A machining center control system

Abstract

The embodiment of the present specification provides a machining center control system, comprising: a numerical control milling machine with a tool magazine, a video acquisition device, a temperature detection device, a cooling device, a memory, and a processor; the processor is used for: acquiring initial machining parameters of the numerical control milling machine from the memory, and controlling the numerical control milling machine to machine a part to be machined with the initial machining parameters; controlling the video acquisition device to acquire a chip video, judging whether chip breaking of the numerical control milling machine is reliable, in response to the chip breaking being unreliable, adjusting the initial machining parameters of the numerical control milling machine, and controlling the numerical control milling machine to machine the part to be machined with the adjusted machining parameters; controlling the temperature detection device to acquire a temperature distribution diagram of the part to be machined, determining a target cooling parameter based on the temperature distribution diagram, and controlling the cooling device to cool the part to be machined with the target cooling parameter.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of processing, in particular to a machining center control system. BACKGROUND

[0002] Numerical control milling machines with tool magazines are also known as machining centers, which have high precision and high quality machining capabilities, and can process complex shaped parts, mainly suitable for machining box type parts. When machining centers process parts for a long time, the machined parts will generate a lot of metal chips and heat. The machined parts with too high temperature and the generated metal chips not only affect the machining precision, but also can even cause harm to the user.

[0003] In order to clean the chips in the cutting fluid in time, CN112589522A discloses a chip collecting device for horizontal machining center, which cleans the chips through the chip cleaning mechanism and filter screen arranged in the machining center, but does not involve adjusting machining parameters based on chip characteristics and cooling machined parts.

[0004] Therefore, it is desirable to provide a machining center control system to improve the quality and efficiency of machining parts by machining centers. SUMMARY

[0005] One of the embodiments of the present specification provides a machining center control system, which comprises a numerical control milling machine with a tool magazine, a video acquisition device, a temperature detection device, a cooling device, a memory, and a processor; the processor is configured to: obtain initial machining parameters of the numerical control milling machine from the memory, and control the numerical control milling machine to machine a part to be machined with the initial machining parameters; the machining parameters of the numerical control milling machine include a first parameter set and a second parameter set, the first parameter set is related to the operating parameters of the numerical control milling machine, and the second parameter set is related to the geometric parameters of the numerical control milling machine; control the video acquisition device to obtain a chip video, judge whether the chip breaking of the numerical control milling machine is reliable, in response to the chip breaking being unreliable, adjust the initial machining parameters of the numerical control milling machine, and control the numerical control milling machine to machine the part to be machined with the adjusted machining parameters; control the temperature detection device to obtain a temperature distribution map of the part to be machined, determine a target cooling parameter based on the temperature distribution map, and control the cooling device to cool the part to be machined with the target cooling parameter.

[0006] One of the embodiments of the present specification provides a machining center control method, the method is executed based on a processor of a machining center control system, comprising: obtaining initial machining parameters of a numerical control milling machine, and controlling the numerical control milling machine to process a part to be processed with the initial machining parameters; the machining parameters of the numerical control milling machine include a first parameter set and a second parameter set, the first parameter set is related to the operating parameters of the numerical control milling machine, and the second parameter set is related to the geometric parameters of the numerical control milling machine; obtaining a chip video, judging whether the chip breaking of the numerical control milling machine is reliable, in response to the chip breaking being unreliable, adjusting the initial machining parameters of the numerical control milling machine, and controlling the numerical control milling machine to process the part to be processed with the adjusted machining parameters; obtaining a temperature distribution map of the part to be processed, determining a target cooling parameter based on the temperature distribution map, and controlling a cooling device to cool the part to be processed with the target cooling parameter.

[0007] One of the embodiments of the present specification provides a machining center control device, the device comprises at least one memory and at least one processor, the at least one memory is used to store computer instructions, and the at least one processor executes the computer instructions or part of the instructions to realize the machining center control method described in the above-mentioned embodiments.

[0008] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium stores computer instructions, when the computer reads the computer instructions, the computer executes the machining center control method described in the above-mentioned embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0009] The present specification will be further illustrated in the form of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, wherein:

[0010] Figure 1 is an exemplary structural diagram of a machining center control system according to some embodiments of the present specification;

[0011] Figure 2 is an exemplary flowchart of a machining center control method according to some embodiments of the present specification;

[0012] Figure 3 is an exemplary schematic diagram for determining whether the chip breaking is reliable according to some embodiments of the present specification;

[0013] Figure 4 is an exemplary flowchart for determining a target cooling parameter according to some embodiments of the present specification;

[0014] Figure 5is an exemplary schematic diagram of determining the temperature of at least one preset point of a part to be processed according to a temperature prediction model shown in some embodiments of the present specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, without creative labor, the present specification can also be applied to other similar scenarios according to these drawings. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in the specification and claims, unless the context clearly indicates otherwise, "a", "one", "an" and / or "the" do not refer to the singular, but can also include the plural. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0018] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps of operation can be removed from these processes.

[0019] When a numerical control milling machine with a tool magazine processes a part for a long time, a lot of chips and heat are generated. The high-temperature processed part and generated chips can affect the processing accuracy. Therefore, it is necessary to reduce the generation of chips and cool the processed part in time. In addition, different materials of the processed part require different processing parameters. The processing parameters need to be determined according to the material type and chip characteristics of the processed part. CN112589522A only cleans the chips through the chip cleaning mechanism and filter screen arranged in the machining center, and adjusts the processing parameters and cools the processed part based on the chip characteristics. Therefore, some embodiments of the machining center control system of the present specification obtain the initial processing parameters of the numerical control milling machine, and control the numerical control milling machine to process the processed part according to the initial processing parameters; and determine whether to adjust the initial processing parameters of the numerical control milling machine based on whether the chip breaking is reliable; on the other hand, the temperature distribution map of the processed part can be obtained, the target cooling parameter is determined based on the temperature distribution map, and the processed part is cooled, which can improve the processing quality and efficiency.

[0020] Figure 1 is an exemplary structural diagram of the machining center control system according to some embodiments of the present specification.

[0021] As Figure 1 shown, in some embodiments, the machining center control system 100 can include a numerical control milling machine 110, a video acquisition device 120, a temperature detection device 130, a cooling device 140, a storage 150, and a processor 160.

[0022] The numerical control milling machine 110 can refer to a device for processing a processed part. The numerical control milling machine 110 can include a tool magazine, which stores different types of tools for performing different processing methods on the processed part. For example, a flat milling cutter can be used to process the flat surface of the processed part; a round corner milling cutter can be used to process the round corner of the processed part.

[0023] The video acquisition device 120 can refer to a device for acquiring relevant videos or images (such as chip videos) of processing the processed part. For example, a video camera.

[0024] The temperature detection device 130 can be used to obtain the temperature distribution map of the processed part. The temperature detection device 130 can be an infrared thermometer, a temperature sensor, etc.

[0025] The cooling device 140 can refer to a device for cooling the processed part. For example, the cooling device can use water cooling, air cooling, etc. to cool the processed part. Among them, water cooling can refer to cooling by spraying water on the processed part through the water spraying port of the cooling device 140.

[0026] The memory 150 can be used to store data and / or instructions related to the machining center control system. For example, store the chip video, etc. The memory 150 can include one or more storage components, each of which can be a separate device or part of other devices.

[0027] The processor 160 can be used to process or execute data and / or instructions related to the machining center control method. In some embodiments, the processor 160 can include one or more processing engines (e.g., single-chip processing engines or multi-chip processing engines).

[0028] In some embodiments, the processor 160 can: obtain initial machining parameters of the numerical control milling machine 110 from the memory 150, and control the numerical control milling machine 110 to machine the part to be machined with the initial machining parameters. Control the video acquisition device 120 to acquire the chip video, judge whether the chip breaking of the numerical control milling machine 110 is reliable, in response to the chip breaking being unreliable, adjust the initial machining parameters of the numerical control milling machine 110, and control the numerical control milling machine 110 to machine the part to be machined with the adjusted machining parameters. Control the temperature detection device 130 to obtain the temperature distribution map of the part to be machined, determine the target cooling parameter based on the temperature distribution map, and control the cooling device 140 to cool the part to be machined with the target cooling parameter.

[0029] More details about the initial machining parameters, chip video, target cooling parameter, etc. described above can be referred to the description of other parts of the specification (e.g., the description of Figure 2 ).

[0030] Figure 2 is an exemplary flowchart of a machining center control method according to some embodiments of the present specification. As shown in Figure 2 , the flow 200 includes the following steps. In some embodiments, the flow 200 can be executed by a processor.

[0031] Step 210, obtaining initial machining parameters of the numerical control milling machine, and controlling the numerical control milling machine to machine the part to be machined with the initial machining parameters.

[0032] The initial machining parameters can be the operating parameters of the numerical control milling machine for the first machining / trial machining of the part to be machined. For example, including machining speed, tool tilt angle, etc.

[0033] In some embodiments, the initial machining parameters can be preset based on experience or system default settings.

[0034] In some embodiments, the machining parameters of the numerical control milling machine can include a first parameter set and a second parameter set.

[0035] The first parameter set can include a cutting speed, a cutting depth, and a feed amount, which respectively refer to a speed at which the tool moves relative to the part to be machined, a depth at which the tool enters perpendicular to the part to be machined during cutting, and a distance at which the tool moves on the surface of the part to be machined during cutting.

[0036] In some embodiments, the first parameter set is related to operating parameters of the numerical control milling machine, and the operating parameters can refer to parameters used to control operation of the numerical control milling machine. For example, the first parameter set can be constituted by or controlled by the operating parameters. The operating parameters can include a motor speed, an operating power, an operating duration, and the like of the numerical control milling machine.

[0037] The second parameter set can include an inclination angle and a shape of a tool tip portion. The inclination angle can refer to a cutting angle of the tool when machining each surface of the part to be machined. For example, the inclination angle can include a primary offset angle, a secondary offset angle, a rake angle, and a relief angle in the case of a turning tool. The shape of the tool tip portion can refer to a tool tip arc radius and the like.

[0038] In some embodiments, the second parameter set is related to geometric parameters of the numerical control milling machine, and the geometric parameters can be used to determine the second parameter set. The geometric parameters can include a tool type and a type of the numerical control milling machine. For example, the tool type and / or the type of the numerical control milling machine are different, and the inclination angle of the tool when machining each surface of the part to be machined is also different.

[0039] In some embodiments, the processor can generate control instructions based on the initial machining parameters to control the numerical control milling machine to perform cutting machining on the part to be machined.

[0040] In step 220, a chip video is obtained, and it is determined whether chip breaking of the numerical control milling machine is reliable. In response to the chip breaking being unreliable, the initial machining parameters of the numerical control milling machine are adjusted, and the numerical control milling machine is controlled to perform machining on the part to be machined with the adjusted machining parameters.

[0041] The chip video can refer to a video of a cutting process of the tool on the part to be machined, and can also be referred to as a chip breaking video.

[0042] In some embodiments, the processor can obtain the chip video by real-time shooting through a video capture device. In some embodiments, the chip video can be stored in a memory.

[0043] In some embodiments, the processor can determine whether chip breaking of the numerical control milling machine is reliable based on the chip video. For example, the processor can extract chip breaking images from the chip video, respectively compare the chip breaking images with standard chip breaking images, and if the image similarity is higher than a similarity threshold, it is determined that the chip breaking is reliable. Chip breaking refers to a process of cutting the part to be machined to generate chips and scraps, and form a product.

[0044] In some embodiments, the processor can determine the first chip feature and / or the second chip feature, and determine whether the chip breaking is reliable, more details can be referred to Figure 3 and the related description.

[0045] In some embodiments, in response to the chip breaking being unreliable, the processor can adjust the initial machining parameter. For example, the processor can acquire the chip breaking image, acquire the adjustment amount by querying the parameter table, adjust the initial machining parameter, and determine the adjusted machining parameter. The parameter table includes multiple groups of machining parameter reference adjustment amounts corresponding to different types of chip breaking images and different types of numerical control milling machines.

[0046] In some embodiments, the processor can adjust the initial machining parameter through a preset algorithm, more details can be referred to Figure 3 and the related description.

[0047] In some embodiments, the processor can generate a control instruction based on the adjusted machining parameter to control the numerical control milling machine to machine the part to be machined.

[0048] In some embodiments, in response to the chip breaking being reliable, the numerical control milling machine can continue to machine the part to be machined with the initial machining parameter.

[0049] Step 230, acquiring a temperature distribution map of the part to be machined, determining a target cooling parameter based on the temperature distribution map, and controlling the cooling device to cool the part to be machined at the target cooling parameter.

[0050] The temperature distribution map can refer to a map reflecting the temperature distribution of different positions of the part to be machined. For example, an infrared thermal image, etc.

[0051] In some embodiments, the processor can acquire the temperature of different positions of the part to be machined based on the temperature detection device, and automatically generate the temperature distribution map.

[0052] The target cooling parameter can refer to a parameter for controlling the cooling device to cool the part to be machined. For example, the target cooling parameter can include the water spraying speed of at least one water spraying port, the cooling position of the part to be machined, etc. The description of the water spraying speed can be referred to Figure 4 and the related description.

[0053] In some embodiments, the processor can determine the position of the part to be machined with a temperature higher than a preset temperature threshold as the cooling position based on the temperature distribution map. In some embodiments, the processor can preset a corresponding water spraying speed in advance for different part temperatures to be machined.

[0054] In some embodiments, the processor can determine the target cooling parameter based on the candidate cooling parameter, more details can be referred to Figure 4 and the related description.

[0055] In some embodiments, the processor can determine the cooling positions of the workpiece, and control the water jet nozzles to spray water at the cooling positions at a water jet speed corresponding to the temperature of the cooling positions, so as to reduce the temperature of the workpiece to a temperature satisfying the preset requirement. When the number of cooling positions exceeds the number of water jet nozzles, the cooling positions with higher temperatures are given priority to reduce the temperature.

[0056] In some embodiments, the processor can start the cooling device to reduce the temperature of the workpiece based on the preset temperature condition. More details can be found in Figure 4 and the related description.

[0057] In some embodiments of the present specification, it is determined whether the chip breaking is reliable by acquiring the chip video, the initial machining parameters are adjusted in real time to ensure the machining quality, and the appropriate cooling parameters are determined based on the temperature distribution map of the workpiece to effectively prevent the size deformation of the workpiece caused by overheating, thereby further improving the machining efficiency and quality.

[0058] Figure 3 is an exemplary schematic diagram for determining whether the chip breaking is reliable according to some embodiments of the present specification. As Figure 3 shown, the processor can determine whether the chip breaking is reliable based on steps 310-350.

[0059] Step 310, judging the material type of the workpiece.

[0060] The material type includes ordinary material, brittle material, ductile material, etc. In some embodiments, the processor can judge the material type of the workpiece in multiple ways. For example, directly obtaining the material type of the workpiece from the production information table, or distinguishing by the elongation of the material.

[0061] The chip features generated by cutting different material types of workpieces are different from each other, so different chip features need to be extracted according to the material type.

[0062] Step 320, in response to the workpiece being a ductile material, extracting first chip features based on the chip video.

[0063] The first chip features can be features representing the degree of chip winding and the possibility of damage to the numerical control milling machine, which can include chip shape, chip continuity, and chip stability.

[0064] The chip shape can include band chip, C-shaped chip, broken chip, pagoda-shaped chip, watch spring-shaped chip, long and tight spiral chip, spiral chip, etc.

[0065] The chip continuity can represent the degree of chip continuity without breaking. The chip continuity of long or spiral chips without breaking is higher than that of chips of other shapes.

[0066] The stability of the cutting chip can refer to data reflecting whether the quality of the cutting chip is stable, and can be measured by the length of the chip and the shape of the cutting chip. For example, the length of the chip and the shape of the cutting chip during the cutting process can be relatively consistent, indicating that the stability of the cutting chip is high.

[0067] In some embodiments, the processor can extract the first cutting chip feature in various ways. For example, the cutting chip images of consecutive multiple frames in the cutting chip video can be extracted, and the first cutting chip feature can be extracted through image recognition technology.

[0068] In some embodiments, the processor can be further configured to: extract at least one machining image of a moment before the cutting chip breaks from the cutting chip video, extract at least one target image based on the at least one machining image; compare each target image with a standard image to determine the shape of the cutting chip; determine at least one cutting chip length of the at least one target image, and determine the continuity of the cutting chip based on the at least one cutting chip length; determine the length variance based on the at least one cutting chip length, and determine the stability of the cutting chip based on the length variance.

[0069] The machining image can refer to an image of the cutting chip before it breaks during machining of the machined part in the cutting chip video.

[0070] In some embodiments, the processor can decompose the cutting chip video frame by frame, and extract a machining image of a moment before each cutting chip breaks.

[0071] The target image can refer to an image related to the cutting chip in the machining image. For example, the target image is an image in which the machining equipment, the collection equipment, etc. are segmented from the machining image, and only the cutting chip part is retained.

[0072] In some embodiments, the processor can obtain the target image based on the machining image through an image segmentation algorithm.

[0073] The standard image can refer to a plurality of images containing various cutting chip shapes that are pre-set. The standard image can be selected from historical cutting chip images.

[0074] In some embodiments, the processor can determine the similarity of the target image to a plurality of standard images, and obtain a standard image whose similarity to the target image satisfies a similarity threshold. The cutting chip shape of the standard image is determined as the cutting chip shape of the target image.

[0075] In some embodiments, the processor can determine a plurality of cutting chip lengths in the plurality of target images through image recognition.

[0076] In some embodiments, the processor can average the plurality of cutting chip lengths, and determine the average value as the continuity of the cutting chip.

[0077] In some embodiments, the processor can determine a length variance based on the plurality of chip lengths, and determine the length variance as the chip stability degree.

[0078] In some embodiments of the present disclosure, the first chip feature is determined by extracting a target image from the machining image. Different materials have different chip features, and the extraction method is also different, which improves the rationality and accuracy of obtaining the first chip feature.

[0079] At step 330, in response to the workpiece being a brittle material, the second chip feature is extracted based on the chip video.

[0080] The second feature can refer to a feature representing the degree of influence of metal debris generated by chip fracture and spatter on the numerical control milling machine. The second chip feature can include chip coverage, roughness, chip spatter degree, and the like of at least one machining critical position.

[0081] The machining critical position can refer to a critical position in the chip that has an impact on chip quality. For example, it includes the contact surface of the tool and the workpiece, the surface of the workpiece, the tool tip, and the like.

[0082] The chip coverage can refer to the ratio of the area of the machining critical position where the chip exists to the total area of the machining critical position. The chip coverage of multiple machining critical positions can be represented by a sequence.

[0083] The roughness can reflect the ratio of the area of the region where the chip is not covered in the machining critical position to the total area of the machining critical position.

[0084] The chip spatter degree can measure the distance range or intensity of the chip spatter from the surface of the workpiece.

[0085] In some embodiments, the processor can extract the second chip feature in various ways. For example, the machining image can be converted into a binary image, and the chip coverage and roughness can be calculated based on the binary image to count the chip area and scratch area in the image. For another example, the chip motion in the chip video can be tracked by an optical flow algorithm to count the chip spatter degree.

[0086] In some embodiments, the processor can be further configured to: extract at least one frame of machining image of at least one preset position from the chip video at an equal time interval; extract at least one frame of chip image based on at least one frame of machining image of at least one preset position at a time; determine the chip coverage based on at least one frame of chip image; extract at least one frame of chip-free image based on at least one frame of machining image of at least one preset position at a time; determine the roughness based on at least one frame of chip-free image; obtain the chip audio feature, compare the chip audio feature with at least one standard audio feature, and determine the target audio feature; and determine the reference chip splashing degree corresponding to the target audio feature as the chip splashing degree of the chip audio feature.

[0087] The preset position can refer to a key machining position.

[0088] The machining image of one preset position can include at least one frame of machining image extracted at an equal time interval.

[0089] The machining image at the same time can include at least one frame of machining image of multiple different preset positions at the time. The equal time interval can be a preset time interval, for example, 1 minute.

[0090] In some embodiments, the processor can extract at least one frame of image from the chip video at an equal time interval, and segment based on the preset position to obtain the machining image of multiple preset positions at each time.

[0091] The chip image can refer to a region containing chips on the preset position in the machining image. The chip-free image can refer to a region without chips on the preset position in the machining image, which can be obtained by image recognition and image segmentation.

[0092] In some embodiments, the processor can determine the ratio of the total area of the chip image and the total area of the machining image of the corresponding preset position as the chip coverage.

[0093] In some embodiments, the processor can determine the total area of the scratch on the chip-free image, and determine the ratio of the total area of the scratch on the chip-free image and the total area of the machining image of the corresponding preset position as the roughness.

[0094] The chip audio feature can refer to the audio feature generated when the chip is generated during machining. The chip audio feature can include frequency, amplitude, spectrum, etc.

[0095] In some embodiments, the processor can collect the chip audio feature through an audio collection device.

[0096] The standard audio feature can refer to a historical chip audio feature. Different standard audio features correspond to different reference chip splashing degrees.

[0097] The target audio feature can refer to a standard audio feature that meets an audio similarity condition with the audio similarity of the chip audio feature.

[0098] In some embodiments, the processor can calculate the similarity between the chip audio feature and the standard audio feature, determine the standard audio feature with the highest similarity as the target audio feature.

[0099] In some embodiments, the processor can determine the reference chip splashing degree corresponding to the target audio feature as the chip splashing degree at the current chip.

[0100] In some embodiments of the present specification, when the workpiece to be machined is a brittle material, the second chip feature is extracted in a reasonable manner, considering that the chip features of workpieces to be machined of different material types are different, and different extraction methods are used to improve the accuracy and reasonableness of obtaining the second chip feature.

[0101] Step 340, in response to the workpiece to be machined being a normal material, extracting a first chip feature and a second chip feature based on the chip video.

[0102] The processor can extract the first chip feature and the second chip feature of the workpiece to be machined, which is the same as steps 320 and 330, and will not be repeated here.

[0103] In some embodiments, the processor can determine whether the first chip feature and / or the second chip feature meets a preset condition to determine whether the chip breaking is reliable.

[0104] The preset condition can refer to a condition for determining whether the chip breaking is reliable, and can include a first preset condition, a second preset condition, and a third preset condition. The preset condition can be preset based on historical experience or artificially. For more details of the preset condition, please refer to the following description.

[0105] Step 350, in response to the first chip feature and / or the second chip feature not meeting the preset condition, determining that the chip breaking is unreliable, and adjusting the initial machining parameter through a preset algorithm.

[0106] The preset algorithm can refer to an algorithm for adjusting the initial machining parameter. For example, the preset algorithm can include a genetic algorithm, a particle swarm optimization algorithm, a vector matching algorithm, etc.

[0107] In some embodiments, the processor can construct a feature vector based on the first chip feature and / or the second chip feature and the initial machining parameter, search in a preset database, and determine the adjustment amount of the initial machining parameter. The preset database stores a plurality of historical feature vectors and a plurality of reference adjustment amounts corresponding thereto. The historical feature vectors are constructed in the same way as the feature vectors are constructed based on historical data, and the reference adjustment amount is the actual adjustment amount corresponding to the historical data.

[0108] In some embodiments, the processor can further determine the target machining parameter by evaluating the model, and more details can be found below.

[0109] In some embodiments of the present disclosure, different ways are adopted to extract the chip features for different types of materials to be machined, thereby improving the pertinence and accuracy of chip feature extraction; whether the chip breaking is reliable is determined by the chip features, so as to adjust the initial machining parameter in real time and improve the machining precision and efficiency.

[0110] In some embodiments, the processor can be further configured to: in response to the first chip feature and / or the second chip feature not satisfying the preset condition, generate at least one set of candidate machining parameters; determine the target machining parameter by evaluating the model based on the at least one set of candidate machining parameters; and the evaluation model is a machine learning model.

[0111] In some embodiments, the processor can compare each of the first chip feature and / or the second chip feature with a preset threshold to determine whether the preset condition is satisfied. For example, the chip coverage rate is compared with a preset chip coverage rate threshold; the roughness is compared with a preset roughness threshold, etc. In response to the first chip feature and / or the second chip feature satisfying the corresponding preset threshold, it is determined that the preset condition is satisfied.

[0112] In some embodiments, the preset condition can include a first preset condition, a second preset condition and a third preset condition. The first preset condition corresponds to a ductile material, and the first preset condition includes that the similarity of the chip shape to a preset shape is greater than a similarity threshold, and the chip continuity is less than a continuity threshold, and the chip stability is greater than a stability threshold. The second preset condition corresponds to a brittle material, and the second preset condition includes that the chip coverage rate is lower than a coverage rate threshold, and the roughness is lower than a roughness threshold, and the chip spatter degree is lower than a spatter degree threshold. The third preset condition corresponds to a common material, and the third preset condition includes that the weighted sum of the first difference and the second difference is lower than a difference threshold. The first difference is determined based on the first chip feature and the corresponding threshold, and the second difference is determined based on the second chip feature and the corresponding threshold.

[0113] The first preset condition can be used to determine whether the chip breaking of the ductile material is reliable.

[0114] In some embodiments, the processor can compare the first chip feature with the first preset condition, and if each parameter (chip shape, chip continuity and chip stability) in the first chip feature satisfies the first preset condition, it can be determined that the chip breaking of the ductile material is reliable.

[0115] The second preset condition can be used to determine whether the chip breaking of the brittle material is reliable.

[0116] In some embodiments, the processor can compare the second chip feature with the second preset condition, and if each parameter (chip coverage, roughness, chip splashing degree) in the second chip feature meets the second preset condition, it can be determined that the brittle material is reliable in chip breaking.

[0117] The third preset condition can be used to determine whether the ordinary material is reliable in chip breaking.

[0118] In some embodiments, the processor can determine a first difference and a second difference based on the first chip feature and the second chip feature.

[0119] The first difference can refer to the sum of the differences between each parameter in the first chip feature and a first threshold value. The first threshold value can include a similarity threshold value, a continuity threshold value, and a stability threshold value.

[0120] In some embodiments, the processor can determine the difference between the chip shape, the chip continuity, the chip stability, and the corresponding threshold value in the first threshold value, and add the three differences to determine the first difference.

[0121] The second difference can refer to the sum of the differences between each parameter in the second chip feature and a second threshold value. The second threshold value can include a coverage threshold value, a roughness threshold value, and a splashing degree threshold value.

[0122] In some embodiments, the processor can determine the difference between the chip coverage, the roughness, and the chip splashing degree, and the corresponding threshold value in the first threshold value, and add the three differences to determine the second difference.

[0123] In some embodiments, the processor can determine whether the weighted sum of the first difference and the second difference is lower than a difference threshold value; if so, the third preset condition is met, and it is determined that the ordinary material is reliable in chip breaking.

[0124] In some embodiments, the weight of the weighted sum of the first difference and the second difference is related to the material brittleness of the part to be machined; the greater the material brittleness, the smaller the weight of the first difference, and the greater the weight of the second difference. The material brittleness can be obtained based on the material information table.

[0125] In some embodiments of the present specification, by adjusting the weight of the weighted sum of the first difference and the second difference based on the material brittleness, it can be more accurate to determine whether the ordinary material meets the third preset condition, and the machining quality and efficiency can be improved.

[0126] In some embodiments of the present specification, by setting different preset conditions for ductile materials, brittle materials, and ordinary materials, the characteristics of the chip during machining of the part to be machined can be more targetedly evaluated, and it can be effectively determined whether the initial machining parameters need to be adjusted, thereby improving the machining quality.

[0127] The candidate machining parameters can refer to machining parameters that can be target machining parameters. The target machining parameters can refer to parameters used to control the numerical control milling machine to finally machine the part to be machined. Corresponding to the initial machining parameters, the target machining parameters also include a first target parameter set and a second target parameter set.

[0128] In some embodiments, the processor can randomly generate at least one set of candidate machining parameters by randomly adding or subtracting the current machining parameters within a preset parameter range based on the current machining parameters. The current machining parameters can refer to the initial machining parameters.

[0129] In some embodiments, the at least one set of candidate machining parameters can include at least one set of first candidate parameter sets and second candidate parameter sets; the parameter ranges of the first candidate parameter sets and the second candidate parameter sets are related to the material brittleness of the part to be machined. The parameter ranges of the first candidate parameter sets and the second candidate parameter sets are different.

[0130] In some embodiments, the first parameter set parameter range, the second parameter set parameter range, and the toughness material, the brittle material, and the ordinary material each have different corresponding relationships. The corresponding relationships can be preset based on machining experience. For example, assuming that the first parameter set of the initial machining parameters is (A, B, C), A, B, and C represent cutting speed, cutting depth, and feed rate, respectively; the second parameter set is (D, E), and D and E represent inclination angle and tool tip shape, respectively. For the toughness material, according to the material properties, the machining quality thereof is more affected by the parameters in the first parameter set, and the parameter range of the first parameter set can be ±10, that is, at least one set of first candidate parameter sets can be randomly generated within the range of (A±10, B±10, C±10), and the parameter range of the second parameter set can be ±0, that is, the initial second parameter set is determined as the second candidate parameter set. For the brittle material, the parameters of the second parameter set have a greater impact on the machining quality thereof, and the parameter range of the second candidate parameter set can be set to be larger, and the parameter range of the first candidate parameter set can be small or even unchanged. For the ordinary material, the parameter ranges of the first parameter set and the second parameter set need to be considered.

[0131] In some embodiments of the present specification, by determining different parameter ranges and generating candidate machining parameters based on material brittleness, the candidate machining parameters can be more suitable for machining parts to be machined with different material properties, and the target machining parameters can be more accurately determined.

[0132] In some embodiments, the processor can determine the target machining parameters based on the at least one set of candidate machining parameters by using an evaluation model.

[0133] In some embodiments, the evaluation model can be a machine learning model or other neural network model, such as a Convolutional Neural Network (CNN) and the like.

[0134] In some embodiments, the processor can be further configured to: process, based on the evaluation model, the material property of the workpiece, the first chip feature, the second chip feature, the initial machining parameter, and the at least one set of candidate machining parameters, to determine at least one set of adjusted first chip feature and adjusted second chip feature; and determine, as the target machining parameter, the candidate machining parameter corresponding to the adjusted first chip feature and the adjusted second chip feature that satisfy the preset condition.

[0135] The material property can include a material type of the workpiece, a hardness of the material, an elongation rate, and the like. The first chip feature, the second chip feature, and the initial machining parameter can be described in relation to the description of Figure 2 、 Figure 3 .

[0136] In some embodiments, the evaluation stability prediction model can be trained by using a first training sample with a large number of first labels.

[0137] In some embodiments, the first training sample includes a sample material property of the workpiece, a sample first chip feature and a sample second chip feature machined based on a sample initial machining parameter, the sample initial machining parameter, and at least one set of sample candidate machining parameters, which can be obtained based on historical machining data of the numerical control milling machine. The sample candidate machining parameters are randomly generated based on the sample initial machining parameter. The second label can be an actual adjusted first chip feature and an actual adjusted second chip feature corresponding to the second training sample, which can be determined based on the historical machining data.

[0138] In some embodiments, the processor can determine, as the target machining parameter, the candidate machining parameter in which the first chip feature and / or the second chip feature satisfy the preset condition.

[0139] In some embodiments of the present specification, the target machining parameter is determined by predicting the first chip feature and the second chip feature corresponding to the candidate machining parameter using the evaluation model, and combining the preset condition, which improves the reliability of the target machining parameter.

[0140] In some embodiments of the present specification, the target machining parameter is determined by processing the at least one set of candidate machining parameters using the evaluation model, which improves the data processing efficiency.

[0141] In some embodiments, the processor can further be configured to: in response to the temperature distribution map not satisfying the temperature preset condition, turn on the cooling device; and in response to turning on the cooling device, determine a target cooling parameter based on the temperature distribution map and the target temperature, the target cooling parameter comprising a water spraying speed of at least one water spraying port.

[0142] The temperature preset condition can refer to a preset condition for determining whether to turn on the cooling device.

[0143] In some embodiments, the processor can divide the temperature distribution map into at least one sub-region of a preset shape and size, perform clustering analysis based on the temperature of each sub-region, and mark a sub-region with a temperature higher than a first temperature threshold after clustering as a target region. The target region can refer to a sub-region that needs to be cooled by the cooling device. The first temperature threshold can refer to a temperature threshold for determining whether a sub-region needs to be cooled, which can be determined by a preset.

[0144] In some embodiments, the temperature preset condition can comprise that the temperature of all sub-regions after clustering is lower than the first temperature threshold, i.e., there is no target region in the temperature distribution map. For a description of the temperature distribution map, please refer to Figure 1 and related descriptions thereof.

[0145] In some embodiments, when the temperature distribution map does not satisfy the temperature preset condition, i.e., there is a target region in the temperature distribution map, the processor can turn on the cooling device to cool the position of the target region corresponding to the workpiece to be processed.

[0146] In some embodiments, the processor can determine a target cooling parameter of the cooling device based on the temperature distribution map and the target temperature.

[0147] The target cooling parameter can refer to a parameter for controlling the cooling device to finally cool the target region, which can include a water spraying speed of at least one water spraying port, a number of open water spraying ports, etc.

[0148] The target temperature can refer to a temperature to which the workpiece to be processed needs to be finally cooled.

[0149] In some embodiments, in response to the workpiece to be processed being a brittle material, the opening of at least one water spraying port of the cooling device and the water spraying speed are related to a chip coverage rate. For example, the greater the chip coverage rate, the greater the water spraying speed.

[0150] In some embodiments of the present specification, the water spraying speed of at least one water spraying port is adjusted by the chip coverage rate of the brittle material to spray water for cooling, which can effectively flush away the chips at the key processing position, avoid the influence of the chips on the processing precision of the brittle material, and reduce the damage of high temperature to the workpiece to be processed; after flushing away the chips, the water can enter the water storage cavity through the metal filter plate in the processing device for repeated use.

[0151] In some embodiments, the processor can retrieve, based on the temperature distribution map, a historical temperature distribution map from the machining database, the historical temperature distribution map satisfying a similarity threshold, and determine a historical cooling parameter corresponding to the historical temperature distribution map as the target cooling parameter.

[0152] In some embodiments, the processor can further determine the target cooling parameter of the cooling device based on a temperature prediction model, which can be seen in detail in Figure 4 and the related description.

[0153] Figure 4 is an exemplary flowchart of determining a target cooling parameter according to some embodiments of the present specification. As Figure 4 shown, the flow 400 includes the following steps. In some embodiments, the flow 400 can be executed by the processor.

[0154] Step 410, generating at least one set of candidate cooling parameters.

[0155] The candidate cooling parameter can refer to a cooling parameter that can be the target cooling parameter.

[0156] In some embodiments, the processor can generate at least one set of candidate cooling parameters in various ways. For example, the cooling parameters of the last cooling device are randomly generated within a preset range as at least one set of candidate cooling parameters; for another example, at least one set of historical cooling parameters that have been used historically are determined as at least one set of candidate cooling parameters.

[0157] Step 420, determining the temperature of at least one preset point of the part to be machined after a preset time based on a temperature prediction model.

[0158] In some embodiments, the processor can determine, based on the temperature prediction model, the temperature of at least one preset point of the part to be machined after a preset time corresponding to each candidate cooling parameter. The preset time can be determined based on experience according to actual cooling requirements. The temperature prediction model can be a machine learning model with a self-defined structure as described below, or a neural network model, for example, a Convolutional Neural Network (CNN).

[0159] Figure 5 is an exemplary schematic diagram of determining the temperature of at least one preset point of the part to be machined based on a temperature prediction model according to some embodiments of the present specification.

[0160] As Figure 5As shown, in some embodiments, the temperature prediction model 500 can include a feature extraction layer 550 and a temperature prediction layer 570; the feature extraction layer 550 is configured to process the temperature distribution map 510 to determine a temperature feature vector 560; and the temperature prediction layer 570 is configured to process the target machining parameter 520, the target temperature 530, the temperature feature vector 560, and the at least one set of candidate cooling parameters 540 to determine the temperature 580 of the at least one preset point of the workpiece after the preset time.

[0161] For more details about the temperature distribution map, the target machining parameter, and the target temperature, please refer to the relevant description above.

[0162] In some embodiments, the feature extraction layer 550 can be a convolutional neural network (CNN) model, and the temperature prediction layer 570 can be a recurrent neural network (RNN) model.

[0163] In some embodiments, the temperature prediction model 500 can be obtained by jointly training the feature extraction layer and the temperature prediction layer. The method of joint training can include, but is not limited to, gradient descent method, etc.

[0164] In some embodiments, the second training sample for training the temperature prediction model includes a sample temperature distribution map, a sample actual machining parameter, a sample target temperature, and at least one set of sample candidate cooling parameters, which can be determined based on historical cooling data. The sample temperature distribution map can be constructed based on the temperature of the workpiece before cooling in the historical cooling data; and the at least one set of sample candidate cooling parameters can be randomly generated based on the sample actual machining parameter. The second label is the actual temperature of each position point of the workpiece after cooling, which can be determined through the historical cooling data.

[0165] In some embodiments of the present specification, by dividing the temperature prediction model into different layers and processing different data respectively, the data processing efficiency and accuracy can be improved.

[0166] In some embodiments, when the processor determines the temperature of the at least one preset point of the workpiece after the preset time corresponding to each candidate cooling parameter, the temperature of the at least one preset point can be compared with the first temperature threshold, and the temperature difference between any two preset points of the plurality of preset points can be compared with the second temperature threshold, and the target cooling parameter can be determined based on the comparison result.

[0167] In step 430, in response to the temperature of the at least one preset point being lower than the first temperature threshold and the temperature difference between any two preset points of the plurality of preset points being lower than the second temperature threshold, the corresponding candidate cooling parameter is determined as the target cooling parameter.

[0168] The second temperature threshold can refer to a temperature threshold used to determine whether the temperature difference between any two preset points meets the temperature requirement of the workpiece to be processed. The first temperature threshold can be referred to as described above.

[0169] In some embodiments of the present specification, the temperature of each preset point on the workpiece to be processed corresponding to the candidate cooling parameter determined by the temperature prediction model is determined, and the target cooling parameter is determined by the first temperature threshold and the second temperature threshold, which can improve the accuracy of determining the target cooling parameter.

[0170] In some embodiments of the present specification, the temperature distribution on the workpiece to be processed can be intuitively reflected by judging the temperature distribution diagram, and the different positions on the workpiece to be processed can be cooled in a targeted manner by determining the speed and opening of each water jet in the target cooling parameter.

[0171] Some embodiments of the present specification also disclose a machining center control device, comprising: at least one memory and at least one processor, the at least one memory is used to store computer instructions, and the at least one processor executes the computer instructions or part of the instructions to realize the machining center control method of any one of the above-mentioned embodiments.

[0172] Some embodiments of the present specification also disclose a computer readable storage medium, which stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer runs the machining center control method of any one of the above-mentioned embodiments.

[0173] The above has described the basic concept, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0174] At the same time, specific words are used in the present specification to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different positions in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0175] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements and sequences, however, the description herein of a process should be understood to include any and all combinations of one or more elements, and sequences that can be perceived as either open-ended or specific.

[0176] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, unless specifically stated as such. It should be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should also be noted that, as used in the specification and the appended claims, the term "or" is generally intended to mean "and / or" unless the context clearly dictates otherwise. Furthermore, the words "comprise," "comprising," "include," "including," and the like are generally intended to be synonymous, unless the context clearly dictates otherwise. Unless specifically stated otherwise, and as can be apparent from the disclosure, use of terms such as "processing," "computing," "calculating," "determining," "displaying," or the like, refer to actions or processes of a machine that manipulates or transforms data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices. Note, too, that while embodiments can be implemented by software, the software can be written in any of numerous languages or combinations of languages, and can be executed in a machine or on a machine.

[0177] Some embodiments use numerals to describe components, quantities of attributes, it should be understood that such numerals used in the description of the embodiments are, in some examples, modified by the adjectives "about," "approximately," or "substantially." Unless otherwise indicated, "about," "approximately," or "substantially" mean that the described numerical value allows for ±20% variation. Accordingly, numerical parameters in the specification and claims are approximations, and can vary depending upon the desired properties sought to be obtained by the particular embodiments. In some embodiments, numerical parameters are determined by the use of standard techniques. Although the numerical ranges and parameters setting forth the broadest scope of the embodiments described herein are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to give a general understanding of the embodiments.

[0178] Each patent, patent application, publication, and other material cited in this specification is incorporated herein by reference in its entirety for the teachings relevant to the sentence and / or paragraph in which the reference is presented. Document histories, to the extent not inconsistent with the pertinent material presented in this specification, are also incorporated by reference herein. To the extent that material incorporated by reference contradicts or contradicts this specification, including claimed terms, the present specification is intended to prevail. It is specifically noted that, where descriptions, definitions, and / or terms in the attached materials differ from those used in the present specification, the descriptions, definitions, and / or terms in the present specification are intended to prevail.

[0179] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. A machining center control system, characterized in that, The system includes: a CNC milling machine with a tool magazine, a video acquisition device, a temperature detection device, a cooling device, a memory, and a processor; the processor is used for: The initial machining parameters of the CNC milling machine are obtained from the memory, and the CNC milling machine is controlled to machine the workpiece according to the initial machining parameters; the machining parameters of the CNC milling machine include a first parameter set and a second parameter set, the first parameter set being related to the operating parameters of the CNC milling machine, and the second parameter set being related to the geometric parameters of the CNC milling machine; The video acquisition device is controlled to acquire chip video, and the reliability of chip breaking of the CNC milling machine is determined. In response to the fact that the part to be processed is a tough material, a first chip feature is extracted based on the chip video. The first chip feature includes chip shape, chip continuity, and chip stability. In response to the fact that the part to be processed is a brittle material, a second chip feature is extracted based on the chip video. The second chip feature includes chip coverage, roughness, and chip splashing degree at at least one key processing location. In response to the fact that the part to be processed is a common material, the first chip feature and the second chip feature are extracted based on the chip video; In response to the first chip feature and / or the second chip feature not meeting the preset conditions, it is determined that the chip breaking is unreliable. Based on the current processing parameters, the current processing parameters are randomly added to or subtracted within the preset parameter range to randomly generate at least one set of candidate processing parameters, wherein the current processing parameters are the initial processing parameters; wherein, the at least one set of candidate processing parameters includes at least one first set of candidate parameters and a second set of candidate parameters, the parameter ranges of the first set of candidate parameters and the second set of candidate parameters are different and related to the material brittleness of the part to be processed; Based on the at least one set of candidate machining parameters, the target machining parameters are determined through an evaluation model, and the CNC milling machine is controlled to process the part to be processed with the target machining parameters. The evaluation model is a machine learning model. The temperature detection device is controlled to acquire the temperature distribution map of the part to be processed, and the target cooling parameters are determined based on the temperature distribution map. The cooling device is then controlled to cool the part to be processed using the target cooling parameters.

2. The system according to claim 1, characterized in that, The processor is also used for: In response to the temperature distribution map not meeting the preset temperature conditions, the cooling device is activated; In response to activating the cooling device, the target cooling parameters are determined based on the temperature distribution map and the target temperature. The target cooling parameters include the water spray rate of at least one spray nozzle.

3. A machining center control method, characterized in that, The method is executed based on the processor of the machining center control system as described in claim 1, and the method includes: The initial machining parameters of the CNC milling machine are obtained, and the CNC milling machine is controlled to machine the workpiece according to the initial machining parameters; the machining parameters of the CNC milling machine include a first parameter set and a second parameter set, wherein the first parameter set is related to the operating parameters of the CNC milling machine, and the second parameter set is related to the geometric parameters of the CNC milling machine; Acquire chip video to determine whether the chip breaking of the CNC milling machine is reliable; In response to the fact that the part to be processed is a tough material, a first chip feature is extracted based on the chip video. The first chip feature includes chip shape, chip continuity, and chip stability. In response to the fact that the part to be processed is a brittle material, a second chip feature is extracted based on the chip video. The second chip feature includes chip coverage, roughness, and chip splashing degree at at least one key processing location. In response to the fact that the part to be processed is a common material, the first chip feature and the second chip feature are extracted based on the chip video; In response to the first chip feature and / or the second chip feature not meeting the preset conditions, it is determined that the chip breaking is unreliable. Based on the current processing parameters, the current processing parameters are randomly added to or subtracted within the preset parameter range to randomly generate at least one set of candidate processing parameters, wherein the current processing parameters are the initial processing parameters; wherein, the at least one set of candidate processing parameters includes at least one first set of candidate parameters and a second set of candidate parameters, the parameter ranges of the first set of candidate parameters and the second set of candidate parameters are different and related to the material brittleness of the part to be processed; Based on the at least one set of candidate machining parameters, the target machining parameters are determined through an evaluation model, and the CNC milling machine is controlled to process the part to be processed with the target machining parameters. The evaluation model is a machine learning model. Obtain the temperature distribution map of the part to be processed, determine the target cooling parameters based on the temperature distribution map, and control the cooling device to cool the part to be processed with the target cooling parameters.

4. The method according to claim 3, characterized in that, The step of obtaining a temperature distribution map of the part to be processed, determining a target cooling parameter based on the temperature distribution map, and controlling the cooling device to cool the part to be processed using the target cooling parameter includes: In response to the temperature distribution map not meeting the preset temperature conditions, the cooling device is activated; In response to activating the cooling device, the target cooling parameters are determined based on the temperature distribution map and the target temperature. The target cooling parameters include the water spray rate of at least one spray nozzle.

5. A machining center control device, characterized in that, The device includes at least one memory and at least one processor, the at least one memory being used to store computer instructions, and the at least one processor executing the computer instructions or parts thereof to implement the machining center control method according to any one of claims 3-4.

6. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, and when the computer reads the computer instructions, the computer executes the machining center control method as described in any one of claims 3-4.

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