CN115055992B

CN115055992B - Intelligent multifunctional clamp and adjusting method thereof

Info

Publication number: CN115055992B
Application number: CN202210697262.2A
Authority: CN
Inventors: 李宋英; 赵成元; 翟超生
Original assignee: Kunshan Yong Xiang Precision Machinery Co ltd
Current assignee: Kunshan Yong Xiang Precision Machinery Co ltd
Priority date: 2022-06-20
Filing date: 2022-06-20
Publication date: 2023-05-09
Anticipated expiration: 2042-06-20
Also published as: CN115055992A; CN116728126A

Abstract

The embodiment of the specification provides an intelligent multifunctional clamp and an adjusting method thereof, wherein the intelligent multifunctional clamp comprises a clamping device, an airtight detection device, a processor and a controller; the clamping device is used for fixing a workpiece; the airtight detection device is used for performing airtight detection on the clamping device to determine an airtight detection result and sending the airtight detection result to the processor; a processor for receiving the airtight detection result; determining a target operating parameter of the clamping device based on the air tightness detection result; determining a first control instruction based on the target working parameter, and sending the control instruction to the controller; the controller is used for receiving a first control instruction sent by the processor; and controlling an adjustment operation of the clamping device based on the first control instruction.

Description

Intelligent multifunctional clamp and adjusting method thereof

Technical Field

The specification relates to the field of clamps, in particular to an intelligent multifunctional clamp and an adjusting method thereof.

Background

The clamp has an important function in the mechanical manufacturing process, and in the production line machining process, a workpiece is required to be clamped and fixed through the clamp, and then related machining production operation is carried out. Because the machining process usually involves the cooperative operation of various devices, such as cutting, polishing, vibration of mechanical devices, etc., the workpiece is loosened to different degrees, the fixture needs to be correspondingly adjusted to fix the workpiece, so as to keep continuous production. If the adjustment is not timely or the adjustment accuracy is not sufficient, the production line is possibly stopped, and even a machined part or machine equipment is damaged.

Therefore, the intelligent multifunctional clamp and the adjusting method thereof can be used for quickly and efficiently adjusting the working parameters of the clamp according to the real-time condition of production and processing in the production line processing process, and meet the production requirements.

Disclosure of Invention

One of the embodiments of the present specification provides an intelligent multifunctional clamp, which includes a clamping device, an airtight detection device, a processor, and a controller; the clamping device is used for fixing a workpiece; the airtight detection device is used for performing airtight detection on the clamping device to determine an airtight detection result and sending the airtight detection result to the processor; a processor for receiving the airtight detection result; determining a target operating parameter of the clamping device based on the air tightness detection result; determining a first control instruction based on the target working parameter, and sending the first control instruction to the controller; the controller is used for receiving a first control instruction sent by the processor; and controlling an adjustment operation of the clamping device based on the first control instruction.

One of the embodiments of the present disclosure provides a method for adjusting an intelligent multifunctional clamp, the multifunctional clamp including a clamping device, an airtight detection device, a processor, and a controller, the method including: controlling the clamping device to fix the workpiece; performing airtight detection on the clamping device through the airtight detection device to determine an airtight detection result, and sending the airtight detection result to the processor; receiving an airtight detection result; determining a target operating parameter of the clamping device based on the air tightness detection result; and determining a first control instruction based on the target working parameter, and sending the first control instruction to the controller so that the controller can adjust the clamping device based on the first control instruction.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic illustration of an application scenario of an intelligent multi-function fixture according to some embodiments of the present description;

FIG. 2 is an exemplary device block diagram of a smart multifunctional clamp according to some embodiments of the present description;

FIG. 3 is an exemplary flow chart of a method of adjustment of a smart multifunctional clamp according to some embodiments of the present description;

FIG. 4 is an exemplary flow chart for determining initial operating parameters according to some embodiments of the present description;

FIG. 5 is an exemplary schematic illustration of determining target operating parameters according to some embodiments of the present disclosure;

fig. 6 is another exemplary flow chart of a method of adjusting a smart multifunctional clamp according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

Fig. 1 is a schematic view of an application scenario of an intelligent multifunctional clamp according to some embodiments of the present disclosure.

In some embodiments, intelligent multi-function jigs (referred to simply as jigs) may be used in a variety of applications in a lathe machining process. In some embodiments, the types of clamps may include, but are not limited to, welding clamps, inspection clamps, assembly clamps, machine tool clamps, and the like. In some embodiments, in order to achieve the surface energy of the workpiece to the specifications of the dimensions, geometry, and mutual positioning accuracy with other surfaces, the workpiece must be held (positioned) and clamped (clamped) by a clamp before machining.

In some application scenarios, the application scenario 100 of the intelligent multi-function clamp may include a work piece 110, a collection device 120, a network 130, a processor 140, a terminal device 150, and a storage device 160. In some embodiments, components in the application scenario 100 of the smart multifunctional clamp may be connected and/or communicate with each other via a network 130 (e.g., a wireless connection, a wired connection, or a combination thereof). For example, processor 140 may be connected to storage device 160 via network 130. As another example, terminal device 150 may be coupled to processor 140, storage device 160, etc. via network 130.

The workpiece 110 may refer to a part to be machined that is fixed to a fixture. In some embodiments, the work piece 110 may include, but is not limited to, automotive parts, motorcycle parts, and the like. In some embodiments, the workpiece 110 may be secured in a clamping device of a clamp, see fig. 2 and its associated description for further description of the clamping device. In some embodiments, the clamping force used to secure the workpiece 110 is related to workpiece information of the workpiece while the clamp is securing the workpiece 110. For example, the clamping force of the fixed workpiece 110 may be related to workpiece information such as the type, material, size, type, etc. of the workpiece.

The acquisition device 120 may be used to acquire data information related to the workpiece 110. In some embodiments, acquisition device 120 may include, but is not limited to, an image acquisition device 120-1, a weight acquisition device 120-2, a pressure sensor 120-3, and the like. The image acquisition device 120-1 may be used to acquire a workpiece image of the workpiece 110 in real time. In some embodiments, the image capture device 120-1 may include one or more of a normal camera, a high-speed camera, a multi-mode camera (e.g., a camera configured with a high-speed camera mode and a normal camera mode), and so forth. The weight acquisition device 120-2 may be used to acquire weight data of the workpiece 110. In some embodiments, weight acquisition device 120-2 may include a photoelectric weight sensor, a hydraulic weight sensor, a capacitive weight sensor, an electromagnetic force weight sensor, and the like. The pressure sensor 120-3 is used to acquire pressure data or the like of the workpiece 110 held by the clamping device. In some embodiments, the related data information acquired by the acquisition device 120 may be transmitted to the processor 140, the terminal device 150, and/or the storage device 160 for corresponding processing. For example, the related data information acquired by the acquisition device 120 may be transmitted to the terminal device 150 for display, so that the user can acquire the related condition of the workpiece 110.

The network 130 may include any suitable network capable of facilitating the exchange of information and/or data of the application scenario 100 of the smart multifunctional clamp. In some embodiments, information and/or data may be sent between one or more components (e.g., acquisition device 120, processor 140, terminal device 150, and/or storage device 160) in application scenario 100 of the smart multifunctional clamp to another component in application scenario 100 of the smart multifunctional clamp via network 130. For example, the processor 140 may receive the work piece data of the work piece 110 obtained by the acquisition device 120 via the network 130. In some embodiments, the network 130 may be any one or more of a wired network or a wireless network. In some embodiments, network 130 may include one or more network access points. For example, the network 130 may include wired or wireless network access points, such as base stations and/or network switching points, through which one or more components of the application scenario 100 of the smart multifunctional clamp may connect to the network 130 to exchange data and/or information. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies.

The processor 140 may be used to process data and/or information from at least one component of the application scenario 100 of the smart multifunctional clamp or an external data source (e.g., a cloud data center). For example, the processor 140 may receive, via the network 130, workpiece data acquired by the acquisition device 120 from the workpiece 110. Accordingly, the processor 140 may process the workpiece data to obtain workpiece information, such as shape, size, weight, etc., of the workpiece 110 and determine initial operating parameters of the clamping device based on the workpiece information of the workpiece 110. For another example, processor 140 may receive process monitoring data acquired by acquisition device 120 via network 130. Accordingly, the processor 140 may process the process monitoring data to determine a target operating parameter for the clamping device. In some embodiments, the processor 140 may be a single server or a group of servers. The server farm may be centralized or distributed. In some embodiments, the processor 140 may be local or remote. For example, processor 140 may access information and/or data from acquisition device 120, terminal device 150, and/or storage device 160 via network 130. As another example, processor 140 may be directly connected to acquisition device 120, terminal device 150, and/or storage device 160 to access information and/or data.

In some embodiments, the processor 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.

Terminal device 150 may refer to one or more terminal devices or software used by a user. In some embodiments, terminal device 150 may include, but is not limited to, one or any combination of mobile device 150-1, tablet computer 150-2, laptop computer 150-3, and the like, as well as other input and/or output enabled devices. In some embodiments, a user may view information and/or input data and/or instructions through terminal device 150. In some embodiments, terminal device 150 may be in communication and/or connected with acquisition device 120, processor 140, and/or storage device 160. For example, a user may send related instructions to the processor 140 via the terminal device 150 to exercise control over the intelligent multi-function clamp. For another example, the user may receive data and/or information related to the workpiece 110 via the terminal device 150, wherein the data and/or information related to the workpiece 110 may include workpiece data, process monitoring data, and the like. For another example, the terminal device 150 may also be configured to receive and display the processing results (e.g., the target operating parameters of the smart multifunctional clamp, etc.) of the processor 140. The above examples are only intended to illustrate the broad scope of the terminal device 150 devices and not to limit the scope thereof.

Storage 160 may store data and/or instructions. In some embodiments, the storage device 160 may store data and/or instructions that the processor 140 uses to execute or use to perform the exemplary methods described in this specification. In some embodiments, the storage device 160 may be connected to the network 130 to communicate with one or more components of the application scenario 100 of the smart multifunctional clamp (e.g., components of the acquisition device 120 and/or the processor 140). In some embodiments, the storage device 160 may be part of the processor 140. In some embodiments, storage device 160 may comprise mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. In some embodiments, storage device 160 may be implemented on a cloud platform.

It should be noted that the application scenario 100 of the intelligent multi-function clamp is provided for illustrative purposes only and is not intended to limit the scope of the present description. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario may also include a database. As another example, application scenarios may be implemented on other devices to implement similar or different functionality. However, variations and modifications do not depart from the scope of the present description.

FIG. 2 is an exemplary device block diagram of a smart multifunctional clamp according to some embodiments of the present description.

As shown in fig. 2, in some embodiments, the smart multifunctional clamp 200 may include a work piece 210, a collection device 220, a processor 230, a controller 240, a clamping device 250, an air tightness detection device 260, a communication device 270, a water blowing device 280, and the like.

The workpiece 210 may refer to a part to be machined that is fixed to a fixture. In some embodiments, the work piece 210 may be clamped by the clamping device 250.

The acquisition device 220 may be used to acquire data information related to the work piece 210. For example, the acquisition device 220 may be used to acquire work piece data, work piece images, weight data, pressure data, etc. for the work piece 210.

The processor 230 is used for processing related data or transmitting/receiving related instruction information, etc. For further description of the work piece, the collection device, the processor, see fig. 1 and the related description thereof, which are not repeated here.

The controller 240 may be used to control the operation of the associated devices. In some embodiments, the controller 240 may receive instructions (e.g., a first control instruction or a second control instruction) from the processor 230 to control the clamping device 250, the air-tightness detecting device 260, and the water blowing device 280. For example, the controller 240 may adjust an operating parameter of the clamping device 250 based on the first control command. For example, the controller 240 may adjust the detection time for the airtight detection means 260 to detect the airtight of the clamping means 250. For another example, the controller 240 may adjust a time point when the water blowing device 280 blows water based on the second control command.

The clamping device 250 may be used to clamp the workpiece 210 for workpiece fixation. In some embodiments, the clamping device 250 adjusts the operating parameters based on the current conditions of the workpiece 210 to ensure that the workpiece 210 remains stationary during processing. In some embodiments, the workpiece information of the workpiece 210 may change as the machining process proceeds, and accordingly, the operating parameters of the clamping device 250 may need to be adjusted accordingly. In some embodiments, the clamping device 250 may adjust its operating parameters under the control of the first control command issued by the processor 230. For more description of adjusting the operating parameters of the clamping device 250, see fig. 3, 5 and their associated description.

The air tightness detecting device 260 may be used to perform air tightness detection on the air tightness when the clamping device 250 fixes the workpiece 210 to determine the air tightness detection result. In some embodiments, the air tightness detection device 260 may send the air tightness detection result to the processor 230, so that the processor 230 determines whether to adjust the operation parameters of the clamping device 250 according to the air tightness detection result. In some embodiments, the airtight detection apparatus 260 may adjust the detection time for which the airtight detection is performed under the control of the controller 240. For more description of adjusting the detection time of the airtight detecting device 260, see fig. 6 and its related description.

The communication device 270 may be connected to a network to communicate with monitoring devices on the production line so that the processor 230 may obtain production monitoring data of the production line based on the communication device 270. In some embodiments, the communication device 270 may also communicate with other intelligent multi-function clamp devices and/or data platforms (e.g., cloud data centers) to receive/transmit related data information and to transmit the received related data information to the processor 230 for data processing.

The water-blowing device 280 may be used to clean up debris generated during processing of the work piece 210. In some embodiments, the water blowing device 280 can spray water to clean chips and scrap iron generated by the workpiece 210 during the processing process, so as to avoid influencing the processing process, such as avoiding the chips and scrap iron from scratching the workpiece. In some embodiments, the water-blowing device 280 may adjust its water-blowing time under the control of the second control instruction issued by the processor 230. For more description of adjusting the water blowing time of the water blowing device 280, see fig. 6 and its related description.

Fig. 3 is an exemplary flow chart of a method of adjusting a smart multifunctional clamp according to some embodiments of the present description. In some embodiments, the process 300 may be performed by the processor 140 or the processor 230. For example, the flow 300 may be stored in a storage device (e.g., the storage device 160) in the form of a program or instructions that, when executed by the processor 140 or the processor 230, may implement the flow 300. The operational schematic of flow 300 presented below is illustrative. In some embodiments, the process may be accomplished with one or more additional operations not described above and/or one or more operations not discussed. In addition, the order in which the operations of flow 300 are illustrated in FIG. 3 and described below is not limiting.

Step 310, controlling the clamping device to fix the workpiece.

The clamping means may refer to the relevant parts of the clamp for fixing the work piece. In some embodiments, the clamping device may include a pneumatic clamping device, a hydraulic clamping device, or the like. In some embodiments, the clamping device may be used to clamp a workpiece to effect workpiece securement to prevent displacement or vibration of the workpiece during processing. For further description of the clamping device, see fig. 2 and its associated description, which will not be repeated here.

The workpiece may refer to a part to be machined that is fixed to a fixture. In some embodiments, the work piece may be a single piece, such as a gear ring, pump cover, tank cover, etc.; the work piece may also be a fixed combination of parts such as a turbine housing, pump body, tank, bracket, etc. For further description of the work piece, see fig. 1 and its associated description, which are not repeated here.

In some embodiments, the workpiece may also be positioned to be placed in the correct position in the clamp before the clamping device is controlled to secure the workpiece. In some embodiments, the workpiece may be positioned using a positioning element.

In some embodiments, the work piece may be secured by manually positioning the work piece and controlling the clamping device. In some embodiments, the controller may control the clamping device to fix the workpiece according to a clamping manner of the clamp. For example, when the clamping mode is pneumatic clamping, the corresponding clamping device is a pneumatic clamping device, and accordingly, the clamping device can be pushed by compressed air as a power source to clamp the workpiece. For example, when the clamping mode is hydraulic clamping, the corresponding clamping device is a hydraulic clamping device, and accordingly, the clamping device can be pushed to clamp the workpiece by taking the hydraulic pressure as a power source.

At step 320, the clamping device is hermetically tested by the hermetic testing device to determine a hermetic test result, and the hermetic test result is sent to the processor.

The air tightness detecting device may refer to a relevant part of the clamp for detecting air tightness when the clamping device fixes the workpiece. In some embodiments, the air tightness detection device may comprise an air tightness detector, a barometer, or the like. In some embodiments, the air-tight detection device may be used to detect whether a work piece is in place or whether loosening of the work piece occurs during the machining process. For further description of the air tightness detection device, see fig. 2 and the related description thereof, which are not repeated here.

The tightness test may refer to a related test of tightness between the work piece and the clamping device. For example, the air-tight test may detect if the work piece is in place or if the work piece is loose during the machining process.

The airtight detection result may refer to a result after the airtight detection of the clamping device, wherein the airtight detection result may include "loosening occurs", "loosening does not occur", and the like. In some embodiments, when the air tightness test result is "no looseness", it means that the clamping device is clamped in place or the workpiece is not loosened, etc.; when the air tightness detection result is "loosening", the clamping device cannot clamp in place or the workpiece is loosened.

In some embodiments, the airtight detection device may be configured to introduce a gas at a certain pressure into the closed workpiece cavity, simultaneously introduce a gas at the same pressure into a standard tank, and allow the gas to stand for a period of time, and observe a pressure difference between the pressure in the standard tank and the pressure in the workpiece, so as to perform airtight detection. In some embodiments, the airtight detection device may further introduce a gas with a certain pressure into the airtight cavity of the workpiece, and after a period of time, the flowmeter starts to measure and keep continuously supplying gas, and observe the flow of the gas for a certain period of time, and determine whether the leakage amount meets the requirement according to the flow, so as to perform airtight detection.

In some embodiments, the air tightness detection device may acquire a current pressure value of the clamping device for fixing the workpiece, and perform air tightness detection based on the current pressure value and the pressure threshold value to determine an air tightness detection result.

When the clamping device is tightly attached to the workpiece, a certain pressure value exists between the clamping device and the workpiece. Accordingly, the current pressure value may refer to the pressure value of the currently detected clamping device when the workpiece is fixed, for example, the current pressure value of the clamping device may be 130Pa. In some embodiments, the current pressure value of the clamping device may be obtained by a pressure gauge.

It will be appreciated that when the clamping device clamps the workpiece in place, the corresponding pressure value is greater; when the clamping device is not used for clamping a workpiece in place, the corresponding pressure value is smaller. Accordingly, the pressure threshold may be used to determine whether the clamping device is clamping the workpiece in place. For example, the pressure threshold may be 150Pa.

In some embodiments, the air tightness detection device may determine the air tightness detection result according to a relation of the current pressure value and the pressure threshold value. In some embodiments, the air tightness detection device may also send detection data (e.g., a current pressure value) to the processor, so that the processor determines an air tightness detection result according to the relationship between the current pressure value and the pressure threshold value.

In some embodiments, when the current pressure value is greater than the pressure threshold, the clamping device clamps the workpiece in place, and accordingly, the air tightness test may be "no loosening occurs". When the current pressure value is smaller than the pressure threshold value, the clamping device cannot clamp the workpiece in place, and accordingly, the airtight detection result can be "loosening". For example, the current pressure value of the workpiece a is 130Pa, the current pressure value of the workpiece B is 200Pa, the pressure threshold value corresponding to the workpiece a is 150Pa, and the pressure corresponding to the workpiece B is 180Pa, the air tightness detection result of the workpiece a may be "loosening occurrence", and the air tightness detection result of the workpiece B may be "loosening non-occurrence".

In some embodiments, the pressure threshold is different for different workpieces. In some embodiments, the corresponding pressure threshold may be determined from workpiece information of the workpiece. For example, after determining the workpiece information for a workpiece, the corresponding pressure threshold for the same or similar workpiece may be obtained via networking.

In some embodiments, the controller may control the air tightness detection device to perform air tightness detection on the clamping device based on a preset interval time. The preset interval time may be a system default value, an empirical value, an artificial preset value, or any combination thereof, and may be set according to an actual requirement, which is not limited in this specification. For example, if the preset interval time is 5mins, the clamping device can be subjected to airtight detection every 5 mins.

In some embodiments, the controller may adjust the preset interval time according to the air tightness detection result, for example, if the air tightness detection result is "loose", the preset interval time may be appropriately shortened; the airtight detection result is "no looseness", and the preset interval time can be maintained or prolonged appropriately.

In some embodiments, the controller may issue an alarm to alert in response to the air tightness detection result being a loosening.

The alarm may refer to information that alerts the clamping device that clamping is not in place, for example, the alarm may be sounding (e.g., "siren"), emitting light (e.g., "red"), etc.

In some embodiments of the specification, when the loosening of a workpiece in the machining process is detected, an alarm can be sent out in time to remind related personnel, the related personnel can take corresponding measures according to the alarm, and the conditions of machining failure, deformation of the workpiece and the like caused by the loosening of the workpiece are effectively avoided.

And 330, receiving the airtight detection result.

In some embodiments, after the airtight detection means determines the airtight detection results, the airtight detection results may be sent to the processor, and the processor may receive the airtight detection results.

Step 340, determining a target operating parameter of the clamping device based on the air tightness detection result.

The working parameters may refer to parameter data of the clamping device when the clamping device is holding the workpiece. In some embodiments, the operating parameters may include a pressure value of the clamping device, an opening width of the clamping device, and the like. In some embodiments, the operating parameters of different workpieces may be different. For example, when the work a is a large work (e.g., a turbine), and the work B is a small work (e.g., a ring gear), the clamping device applies a larger pressure value and a larger opening width to the work a than the work B.

In some embodiments, the pressure value and the opening width, etc. at which the clamping device fixes the workpiece may change as the machining process of the workpiece proceeds, and accordingly, the working parameters of the workpiece may change as the machining process proceeds. For example, the opening width of the workpiece a may be larger in the early processing stage than in the later processing stage, and the pressure value may be larger in the early processing stage than in the later processing stage.

The target operating parameter may refer to an adjusted operating parameter corresponding to the clamping device when it is determined that adjustment of the clamping device is required to ensure that the clamping device clamps the workpiece in place. It will be appreciated that the workpiece is reduced in size during processing in the production line due to cutting, grinding or the like, and the clamping angle, opening width, clamping force or the like of the clamping device needs to be adjusted to a suitable target value. Correspondingly, the target working parameter may be a working parameter of the clamping device after corresponding adjustment.

In some embodiments, the processor may obtain process monitoring data in response to the air tightness detection result being that looseness occurs, and process the process monitoring data based on the parameter adjustment model to determine a target operating parameter of the clamping device. For more description of determining the target operating parameters based on the parameter adjustment model, see fig. 5 and its associated description.

Step 350, determining a first control command based on the target working parameter, and sending the first control command to the controller, so that the controller performs an adjustment operation on the clamping device based on the first control command.

The first control command refers to a corresponding control command generated according to the target working parameter and can be used for triggering corresponding adjustment operation of the clamping device. For example, the first control command may be to adjust the pressure value of the clamping device to 300Pa or the like.

In some embodiments, the processor may determine the first control instruction in a variety of ways based on the target operating parameter. For example, the processor may generate the first control instruction corresponding to the target operating parameter based on an instruction generator, an instruction signal generating device, a signal converter, a single chip microcomputer, or any other possible manner or technical means in the prior art, which is not limited in this specification.

The adjustment operation refers to an operation of adjusting the clamping device according to the first control instruction, for example, the actual working parameter is that the pressure value of the clamping device is 180Pa, the opening width of the clamping device is 18cm, the target working parameter is that the pressure value of the clamping device is 200Pa, the opening width of the clamping device is 15cm, the adjustment operation may be that the pressure value of the clamping device is adjusted to be 20Pa, the opening width is reduced by 3cm, and the like.

In some embodiments, the processor may send a first control instruction to the controller, and the controller may control the clamping device to perform the adjustment operation based on the first control instruction. For example, when it is determined that the target operating parameter is that the pressure value of the clamping device is 200Pa and the opening width of the clamping device is 15cm, the processor may send a corresponding first control instruction to the controller, and the controller may control the clamping device to adjust the pressure value to 200Pa and the opening width to 15cm based on the first control instruction.

In some embodiments of the specification, the airtight detection result is determined by performing airtight detection on the clamping device, and the adjustment parameters of the clamping device are determined according to the airtight detection result, so that the clamping device can be adjusted in real time in the processing process to fix a workpiece, and the situations that the workpiece is loose, is not clamped in place in the processing process and the like are effectively avoided.

FIG. 4 is an exemplary flow chart for determining initial operating parameters according to some embodiments of the present description. In some embodiments, the process 400 may be performed by the processor 140 or the processor 230. For example, the flow 400 may be stored in a storage device (e.g., the storage device 160) in the form of a program or instructions that, when executed by the processor 140 or the processor 230, may implement the flow 400. The operational schematic of flow 400 presented below is illustrative. In some embodiments, the process may be accomplished with one or more additional operations not described above and/or one or more operations not discussed. In addition, the order in which the operations of flowchart 400 are illustrated in FIG. 4 and described below is not limiting.

In some embodiments, prior to machining the fixed workpiece, initial operating parameters corresponding to the initially fixed workpiece by the clamping device may be determined so that the workpiece may be clamped in place prior to machining. The initial working parameters refer to working data corresponding to the clamping device when the workpiece is initially fixed in the clamping device and before the workpiece is machined. The determination of the initial operating parameters of the clamping device will be exemplified below by steps 410-430.

In step 410, the workpiece data is acquired by the acquisition device and sent to the processor.

The acquisition device can be used for acquiring relevant data information of the workpiece. In some embodiments, the acquisition device may include, but is not limited to, an image acquisition device, a weight acquisition device, a pressure sensor, and the like. For further description of the acquisition device, see fig. 1 and its associated description, which are not repeated here.

The workpiece data may refer to data reflecting the relevant conditions of the workpiece prior to the workpiece being processed. For example, the workpiece data may include workpiece images, weight data, size data, shape data, texture data, and the like of the workpiece. Wherein the workpiece image may refer to an image related to the workpiece. In some embodiments, the workpiece image may be images taken from multiple locations, multiple angles. For example, the workpiece image may be an image captured directly in front of, directly behind, obliquely in front of, obliquely behind, directly above, directly below, or the like of the workpiece. For example, the workpiece image may be an image captured by a camera at angles of 180 °, 270 °, 360 °, or the like.

In some embodiments, the work piece data may be acquired by an acquisition device.

In some embodiments, the weight data of the work piece may be obtained by a weight acquisition device. In some embodiments, a workpiece image of the workpiece may be acquired by an image acquisition device. For more description of the weight acquisition device, the image acquisition device see fig. 1 and its related description.

In some embodiments, the acquisition device may further comprise an ultrasonic sensor, a visual sensor, an infrared sensor or a laser sensor for acquiring size data, shape data and/or texture data of the work piece, etc.

Step 420, determining workpiece information for the workpiece based on the workpiece data.

The workpiece information of the workpiece may refer to data information reflecting the relevant condition of the workpiece. In some embodiments, the workpiece information for the workpiece may include weight, size, shape, material, type, and the like. For example, the workpiece information of the workpiece a may be 30g in weight, 20cm by 50cm in size, rectangular parallelepiped in shape, resin in material, box cover in type, and the like.

In some embodiments, the processor may directly determine the workpiece information of the workpiece based on the workpiece data. For example, the processor may directly determine the weight of the workpiece based on the weight data acquired by the weight acquisition device.

In some embodiments, the processor may process the workpiece data to determine workpiece information for the workpiece. In some embodiments, the processor may process the workpiece image based on the recognition model to determine workpiece information for the workpiece. The recognition model may refer to a machine learning model which is trained in advance and is used for determining workpiece information of a workpiece. In some embodiments, the recognition model may be a recurrent neural network model (Recurrent Neural Network, RNN).

In some embodiments, the processor may also perform image recognition on the image data of the workpiece using an image recognition algorithm to determine the size, shape, and/or texture of the workpiece, etc. By way of example, the image recognition algorithm may include Dijkstra's algorithm, bellman-Ford algorithm, kruskal algorithm, etc., which is not limited in this specification.

Step 430, determining initial operating parameters of the clamping device based on the workpiece information.

In some embodiments, the initial operating parameters of the clamping device may be determined manually from the workpiece information. For example, after determining the workpiece information, the initial operating parameters of the clamping device may be empirically determined manually.

In some embodiments, the processor may construct a workpiece vector based on the workpiece information, determine vector distances between the workpiece vector and the at least one history vector, respectively. Further, the processor may determine a historical vector having a vector distance less than a distance threshold as the target vector and determine a historical operating parameter corresponding to the target vector as the initial operating parameter of the clamping device.

A workpiece vector may refer to a set of features that digitally reflect workpiece information of a workpiece. For example, the workpiece vector may be expressed as (a, b, c, d, e), where a represents the type of workpiece, b represents the size of the workpiece, c represents the shape of the workpiece, d represents the material of the workpiece, and e represents the type of workpiece.

In some embodiments, the processor may determine the workpiece vector according to various methods or algorithms. For example, the processor may determine a work vector of work information based on a bag of words (BoW) model. In some embodiments, the processor may determine a work vector of the work information based on a word vector (word 2 vec) method. In some embodiments, the processor may determine the workpiece vector of the workpiece information based on a Scale Invariant Feature Transform (SIFT) method or an accelerated robust feature (SURF) method.

The history vector is a vector constructed according to the workpiece information of the corresponding workpiece when the clamping device clamps the workpiece in place. Correspondingly, the workpiece corresponding to the history vector is clamped in place by the clamping device at the moment, and the working parameter corresponding to the clamping device is the history working parameter corresponding to the history vector.

In some embodiments, the processor may determine the vector distance between the workpiece vector and the at least one history vector, respectively, by calculation. Methods for calculating vector distances may include, but are not limited to, euclidean distance, manhattan distance, chebyshev distance, mahalanobis distance, and the like.

In some embodiments, the processor may determine a history vector having a vector distance less than a distance threshold as the target vector. The distance threshold may be a system default value, an empirical value, an artificial preset value, or any combination thereof, and may be set according to actual requirements, which is not limited in this specification. The target vector is a history vector with a pointing distance smaller than a distance threshold, for example, history vector a is smaller than the distance threshold, and history vector B is larger than the distance threshold, and then it may be determined that history vector a is the target vector. Further, the processor may determine a historical operating parameter corresponding to the target vector as an initial operating parameter of the clamping device.

In some embodiments of the present disclosure, by determining the initial operating parameters of the clamping device, the clamping device may be enabled to fix the workpiece before the machining starts, so as to ensure that the subsequent machining process is performed smoothly.

FIG. 5 is an exemplary schematic diagram illustrating determining target operating parameters according to some embodiments of the present disclosure.

In some embodiments, the processor may obtain process monitoring data 510 in response to the air tightness detection result being a loosening.

The process monitoring data 510 may refer to monitoring data of a workpiece during processing. In some embodiments, the process monitoring data 510 may include weight monitoring data 511, image monitoring data 512, and pressure monitoring data 513. The weight monitoring data 511 may refer to real-time monitoring data of the weight of the workpiece during the machining process, the image monitoring data 512 may refer to an image of the workpiece obtained in real time during the machining process, and the pressure monitoring data 513 may refer to a pressure value of the clamping device obtained in real time during the machining process. In some embodiments, the process monitoring data 510 may be acquired by an acquisition device. For example, the weight monitoring data 511 of the workpiece may be acquired by the weight acquisition device, and the image monitoring data 512 of the workpiece may be acquired by the image acquisition device; the pressure monitoring data 513 of the workpiece may also be acquired by a pressure sensor or the like. For more description of the acquisition device see fig. 1 and its associated description.

In some embodiments, the processor may process the process monitoring data 510 based on the parameter adjustment model 520 to determine a target operating parameter 540 for the clamping device.

Parameter adjustment model 520 may refer to a model for determining a target operating parameter. In some embodiments, the parameter adjustment model 520 may be a machine learning model. For example, the scrap iron risk model may be a deep neural network (Deep Neural Networks, DNN), a convolutional neural network (Convolutional Neural Networks, CNN), a recurrent neural network (Recurrent Neural Network, RNN), or the like.

In some embodiments, parameter adjustment model 520 may include an embedding layer 521 and a result output layer 522.

The embedded layer 521 may be used to extract workpiece features, the input of which may be image monitoring data 512. The output of the embedded layer 521 may be a workpiece feature. In some embodiments, the model type of the embedded layer 521 may be CNN.

The resulting output layer 522 may be used to determine the target operating parameters of the clamping device based on the above-described feature vectors, the inputs of which may include the output of the embedded layer 521, the weight monitoring data 511, and the pressure differential 530. Wherein the pressure difference 530 is the difference between the pressure monitoring data 513 and the pressure threshold. For more description of the pressure threshold, see step 320 and its associated description. The output of the resulting output layer 522 may be the target operating parameter of the clamping device. In some embodiments, the model type of the result output layer 522 may be DNN.

In some embodiments, the target operating parameter 540 output by the parameter adjustment model 520 may be a parameter vector, where the values of the elements in the parameter vector are the parameter values corresponding to the operating parameters of the clamping device.

In some embodiments, the parameter adjustment module may be derived through joint training of the embedding layer and the result output layer. For example, inputting training samples, i.e. sample image monitoring data of a sample clamping device, into the embedded layer to obtain workpiece features output by the embedded layer; and then taking the output of the embedded layer as the input of the result output layer to obtain the target working parameters output by the result output layer. The label is a sample target working parameter of the sample clamping device, and in the training process, a loss function is established based on the output of the label and the result output layer to update the parameters of the model.

In some embodiments, the training samples may be obtained based on historical data, which may be corresponding relevant data when the clamping device clamps the workpiece in place.

According to the method and the device for obtaining the parameters of the embedded layer, parameters of each layer of the parameter adjustment model are obtained in a combined training mode, the problem that labels are difficult to obtain when the embedded layer is trained independently is solved, the embedded layer can better reflect the characteristics of a machined part of the machined part, and the method and the device are beneficial to determining target working parameters accurately.

According to some embodiments of the specification, the target working parameters of the clamping device are determined through the parameter adjustment model, and the working parameters of the adjusted clamping device can be accurately and efficiently determined, so that the clamping device can be conveniently adjusted, the adjustment efficiency is improved, and meanwhile, the follow-up processing process is ensured to be smoothly carried out.

Fig. 6 is another exemplary flow chart of a method of adjusting a smart multifunctional clamp according to some embodiments of the present description. In some embodiments, the process 600 may be performed by the processor 140 or the processor 230. For example, the flow 600 may be stored in a storage device (e.g., the storage device 160) in the form of a program or instructions that, when executed by the processor 140 or the processor 230, may implement the flow 600. The operational schematic of flow 600 presented below is illustrative. In some embodiments, the process may be accomplished with one or more additional operations not described above and/or one or more operations not discussed. In addition, the order in which the operations of flow 600 are illustrated in FIG. 6 and described below is not limiting.

In step 610, production monitoring data of the production line is obtained based on the communication device communicating with the monitoring device on the production line.

The communication means may refer to means for communicating information on the production line, e.g. the communication means may be a repeater, a hub, a bridge, a switch, a router, etc. See fig. 2 for more description of a communication device and its associated description.

The monitoring device may refer to a device for monitoring the production line. For example, the monitoring device may be a camera, a drone, a listener, or the like.

Production monitoring data may refer to monitoring data related to a production line. For example, the production monitoring image may be image data of the surroundings of the jig during processing, which may include scrap iron accumulated in the vicinity of the jig.

In some embodiments, the monitoring device captures production monitoring data of the production line in real time, and the processor communicates with the monitoring device based on the communication device, and the monitoring device can send the obtained production monitoring data to the processor. In some embodiments, the monitoring device may send the production monitoring data obtained by the monitoring device to the processor at intervals of a preset time. The preset time may be a system default value, an empirical value, an artificial preset value, or any combination thereof, and may be set according to an actual requirement, which is not limited in this specification. For example, the monitoring device may send its acquired production monitoring data to the processor every 30 minutes.

Step 620, determining a water blowing time of the water blowing device based on the production monitoring data and the production line characteristics.

Line characteristics refer to relevant characteristics that may reflect line manufacturing. For example, the line characteristic may be the rotational speed of the lathe. In some embodiments, the line characteristics corresponding to different workpieces may be different. In some embodiments, the production line characteristics may be obtained by a work piece production look-up table.

The water blowing device can refer to a device for cleaning scrap iron on the surface of a workpiece. For example, the water blowing device may include an automatic water blowing device, a key selection water blowing device, and the like. See fig. 2 and the associated description for further description of the water-blowing device.

In some embodiments, the processor may determine the water blowing time based on the production monitoring data and the line characteristics. For example, when it is determined that scraps such as iron shaving exist on the surface of a workpiece and the rotation speed of a lathe is 3r/s according to the production monitoring image, the water blowing time can be determined to be 3 mins/time.

In some embodiments, the processor may determine a scratch risk index for the working surface of the work piece based on the production monitoring data. Further, the processor may predict a point in time when the processing surface reaches the risk index threshold in conjunction with the line characteristics, and adjust a water blowing time of the water blowing device based on the point in time.

The scratch risk refers to the risk that accumulated scrap iron winds around a cutter and rubs or collides with a workpiece to scratch the machined surface of the workpiece.

The scratch risk index refers to the number of risk values that the working surface of the workpiece may be scratched. For example, more iron is piled up, and the scratch risk index is higher; less iron shaving is accumulated, and the scratch risk index is low, such as grade 2. In some embodiments, the scratch risk index may be expressed in terms of a number (e.g., between 0-1), where a larger number indicates a higher risk of scratching the work piece, the more likely the work piece's work surface is scratched or bumped.

In some embodiments, the processor may process the production monitoring data based on the scrap iron risk model to determine a scratch risk index for the machined surface of the workpiece.

The scrap iron risk model may be used to determine a scratch risk index for the machined surface of the workpiece. In some embodiments, the scrap iron risk model is a machine learning model. For example, the scrap iron risk model may be a convolutional neural network model DNN, CNN, RNN, or the like.

In some embodiments, the input of the scrap iron risk model may be production monitoring data and the output may be a scratch risk index for the machined surface of the workpiece.

In some embodiments, the scrap iron risk model may include a feature layer and a judgment layer.

The feature layer may be used to determine the distribution characteristics of the skiving. Wherein the distribution characteristics of the iron chips can be used to indicate the degree of accumulation of the iron chips, the accumulation position of the iron chips, etc. For example, whether or not the stacking position of the scrap iron is on the cutter, etc. The distribution characteristics of the iron pieces can be expressed by vectors (x, y, w, h), where (x, y) represents the center point position of the iron piece region, w represents the width, and h represents the height. In some embodiments, the input of the feature layer may be image monitoring data and the output may be a distribution feature of the skiving. In some embodiments, the model type of the feature layer may be R-CNN.

The judging layer may be used to determine a scratch risk index of the machined surface based on the distribution characteristics of the iron skips, the input may be the output of the characteristic layer, and the output may be the scratch risk index of the machined surface. In some embodiments, the model type of the decision layer may be DNN.

In some embodiments, the scrap iron risk model may be trained from a plurality of labeled training samples. For example, a plurality of training samples with labels can be input into an initial scrap iron risk model, a loss function is constructed through the labels and the results of the initial scrap iron risk model, and parameters of the scrap iron risk model are iteratively updated based on the loss function. And when the loss function of the initial scrap iron risk model meets the preset condition, model training is completed, and a trained scrap iron risk model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold value, etc.

In some embodiments, the scrap iron risk model may be obtained through joint training of the feature layer and the judgment layer. For example, inputting training samples, namely sample image monitoring data of a sample clamping device, into the feature layer to obtain the distribution characteristics of the iron shaving output by the feature layer; and then taking the output of the characteristic layer as the input of the judgment layer to obtain the scratch risk index of the output of the judgment layer. The label is a sample scratch risk index of a sample workpiece in the sample clamping device, and in the training process, a loss function is established based on the output of the label and the judging layer to update parameters of the model.

In some embodiments, the training samples may be obtained based on historical data, which may be corresponding relevant data when the clamping device clamps the workpiece in place. The label can be used for determining the scratch risk index based on the corresponding actual conditions, and can also be determined by manual labeling.

The risk index threshold may refer to a threshold corresponding to the scratch risk index, and when the scratch risk index of the machining surface reaches the risk index threshold, the machining surface may be scratched by the cutter. In some embodiments, the risk index threshold is different for different workpieces. In some embodiments, the risk index threshold may be adjusted according to the processing scenario of different workpieces. For example, for a work piece requiring precision processing and having high safety requirements, the corresponding risk index threshold may be low, and accordingly, the blowing water cleaning may be required when the amount of iron shaving accumulation is low.

In some embodiments, after determining the scratch risk index at the current point in time, the processor may predict a point in time at which the work surface reaches the risk index threshold in conjunction with the line characteristics. It will be appreciated that the rotational speed of the lathe during machining may affect the amount and distribution of iron skiving. For example, if the number of revolutions of the lathe is slow, the accumulation amount of scrap iron is small, and the distribution range of scrap iron is small, the time point when the machined surface reaches the risk index threshold can be predicted to be long.

In some embodiments, the processor may determine scratch risk indices for a plurality of time points based on the skidded risk model and fit the scratch risk indices determined for the plurality of risk time points to predict the time point at which the risk index threshold is reached. Exemplary fitting methods may include, but are not limited to, polynominal-Polynomial fitting, custom effect-Custom function fitting, exonental-EXPONENTIAL function fitting, fitting using matlab curve fitting toolboxes, and the like, which are not limited in this specification.

In some embodiments, the processor may adjust the water blowing time of the water blowing device based on the point in time when the risk index threshold is reached. For example, the water blowing device performs a water blowing operation every 10mins, the water blowing device performs a water blowing operation at 16:40, the determined time point when the risk index threshold is reached is 5mins (i.e. 16:45), and the next water blowing time of the water blowing device is 16:50, and accordingly, the water blowing operation may be performed before the next water blowing time of the water blowing device is adjusted to be 16:45, for example, adjusted to be 16:42, 16:43, etc.

In some embodiments, the processor may also synchronize adjusting the detection time of the air tightness detection device based on the point in time when the risk index threshold is reached. It will be appreciated that as the machining strength of the machining process increases, the amount of scrap iron build-up around the workpiece increases, the workpiece is easily loosened by changes in shape, weight, etc., and accordingly, the air tightness test force can be enhanced to determine whether the clamping device is clamping the workpiece in place. For example, when the time point at which the risk index threshold is reached is 50 minutes, which means that the processing strength is weak, the detection time of the airtight detection device may be maintained or appropriately delayed. When the time point of reaching the risk index threshold is 10 minutes, the processing strength is high, and the detection time of the airtight detection device can be properly advanced.

Step 630, determining a second control instruction based on the water blowing time of the water blowing device, and sending the second control instruction to the controller to control the water blowing operation of the water blowing device.

The second control instruction refers to a corresponding control instruction generated according to the water blowing time and can be used for triggering corresponding adjustment operation of the water blowing device. For example, the second control instruction may be a water blowing time of 5s, a water blowing interval of 10s, or the like.

In some embodiments, the processor may determine the first control instruction in a variety of ways based on a water blowing time of the water blowing device. For example, the processor may generate the second control instruction corresponding to the water blowing time of the water blowing device based on an instruction generator, an instruction signal generating device, a signal converter, a single chip microcomputer, or any other possible manner or technical means in the prior art, which is not limited in this specification.

In some embodiments, the processor may send a second control instruction to the controller, and the controller may control the water blowing operation of the water blowing device based on the second control instruction. For example, when the next time of the water blowing device is adjusted to be 16:42, the processor may send a corresponding second control instruction to the controller, and the controller may control the water blowing device to perform the water blowing operation when the water blowing device is adjusted to be 16:42 based on the second control instruction.

In some embodiments of the present disclosure, the water blowing time of the water blowing device is determined based on the production monitoring data and the production line characteristics, and the scratch of the machined surface of the part can be effectively prevented, the rejection rate of the part is reduced, the cost is reduced, and the machining efficiency is improved by the water blowing time of the water blowing device.

It should be noted that the descriptions above with respect to the flow 300, the flow 400, and the flow 600 are merely for illustration and description, and are not intended to limit the application scope of the present disclosure. Various modifications and changes to the

processes

300, 400, 600 may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. The intelligent multifunctional clamp is characterized by comprising a clamping device, an airtight detection device, a processor, a controller, a water blowing device and a communication device;

the clamping device is used for fixing a workpiece;

the air tightness detection device is used for carrying out air tightness detection on the clamping device so as to determine an air tightness detection result and sending the air tightness detection result to the processor;

the processor is used for receiving the airtight detection result;

determining a target operating parameter of the clamping device based on the air tightness detection result;

determining a first control instruction based on the target working parameter, and sending the first control instruction to the controller;

the processor is further configured to:

Acquiring production monitoring data of a production line based on the communication of the communication device and a monitoring device on the production line;

determining the water blowing time of the water blowing device based on the production monitoring data and the production line characteristics;

determining a second control instruction based on the water blowing time of the water blowing device, and sending the second control instruction to the controller so as to control the water blowing operation of the water blowing device;

wherein, based on the production monitoring data and the production line characteristics, determining the water blowing time of the water blowing device comprises:

processing the production monitoring data based on an iron scrap risk model, determining a scratch risk index of a machining surface of a machined part, wherein the iron scrap risk model is a machine learning model and comprises a characteristic layer and a judgment layer,

the characteristic layer is used for determining the distribution characteristics of the scrap iron;

the judging layer is used for determining a scratch risk index of the processing surface based on the distribution characteristics of the scrap iron;

predicting a time point when the processing surface reaches a risk index threshold value based on the scratch risk index of the processing surface and the production line characteristics;

determining the water blowing time of the water blowing device based on the time point when the processing surface reaches the risk index threshold;

The controller is used for receiving a first control instruction sent by the processor; and

and controlling the adjustment operation of the clamping device based on the first control instruction.

2. The multi-function fixture of claim 1, wherein the multi-function fixture further comprises:

the acquisition device is used for acquiring the workpiece data and sending the workpiece data to the processor;

the processor is configured to:

determining workpiece information of the workpiece based on the workpiece data;

based on the workpiece information, initial operating parameters of the clamping device are determined.

3. The multi-function fixture of claim 1, wherein to determine a target operating parameter of the clamping device based on the air tightness test result, the processor is further configured to:

in response to the airtight detection result that loosening occurs,

acquiring processing monitoring data;

and processing the processing monitoring data based on a parameter adjustment model to determine the target working parameter of the clamping device.

4. The multi-function fixture of claim 1, wherein the controller is further configured to:

and responding to the airtight detection result to be loose, and sending out an alarm to remind.

5. An adjustment method of an intelligent multifunctional clamp, which is characterized in that the multifunctional clamp comprises a clamping device, an airtight detection device, a processor, a controller, a water blowing device and a communication device, and the method comprises the following steps:

controlling the clamping device to fix a workpiece;

performing airtight detection on the clamping device through the airtight detection device to determine an airtight detection result, and sending the airtight detection result to the processor;

receiving the airtight detection result;

determining a first control command based on the target operating parameter and transmitting the first control command to the controller, and,

determining the water blowing time of the water blowing device based on the time point when the processing surface reaches the risk index threshold; and

causing the controller to perform an adjustment operation on the clamping device based on the first control instruction.

6. The method of adjusting a multi-function fixture of claim 5, wherein the multi-function fixture further comprises a collection device, the method further comprising:

acquiring workpiece data through the acquisition device, and sending the workpiece data to the processor;

Determining workpiece information of the workpiece based on the workpiece data;

7. The method of adjusting a multifunctional clamp according to claim 5, wherein determining a target operating parameter of the clamping device based on the air tightness detection result comprises:

in response to the airtight detection result that loosening occurs,

acquiring processing monitoring data;

8. The method of adjusting a multi-function fixture of claim 5, further comprising: