Thin-wall part machining method and device, electronic equipment and storage medium

Abstract

The invention discloses a thin-wall part machining method and device, electronic equipment and a storage medium, and relates to the technical field of part machining.The method comprises the steps that rough machining and rough milling treatment is conducted on a target thin-wall part, and a part semi-finished product is obtained; acquiring image information of the semi-finished part; determining current size information of the semi-finished part based on the image information; obtaining target size information of the target thin-wall part; determining finish machining data information of the semi-finished part based on the current size information and the target size information; and on the basis of the finish machining data information, finish milling is conducted on the semi-finished part product, and the finished part product is obtained.The technical problem that in the prior art, the thin-wall part machining precision is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of parts processing, and in particular to a processing method, device, electronic equipment and storage medium for thin-walled parts. Background Art

[0002] In the field of parts processing, thin-walled parts are widely used in various equipment due to their compact structure, light weight and special performance. However, thin-walled parts have low rigidity and weak strength. During processing, clamping is difficult, and vibration and cutting heat are generated during cutting, which can easily cause deformation of thin-walled parts. This affects the dimensional accuracy and shape accuracy of the workpiece, which is also the difficulty in processing thin-walled parts.

[0003] In the prior art, there is a method of using a special vacuum suction cup to clamp thin-walled parts for mass processing. However, due to the thermal expansion of the parts, the parts will be arched between two solid rods or two holes of the suction cup, affecting the accuracy of the parts. Therefore, it is necessary to monitor the status of the parts in real time during the processing of thin-walled parts.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic equipment and storage medium for processing thin-walled parts, so as to at least solve the technical problem of low processing precision of thin-walled parts in the prior art.

[0006] According to one aspect of an embodiment of the present invention, a method for processing thin-walled parts is provided, comprising: performing rough processing and rough milling on a target thin-walled part to obtain a semi-finished part; obtaining image information of the semi-finished part; determining current size information of the semi-finished part based on the image information; obtaining target size information of the target thin-walled part; determining finishing data information of the semi-finished part based on the current size information and the target size information; and finishing milling the semi-finished part based on the finishing data information to obtain a finished part.

[0007] Furthermore, based on the image information, the current size information of the semi-finished part is determined, including: preprocessing the image information to obtain preprocessed image information; based on the preprocessed image information, determining the current size information; wherein the preprocessing includes at least one of the following: Gaussian filtering, gradient, non-maximum suppression and double threshold processing.

[0008] Furthermore, based on the preprocessed image information, the current size information is determined, including: based on a preset target neural network, performing edge recognition processing on the preprocessed image information to obtain an edge recognition processing result; based on the edge recognition processing result, the current size information is determined.

[0009] Furthermore, based on a preset target neural network, edge recognition processing is performed on the preprocessed image information, and before the edge recognition processing result is obtained, it includes: obtaining a sample image and an initial neural network, wherein the initial neural network includes at least one of the following: an initial image encoding module and an initial image decoding module; processing the sample image based on the initial neural network to obtain an initial processed image; determining a deviation value between the initial processed image and the sample image, and performing a first preset processing on the initial neural network based on the deviation value to generate a first-stage neural network, wherein the first-stage neural network at least includes a first image encoding module; obtaining a preset image decoding module, and generating a second-stage neural network based on the preset image decoding module and the first image encoding module; obtaining image label information corresponding to the sample image, and constructing an objective function based on the sample image and the image label information; performing a second preset processing on the second-stage neural network based on the objective function to generate a preset target neural network.

[0010] Furthermore, the target thin-walled part is subjected to rough machining and rough milling to obtain a semi-finished part, including the following steps: rough machining the target thin-walled part; placing the rough-machined target thin-walled part on a vacuum suction cup, and rough milling the target thin-walled part to obtain a semi-finished part.

[0011] Furthermore, based on the finishing data information, the semi-finished parts are finish-milled, including: acquiring sensor information of the target device; generating target action instructions based on the finishing data information and the sensor information, the target action instructions are used to control the target device to finish-mill the semi-finished parts.

[0012] According to another aspect of an embodiment of the present invention, a processing device for thin-walled parts is also provided, including: a first processing module, the first processing module is used to perform rough processing and rough milling on the target thin-walled part to obtain a semi-finished part; a first acquisition module, the first acquisition module is used to acquire image information of the semi-finished part; a first determination module, the first determination module is used to determine the current size information of the semi-finished part based on the image information; a second acquisition module, the second acquisition module is used to acquire target size information of the target thin-walled part; a second determination module, the second determination module is used to determine the finishing data information of the semi-finished part based on the current size information and the target size information; and a second processing module, the second processing module is used to perform finish milling on the semi-finished part based on the finish data information to obtain a finished part.

[0013] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0017] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0018] In an embodiment of the present invention, after rough machining and rough milling are performed on a target thin-walled part to obtain a semi-finished part, the current size of the semi-finished part is determined by a real-time image of the semi-finished part, and fine machining data is determined based on the current size and the target size of the target thin-walled part, thereby achieving fine machining of the target thin-walled part, improving the machining accuracy of the part, and thereby solving the technical problem of low machining accuracy of thin-walled parts in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 It is a flow chart of a method for processing thin-walled parts according to the prior art;

[0021] Figure 2 is a schematic structural diagram of a thin-walled parts processing device according to an embodiment of the present invention;

[0022] Figure 3 yes Figure 2 Sectional view at AA in the middle;

[0023] Figure 4 The invention is a schematic structural diagram of a thin-walled parts processing device according to the prior art.

[0024] The above drawings include the following reference numerals:

[0025] 10. Vacuum suction cup;

[0026] 20. Sensors;

[0027] 41. The first processing module;

[0028] 42. A first acquisition module;

[0029] 43. A first determination module;

[0030] 44. Second acquisition module;

[0031] 45. Second determination module;

[0032] 46. ​​The second processing module. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] According to an embodiment of the present invention, an embodiment of a method for processing thin-walled parts is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 1 is a method according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0037] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0038] In step S100, excess material on the part is removed by rough machining and rough milling to quickly form the basic shape of the part, so as to improve machining efficiency.

[0039] Step S200, obtaining image information of the semi-finished part.

[0040] In step S200, images of the semi-finished parts are acquired using a high-precision imaging device (such as an industrial camera or a laser scanner) so that the images can be used for subsequent dimensional measurement and analysis.

[0041] Step S300, determining the current size information of the semi-finished part based on the image information.

[0042] In S300, the current size information of the semi-finished part includes but is not limited to key sizes such as length, width, height, and aperture.

[0043] Step S400, obtaining target size information of a target thin-walled part.

[0044] In step S400, the target size information of the target thin-walled part will be used as a basis for subsequent fine processing.

[0045] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0046] In step S500, the area and amount of processing required for further processing are calculated based on the current size information and target size information obtained in steps S300 and S400 to ensure that the processed parts can meet the design requirements.

[0047] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0048] In step S600, the semi-finished part is further processed using fine milling equipment and tools according to the fine processing data calculated in step S500 to remove excess material to achieve the dimensional accuracy and surface quality required by the design. After fine milling, the part size meets the design requirements, but before it becomes a finished product, some post-processing may be required, such as deburring, cleaning, inspection, etc., to ensure the quality of the part.

[0049] Through the above steps, after rough processing and rough milling of the target thin-walled part to obtain a semi-finished part, the current size of the semi-finished part can be determined through the real-time image of the semi-finished part, and the finishing data can be determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0050] In this embodiment, the state of the semi-finished parts can be monitored during the fine processing, and fine processing can be performed according to the state of the parts, thereby improving the stability of the thin-walled parts during processing, ensuring the accuracy of the processed thin-walled parts, and achieving better results in subsequent use. In addition, the downtime adjustment time caused by unstable processing is reduced, so that the overall processing efficiency is improved and the waste of raw materials and the cost of reworking scraps are reduced.

[0051] As an optional implementation, based on the image information, determining the current size information of the semi-finished part includes:

[0052] Step S310, preprocessing the image information to obtain preprocessed image information, wherein the preprocessing includes at least one of the following: Gaussian filtering, gradient processing, non-maximum suppression, and double threshold processing.

[0053] In step S310, the image information may be three-dimensional image information, and the three-dimensional image is input into the model to implement preprocessing of the three-dimensional image. In a specific embodiment of the present application, the preprocessing includes Gaussian filtering, gradient, non-maximum suppression, and double threshold processing performed in sequence to extract useful features such as edges, contours, etc. from the three-dimensional image to provide input data for subsequent image analysis or machine learning models.

[0054] Step S320, determining current size information based on the pre-processed image information;

[0055] Through the above steps, the preprocessing of the image information is achieved, so that the current size information obtained subsequently is more accurate, thereby improving the processing accuracy of the target thin-walled parts.

[0056] Further, based on the preprocessed image information, current size information is determined, including:

[0057] Step S321, based on a preset target neural network, edge recognition processing is performed on the pre-processed image information to obtain an edge recognition processing result.

[0058] In step S321, the preprocessed image is input into a preset neural network, and the network outputs the position information of the edges in the image. The output of the neural network is usually an edge map, which highlights the edges in the image. This result can be used for subsequent size information determination.

[0059] Step S322, determining current size information based on the edge recognition processing result.

[0060] In step S322, after obtaining the edge recognition results, it is necessary to analyze these edges to determine the size of the objects or features in the image (including finding continuous edge segments and measuring their lengths). The determination of the size information involves geometric analysis, such as using straight lines, curves or shape recognition algorithms to measure the width, height, depth, etc. of the object. In some cases, it may be necessary to calibrate the measurement results with reference to known size standards or ratios. After determining the size information, calibration and verification steps are required to ensure the accuracy of the measurement results. Finally, the determined size information is output, which can be numerical data, charts, or annotations superimposed on the original image.

[0061] Through the above steps, relatively accurate current size information of the semi-finished part is determined to facilitate subsequent processing of the semi-finished part.

[0062] Furthermore, based on the preset target neural network, edge recognition processing is performed on the preprocessed image information, and before the edge recognition processing result is obtained, it includes:

[0063] Step S311, obtaining a sample image and an initial neural network, wherein the initial neural network includes at least one of the following: an initial image encoding module and an initial image decoding module.

[0064] In step S311, the initial neural network can be any neural network model architecture that can perform edge recognition, such as an image reconstruction model or an image restoration model. In a specific embodiment of the present application, the initial neural network includes an image encoding module and an image decoding module, and the image encoding module may include multiple convolutional layers and multiple pooling layers, and the multiple convolutional layers and the multiple pooling layers may be distributed alternately, such as the first convolutional layer (such as convolution kernel size: 9x9, number of channels: 3, corresponding to the three channels of R, G, and B of the image), the first pooling layer (such as pooling size: 3x3), the second convolutional layer (such as convolution kernel size: 9x9, number of channels: 144), the second pooling layer (such as pooling size: 3x3), etc. The image decoding module includes multiple upsampling layers and multiple convolution layers, and the multiple upsampling layers and the multiple convolution layers can be distributed alternately, such as the first upsampling layer (such as upsampling size: 2x2), the first convolution layer (such as convolution kernel size: 3x3, number of channels: 288), the second upsampling layer (such as upsampling size: 2x2), the second convolution layer (such as convolution kernel size: 3x3, number of channels: 144), etc. The output of the pooling layer of the image encoding module is linked to the input of the upsampling layer of the image decoding module.

[0065] Step S312, processing the sample image based on the initial neural network to obtain an initial processed image.

[0066] Step S312 is performed so that the initial neural network can be optimized according to the deviation value between the initial processed image and the sample image.

[0067] Step S313, determining the deviation value between the initial processed image and the sample image, and performing a first preset processing on the initial neural network based on the deviation value to generate a first stage neural network, wherein the first stage neural network includes at least a first image encoding module.

[0068] In step S313, unsupervised network optimization processing is performed on the initial neural network based on the deviation value to generate a first-stage neural network.

[0069] Step S314, obtaining a preset image decoding module, and generating a second-stage neural network based on the preset image decoding module and the first image encoding module.

[0070] In step S314, the preset image decoding module and the first image encoding module are combined to construct a second-stage neural network; the preset image decoding module includes multiple upsampling layers and multiple convolutional layers, and the multiple upsampling layers and the multiple convolutional layers can be distributed alternately. In the preset image decoding module, an output layer is connected after the last convolutional layer, and the output layer can include a convolution kernel and an activation function such as sigmoid, so that the edge probability value (such as a probability value of 0-1) of each pixel point in the corresponding image can be generated through the output layer, and then the pixel point whose edge probability value exceeds the preset probability value can be used as the edge of the corresponding thin-walled part. The specific value of the preset probability value can be configured according to actual needs, such as taking values ​​such as 0.6, 0.7, etc.

[0071] Step S315, obtaining image label information corresponding to the sample image, and constructing an objective function based on the sample image and the image label information.

[0072] In step S315, image label information corresponding to the sample image is obtained; the image label information indicates the edge points of the thin-walled parts, and an objective function (loss function), such as mean square error, is constructed based on the sample image and the image label information corresponding to the sample image.

[0073] Step S316, performing a second preset processing on the second stage neural network based on the objective function to generate a preset target neural network.

[0074] In step S316, supervised network optimization processing is performed on the second-stage neural network based on the objective function to generate a preset target neural network so that the current size information of the semi-finished part can be obtained by marginalizing the image information through the preset target neural network.

[0075] Through the above steps, a trained preset target neural network is obtained to improve the dimensional accuracy of the semi-finished parts during the marginal processing and improve the processing accuracy.

[0076] Further, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part, including the following steps:

[0077] Step S010, performing rough machining on the target thin-walled part.

[0078] In step S010, rough machining is performed on the target thin-walled part to make its shape close to the final design shape. The main purpose of this step is to remove excess material and prepare for subsequent processing. Rough machining includes but is not limited to milling operations.

[0079] Step S020, placing the rough-machined target thin-walled part on a vacuum suction cup, and performing rough milling on the target thin-walled part to obtain a semi-finished part.

[0080] In step S020, the thin-walled part to be processed is placed on a vacuum suction cup to protect the part from damage during subsequent processing and improve the processing accuracy. The vacuum suction cup can absorb vibration, reduce noise during processing, and improve the stability of the part. Next, the thin-walled part to be processed is rough milled to make the shape of the target thin-walled part closer to the final design shape. The main purpose of rough milling is to remove more material to prepare for subsequent fine processing.

[0081] Further, based on the finishing data information, the semi-finished parts are finished by finishing milling, including:

[0082] Step S610, obtaining sensor information of the target device.

[0083] In step S620, sensor information of the target device may be fed back in real time.

[0084] Step S620, generating a target action instruction based on the finishing data information and the sensor information, wherein the target action instruction is used to control the target device to perform finish milling on the semi-finished part.

[0085] In step S620, when the thin-walled parts to be processed are finely milled according to the data information to be finely processed, the fine milling processing equipment includes a movable milling mechanism and a carrier for placing thin-walled parts. The bottom of the carrier contacts the adjustment bolt to achieve up and down movement, and the carrier is also provided with a transverse through groove and a longitudinal through groove for the transverse and longitudinal movement of the carrier. The carrier is movably arranged so that the milling mechanism can fit the thin-walled parts on the carrier more closely when processing the thin-walled parts. The movable milling mechanism is provided with fan-shaped piezoelectric ceramics and a vibrating body, and two rows of fan-shaped grooves are arranged on the vibrating body. A milling cutter head is arranged at the bottom, and the fan-shaped piezoelectric ceramics installed in the upper row of fan-shaped grooves excite bending vibration, and the fan-shaped piezoelectric ceramics installed in the lower row of fan-shaped grooves excite longitudinal vibration. The longitudinal and bending vibrations are combined to generate elliptical ultrasonic vibrations on the surface of the milling cutter head, and the thin-walled parts are milled. Among them, ultrasonic elliptical vibration processing belongs to intermittent milling, which can effectively improve the temperature rise of thin-walled parts and cutter heads, ensure the processing quality, and obtain high-precision thin-walled parts.

[0086] Combination Figures 2 to 3 As shown, a plurality of sensors 20 are arranged on the vacuum suction cup 10, and the sensors 20 are distributed in a dot matrix. During the fine milling process, high-precision sensors are used in conjunction with the vacuum suction cup 10 to collect signals, wherein a signal frequency is generated by a signal acquisition board, and the RS232 protocol is used to communicate with the HMI interactive screen to form a vertical displacement curve of the semi-finished part. Different switch signals are output according to the vibration amplitude of the curve to communicate with the numerical control system NC, thereby realizing precise control of the milling process.

[0087] Through the above steps, the status of semi-finished parts can be monitored, and fine processing can be performed according to the status of the parts, thereby improving the stability of thin-walled parts during processing, ensuring the accuracy of the processed thin-walled parts, and achieving better results in subsequent use. In addition, the downtime adjustment time caused by unstable processing is reduced, thereby improving the overall processing efficiency and reducing the waste of raw materials and the cost of reworking scraps.

[0088] Combination Figure 4 As shown, according to an embodiment of the present invention, an embodiment of a processing device for thin-walled parts is provided. It should be noted that the device can be used to execute the processing methods of thin-walled parts in the above embodiments.

[0089] Specifically, the processing device for thin-walled parts includes: a first processing module 41, a first acquisition module 42, a first determination module 43, a second acquisition module 44, a second determination module 45 and a second processing module 46, wherein the first processing module 41 is used to perform rough processing and rough milling on the target thin-walled part to obtain a semi-finished part; the first acquisition module 42 is used to obtain image information of the semi-finished part; the first determination module 43 is used to determine the current size information of the semi-finished part based on the image information; the second acquisition module 44 is used to obtain the target size information of the target thin-walled part; the second determination module 45 is used to determine the finishing data information of the semi-finished part based on the current size information and the target size information; the second processing module 46 is used to perform finish milling on the semi-finished part based on the finish data information to obtain a finished part.

[0090] By adopting this processing device, after rough processing and rough milling are performed on the target thin-walled part to obtain the semi-finished part, the current size of the semi-finished part is determined through the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby realizing the fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0091] The embodiment of the present application also provides an electronic device, comprising: a memory storing an executable program; a processor for running the program, wherein when the program is running, the method in each embodiment of the present invention is executed. The method comprises the following steps:

[0092] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0093] Step S200, obtaining image information of the semi-finished part.

[0094] Step S300, determining the current size information of the semi-finished part based on the image information.

[0095] Step S400, obtaining target size information of a target thin-walled part.

[0096] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0097] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0098] Through the above steps, after the target thin-walled part is rough-machined and roughly milled to obtain a semi-finished part, the current size of the semi-finished part is determined by the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0099] The embodiment of the present application further provides a computer-readable storage medium, the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the method in each embodiment of the present invention. The method includes the following steps:

[0100] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0101] Step S200, obtaining image information of the semi-finished part.

[0102] Step S300, determining the current size information of the semi-finished part based on the image information.

[0103] Step S400, obtaining target size information of a target thin-walled part.

[0104] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0105] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0106] Through the above steps, after the target thin-walled part is rough-machined and roughly milled to obtain a semi-finished part, the current size of the semi-finished part is determined by the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0107] The embodiments of the present application also provide a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor. The method includes the following steps:

[0108] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0109] Step S200, obtaining image information of the semi-finished part.

[0110] Step S300, determining the current size information of the semi-finished part based on the image information.

[0111] Step S400, obtaining target size information of a target thin-walled part.

[0112] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0113] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0114] Through the above steps, after the target thin-walled part is rough-machined and roughly milled to obtain a semi-finished part, the current size of the semi-finished part is determined by the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0115] The embodiments of the present application also provide a computer program product, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented. The method includes the following steps:

[0116] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0117] Step S200, obtaining image information of the semi-finished part.

[0118] Step S300, determining the current size information of the semi-finished part based on the image information.

[0119] Step S400, obtaining target size information of a target thin-walled part.

[0120] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0121] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0122] Through the above steps, after the target thin-walled part is rough-machined and roughly milled to obtain a semi-finished part, the current size of the semi-finished part is determined by the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0123] The embodiment of the present application also provides a computer program, which implements the method in each embodiment of the present invention described above when the computer program is executed by a processor. The method comprises the following steps:

[0124] Step S100, rough machining and rough milling are performed on the target thin-walled part to obtain a semi-finished part.

[0125] Step S200, obtaining image information of the semi-finished part.

[0126] Step S300, determining the current size information of the semi-finished part based on the image information.

[0127] Step S400, obtaining target size information of a target thin-walled part.

[0128] Step S500, based on the current size information and the target size information, determine the finishing data information of the semi-finished part.

[0129] Step S600, based on the finishing data information, finish milling is performed on the semi-finished part to obtain the finished part.

[0130] Through the above steps, after the target thin-walled part is rough-machined and roughly milled to obtain a semi-finished part, the current size of the semi-finished part is determined by the real-time image of the semi-finished part, and the finishing data is determined according to the current size and the target size of the target thin-walled part, thereby achieving fine processing of the target thin-walled part, improving the processing accuracy of the part, and thus solving the technical problem of low processing accuracy of thin-walled parts in the prior art.

[0131] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0136] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing thin-walled parts, characterized in that: include: Perform rough machining and rough milling on the target thin-walled parts to obtain semi-finished parts; Acquire image information of the semi-finished part; Based on the image information, determine the current size information of the semi-finished part; Acquiring target size information of the target thin-walled part; Determining finishing data information of the semi-finished part based on the current size information and the target size information; Based on the finishing data information, the semi-finished part is finish-milled to obtain a finished part.

2. The method according to claim 1, characterized in that Determining the current size information of the semi-finished part based on the image information includes: Preprocessing the image information to obtain preprocessed image information; Based on the preprocessed image information, determining the current size information; Wherein, the preprocessing includes at least one of the following: Gaussian filtering, gradient processing, non-maximum suppression and double threshold processing.

3. The method according to claim 2, characterized in that Determining the current size information based on the preprocessed image information includes: Based on a preset target neural network, edge recognition processing is performed on the preprocessed image information to obtain an edge recognition processing result; Based on the edge recognition processing result, the current size information is determined.

4. The method according to claim 3, characterized in that Based on the preset target neural network, edge recognition processing is performed on the preprocessed image information, and before the edge recognition processing result is obtained, it includes: Acquire a sample image and an initial neural network, wherein the initial neural network includes at least one of the following: an initial image encoding module and an initial image decoding module; Processing the sample image based on the initial neural network to obtain an initial processed image; Determine a deviation value between the initial processed image and the sample image, and perform a first preset processing on the initial neural network based on the deviation value to generate a first-stage neural network, wherein the first-stage neural network at least includes a first image encoding module; Obtaining a preset image decoding module, and generating a second-stage neural network based on the preset image decoding module and the first image encoding module; Acquire image label information corresponding to the sample image, and construct an objective function based on the sample image and the image label information; The second stage neural network is subjected to a second preset processing based on the objective function to generate the preset target neural network.

5. The method according to claim 1, characterized in that The target thin-walled part is subjected to rough machining and rough milling to obtain the semi-finished part, comprising the following steps: Rough machining of target thin-walled parts; The target thin-walled part after rough machining is placed on a vacuum suction cup, and the target thin-walled part is subjected to rough milling to obtain the semi-finished part.

6. The method according to claim 1, characterized in that Based on the finishing data information, finishing milling is performed on the semi-finished part, including: Get sensor information of the target device; Based on the finishing data information and the sensor information, a target action instruction is generated, and the target action instruction is used to control the target device to perform finish milling on the semi-finished part.

7. A processing device for thin-walled parts, characterized in that: include: A first processing module, wherein the first processing module is used to perform rough processing and rough milling on the target thin-walled part to obtain a semi-finished part; A first acquisition module, the first acquisition module is used to acquire image information of the semi-finished part; A first determining module, the first determining module is used to determine the current size information of the semi-finished part based on the image information; A second acquisition module, the second acquisition module is used to acquire target size information of the target thin-walled part; A second determination module, the second determination module is used to determine the finishing data information of the semi-finished part based on the current size information and the target size information; The second processing module is used to perform fine milling on the semi-finished part based on the fine processing data information to obtain a finished part.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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