pressure.

[0402] Path adjustment: If the position sensor detects that the robot arm deviates from the planned path, the path is replanned.

[0403] Exception handling

[0404] Pause grinding: If a serious abnormality is detected, such as a sharp increase in temperature, immediately suspend grinding and wait until the problem is resolved before continuing.

[0405] Through the above steps, it can be ensured that the grinding instructions are dynamically adjusted according to the real-time sensor data, thereby improving the accuracy and safety of the grinding process.

[0406] According to one embodiment of the present application, the sensor data of the robotic arm includes a position sensor and a torque sensor of the robotic arm, which are used to monitor the position and motion state of the robotic arm;

[0407] The torque sensor is used to detect the force applied to the robot arm during the grinding process to prevent excessive torque from causing damage to the equipment or excessive grinding.

[0408] Specifically, the position sensor

[0409] effect

[0410] Monitoring position: The position sensor is used to monitor the position of the robot arm in three-dimensional space in real time to ensure that the robot arm moves accurately to the specified position according to the preset path.

[0411] Correction deviation: By providing real-time feedback of the actual position of the robot arm, any position deviation caused by mechanical error or external interference can be corrected to ensure accurate movement of the robot arm.

[0412] application

[0413] Grinding path execution: During the grinding process, position sensors ensure that the robot arm can move along the pre-planned path to cover all areas of excess material that need to be ground.

[0414] Collision prevention: Position sensors can also help prevent the robot arm from colliding with other equipment or the surrounding environment, ensuring safe operation.

[0415] Torque sensor

[0416] effect

[0417] Monitoring force conditions: Torque sensors are used to detect the force conditions of the robot arm during the grinding process, especially the force generated by the contact between the grinding wheel and the sand core during sand core grinding.

[0418] Prevent equipment damage: By monitoring the force applied to the robotic arm, it is possible to detect in time whether excessive torque has occurred, thus preventing the equipment from being damaged due to excessive torque.

[0419] Prevent over-grinding: The torque sensor can also prevent over-grinding. That is, during the sand core grinding process, if it is detected that the contact force between the grinding wheel and the sand core is too large, it means that there may be a risk of over-grinding. At this time, the system can adjust the grinding parameters to avoid unnecessary damage to the sand core.

[0420] application

[0421] Dynamic adjustment: During the grinding process, if the torque sensor detects that the contact force between the grinding wheel and the sand core is too large, the system can dynamically adjust the speed and pressure of the grinding wheel to reduce the grinding force and prevent damage to the sand core.

[0422] Safety protection: If the torque detected by the torque sensor exceeds the preset safety threshold, the system can immediately stop the grinding action to prevent damage to the equipment or sand core.

[0423] Optimized grinding: By real-time monitoring of the force applied to the robot arm, the system can adjust the grinding strategy according to the actual situation, optimize the grinding effect, and ensure the grinding quality of the sand core.

[0424] Comprehensive Application

[0425] In the actual grinding process, the position sensor and the torque sensor work together to ensure that the robot arm can not only accurately reach the grinding position, but also monitor the force conditions during the grinding process in real time, thereby ensuring the safety and effectiveness of the grinding operation. Through the comprehensive application of these two sensors, the intelligent control of the sand core grinding process can be achieved, the grinding accuracy can be improved, the risk of equipment damage can be reduced, and the quality consistency of the final product can be ensured.

[0426] For example, during the core grinding process, the position sensor ensures that the robot arm moves accurately along the preset path, while the torque sensor monitors the change in torque when the grinding wheel contacts the core. If excessive torque is detected, the system can immediately adjust the grinding parameters, such as reducing the grinding wheel speed or reducing the grinding pressure, to prevent damage to the core. This real-time monitoring and dynamic adjustment mechanism makes the grinding process more controllable, reduces the need for manual intervention, and improves production efficiency.

[0427] According to one embodiment of the present application, the sensor data of the sand core grinding device includes a current sensor and a temperature sensor, which are used to monitor the operating state of the grinding wheel;

[0428] The current sensor determines whether the grinding wheel is working properly and the contact condition between the grinding wheel and the sand core;

[0429] The temperature sensor detects the temperature change of the grinding wheel during the grinding process to prevent the grinding effect from being affected by overheating.

[0430] Specifically, the current sensor

[0431] effect

[0432] The current sensor is mainly used to monitor the working status of the grinding wheel, including whether the grinding wheel is running normally and the contact between the grinding wheel and the sand core.

[0433] Monitor whether the grinding wheel is working properly

[0434] Current change detection: The current sensor can monitor the working current of the grinding wheel motor in real time. If the current fluctuates abnormally or is significantly low, it may indicate that there is a problem with the grinding wheel motor, such as motor blocking, circuit breaking or wear.

[0435] Health status assessment: By detecting current changes, the health status of the grinding wheel motor can be assessed to ensure that the grinding wheel is in good working condition.

[0436] Monitoring the contact between grinding wheel and core

[0437] Contact force detection: When the grinding wheel contacts the sand core, the current changes. The current sensor can detect this change, reflecting the contact force between the grinding wheel and the sand core.

[0438] Prevent overcutting: If the current increases abnormally, it means that the grinding wheel and the sand core are in too close contact, which may cause overcutting. At this time, the system can adjust the pressure or speed of the grinding wheel to prevent the sand core from being damaged.

[0439] Temperature Sensor

[0440] effect

[0441] The temperature sensor is mainly used to detect the temperature change of the grinding wheel during the grinding process to prevent the grinding wheel from overheating and affecting the grinding effect.

[0442] Monitoring grinding wheel temperature changes

[0443] Temperature monitoring: The temperature sensor monitors the temperature of the grinding wheel in real time to ensure that the grinding wheel works within a safe range. If the temperature of the grinding wheel is too high, it may cause annealing of the grinding wheel material and reduce the life of the grinding wheel.

[0444] Cooling effect evaluation: By monitoring temperature changes, the effectiveness of the cooling system can be evaluated. If the grinding wheel temperature continues to rise, it may indicate that the cooling system is not working properly or the coolant flow is insufficient.

[0445] Prevent overheating from affecting the grinding effect

[0446] Temperature threshold setting: The system can set a temperature threshold. When the grinding wheel temperature exceeds the threshold, the system will automatically adjust the grinding wheel speed or suspend grinding and wait for the temperature to drop before continuing grinding.

[0447] Extend the life of the grinding wheel: By monitoring temperature changes, the grinding parameters can be adjusted in time to prevent the grinding wheel from overheating and extend the service life of the grinding wheel.

[0448] Specific application examples

[0449] Application Examples of Current Sensors

[0450] Abnormal current detection: During the grinding process, if the current sensor detects an abnormal increase in current, it indicates that the contact force between the grinding wheel and the sand core is too large, which may cause damage to the sand core. At this time, the system can adjust the grinding wheel pressure or speed to ensure the safety of the grinding process.

[0451] Motor health assessment: By monitoring current changes, the health status of the grinding wheel motor can be evaluated to ensure that the motor is in normal working condition and extend the service life of the equipment.

[0452] Application Examples of Temperature Sensors

[0453] Abnormal temperature detection: During the grinding process, if the temperature sensor detects that the grinding wheel temperature rises abnormally, it indicates that the grinding wheel may be overheated. At this time, the system can automatically adjust the grinding wheel speed or suspend grinding, and wait for the temperature to drop before continuing grinding to prevent damage to the grinding wheel.

[0454] Cooling system evaluation: By monitoring temperature changes, the effectiveness of the cooling system can be evaluated to ensure that the grinding wheel works within the appropriate temperature range and improve the grinding effect.

[0455] Through real-time monitoring of current sensors and temperature sensors, the sand core grinding device can ensure that the grinding wheel works under normal conditions, avoiding the grinding effect affected by abnormal current or excessive temperature, thereby improving the grinding quality and efficiency.

[0456] According to an embodiment of the present application, the image of the bottom surface of the sand core during the grinding process is acquired at a preset time interval, and the grinding state of the sand core is determined according to the image, specifically:

[0457] The camera is triggered to capture images of the bottom surface of the sand core at intervals of 10 seconds; based on feature changes in the image, the removal of excess material on the bottom surface of the sand core is evaluated, and whether there are areas that have not been ground clean or newly generated excess material is checked; and whether the surface of the sand core has achieved the expected smoothness and finish;

[0458] According to the analysis result of the image, the grinding instruction is dynamically adjusted, including adjusting the grinding wheel rotation speed, grinding pressure and grinding path.

[0459] Specifically, image acquisition

[0460] Timed shooting: Set the camera to take an image of the bottom surface of the sand core every 10 seconds. This is done to monitor the state of the sand core during the grinding process in real time to ensure the continuity and timeliness of the grinding operation.

[0461] Image analysis

[0462] Feature change assessment: Analyze the acquired images and focus on checking the removal of excess material on the bottom of the sand core. Specifically, through image processing techniques such as edge detection and threshold segmentation, identify the boundaries of excess material on the bottom of the sand core and evaluate the degree of its removal.

[0463] Unground clean area inspection: Check the bottom surface of the sand core to see if there are unground clean areas or newly generated excess materials. This step is to ensure that the surface of the sand core reaches the expected flatness and smoothness during the grinding process.

[0464] Surface quality judgment: According to the feature changes in the image, judge whether the surface of the sand core has reached the expected smoothness and finish. Through image analysis technology, such as image segmentation and feature point extraction, judge whether the surface of the sand core meets the quality requirements.

[0465] Grinding condition assessment

[0466] Assessment of excess material removal: Image analysis results are used to assess the removal of excess material from the bottom of the core and to check for areas that have not been ground clean or newly generated excess material.

[0467] Surface quality judgment: Based on the feature changes in the image, determine whether the sand core surface has achieved the expected smoothness and finish.

[0468] Dynamic adjustment of grinding instructions

[0469] Adjust grinding wheel speed: If the image analysis results show that there is excess material on the bottom of the sand core that has not been completely removed, the system can adjust the grinding wheel speed to speed up the grinding process.

[0470] Adjust grinding pressure: According to the excess material removal in the image, adjust the pressure of the grinding wheel on the sand core to ensure the grinding effect.

[0471] Adjust grinding path: If the image analysis results show that there are areas that have not been ground clean or newly generated excess material, the system can adjust the grinding path of the robot arm to ensure that all areas that need grinding are covered.

[0472] Specific steps example

[0473] Image acquisition

[0474] Timed shooting: Set the camera to take an image of the bottom surface of the sand core every 10 seconds. For example, use an industrial camera to take images of the bottom surface of the sand core at regular intervals during the grinding process.

[0475] Image analysis

[0476] Feature change assessment: Through image processing techniques such as edge detection and threshold segmentation, the boundaries of excess material on the bottom of the sand core are identified and the degree of its removal is evaluated.

[0477] Check for unground areas: Check the bottom of the core to see if there are unground areas or newly generated excess material. For example, if the image shows that some areas still have excess material, re-grinding is required.

[0478] Surface quality judgment: Based on the feature changes in the image, judge whether the sand core surface has achieved the expected smoothness and finish. For example, if the image shows that the sand core surface is still rough, the grinding parameters need to be adjusted.

[0479] Grinding condition assessment

[0480] Evaluation of excess material removal: Image analysis results are used to evaluate the removal of excess material from the bottom of the sand core.

[0481] Surface quality judgment: Based on the feature changes in the image, determine whether the sand core surface has achieved the expected smoothness and finish.

[0482] Dynamic adjustment of grinding instructions

[0483] Adjust grinding wheel speed: If the image analysis results show that there is excess material on the bottom of the sand core that has not been completely removed, the system can adjust the grinding wheel speed to speed up the grinding process.

[0484] Adjust grinding pressure: According to the excess material removal in the image, adjust the pressure of the grinding wheel on the sand core to ensure the grinding effect.

[0485] Adjust grinding path: If the image analysis results show that there are areas that have not been ground clean or newly generated excess material, the system can adjust the grinding path of the robot arm to ensure that all areas that need grinding are covered.

[0486] Through the above steps, the state of the sand core during the grinding process can be monitored in real time, and the grinding instructions can be dynamically adjusted according to the image analysis results to ensure the quality and efficiency of the sand core grinding.

[0487] Example 2

[0488] A computer program product containing instructions, when running on a device, enables the device to execute the steps in a method for sand core grinding of a one-piece cylinder head.

[0489] Example 3

[0490] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in a method for grinding sand cores of a one-piece cylinder head.

[0491] Example 4

[0492] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the sand core grinding method for a one-piece cylinder head are implemented.

[0493] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0494] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0495] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for grinding a sand core of a one-piece cylinder head, applied to a system including a robot arm and a sand core grinding device, characterized in that: The method includes: Obtaining parameters of the excess material of the sand core according to an initial image, wherein the initial image is obtained by photographing the bottom surface of the sand core based on a visual inspection area; Generate a grinding instruction according to the parameters of the excess material of the sand core, and adjust the grinding instruction according to the sensor data; the grinding instruction is used to make the robot arm cooperate with the sand core grinding device to grind the excess material of the sand core; the sensor data type at least includes the sensor data of the robot arm and the sensor data of the sand core grinding device; The image of the bottom surface of the sand core during the grinding process is acquired at preset time intervals, and the grinding state of the sand core is determined according to the image.

2. The method according to claim 1, characterized in that: The parameters of the excess material of the sand core are obtained according to the initial image, and the initial image is obtained by photographing the bottom surface of the sand core based on the visual inspection area, specifically: Use an industrial camera to photograph the bottom surface of the sand core in the visual inspection area and pre-process the original image, including grayscale conversion, noise removal, and image enhancement; Use image segmentation technology to separate the bottom surface of the sand core from the background; use edge detection algorithm to extract the boundary information of the excess material on the bottom surface of the sand core; use region growing and watershed transformation methods to identify the specific area of ​​excess material; Calculate the geometric parameters of the excess material to assess its size and distribution; analyze the physical properties of the excess material such as density and color differences to distinguish different types of excess material; Output the relevant parameters of excess materials, including location coordinates, size, and quantity information.

3. The method according to claim 1, characterized in that The generating of the grinding instruction according to the parameters of the excess material of the sand core is specifically: Use computer vision technology to identify excess material on the sand core, including location, shape, and size information; Obtain the precise location and shape of excess material based on image processing algorithms; According to the location information of the excess materials, plan the movement trajectory of the robot arm to ensure that all the excess materials are covered; Considering the structural characteristics and grinding requirements of the sand core, the grinding path is designed; Set the grinding wheel pressure and speed parameters according to the properties of the excess material; The grinding path and parameters are converted into an instruction sequence executable by the robot arm; the instruction sequence includes the movement sequence, speed, acceleration of the robot arm and the working status information of the grinding wheel.

4. The method according to claim 1, characterized in that: The adjusting the grinding instruction according to the sensor data is specifically: Collect data from the force sensor and position sensor installed on the robot arm; and obtain real-time feedback from the current sensor and temperature sensor on the sand core grinding device; Use data fusion technology to integrate data from different sensors; analyze sensor data to check if any abnormalities occur; Dynamically adjust grinding instructions based on sensor data, including adjusting the speed and pressure of the grinding wheel; When an abnormality is detected, the grinding path is modified or grinding is suspended in time.

5. The method according to claim 1, characterized in that The sensor data of the robotic arm includes a position sensor and a torque sensor of the robotic arm, which are used to monitor the position and motion state of the robotic arm; The torque sensor is used to detect the force applied to the robot arm during the grinding process to prevent excessive torque from causing damage to the equipment or excessive grinding.

6. The method according to claim 1, characterized in that The sensor data of the sand core grinding device includes a current sensor and a temperature sensor, which are used to monitor the operating state of the grinding wheel; The current sensor determines whether the grinding wheel is working properly and the contact condition between the grinding wheel and the sand core; The temperature sensor detects the temperature change of the grinding wheel during the grinding process to prevent the grinding effect from being affected by overheating.

7. The method according to claim 1, characterized in that The step of acquiring an image of the bottom surface of the sand core during the grinding process at a preset time interval, and determining the grinding state of the sand core according to the image, is specifically as follows: The camera is triggered to capture images of the bottom surface of the sand core at intervals of 10 seconds; based on feature changes in the image, the removal of excess material on the bottom surface of the sand core is evaluated, and whether there are areas that have not been ground clean or newly generated excess material is checked; and whether the surface of the sand core has achieved the expected smoothness and finish; According to the analysis result of the image, the grinding instruction is dynamically adjusted, including adjusting the grinding wheel rotation speed, grinding pressure and grinding path.

8. A computer program product comprising instructions, which, when executed on a device, is characterized in that: The device is enabled to execute the steps in the method for grinding the sand core of a one-piece cylinder head as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for grinding the sand core of a one-piece cylinder head as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for grinding the sand core of a one-piece cylinder head as described in any one of claims 1 to 7 are implemented.

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