CN109693140A - A kind of intelligent flexible production line and its working method - Google Patents
A kind of intelligent flexible production line and its working method Download PDFInfo
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Abstract
The present invention relates to a kind of intelligent flexible production line and its working methods, this intelligent flexible production line, include: host computer, transportation manipulator and repairing station and several processing stations arranged by manufacturing procedure, is connected between each processing stations by conveying mechanism；Work Piece Verification System Based is provided at conveying mechanism；The workpiece that the Work Piece Verification System Based is suitable for circulating to conveying mechanism detects；The host computer and Work Piece Verification System Based are electrically connected, and after determining that workpiece is unqualified, piece-holder is transported to repairing station by transportation manipulator；After repairing station to be done completes workpiece repairing, piece-holder is transported back conveying mechanism to enter next processing stations by transportation manipulator.
Description
Technical field
The invention belongs to intelligent Manufacturing Technology fields, and in particular, to a kind of intelligent flexible production line and its method.
Background technique
Flexible production line is that more adjustable lathes (process equipment) are tied, and is equipped with automatic conveyer group
At production line.It relies on computer management, and a variety of production models are combined, and accomplishes that object is most so as to reduce production cost
It is used.
But the manufacturing defect of workpiece is difficult to be typically only capable to by timely artificial discovery sometimes during automated production
The desk checking for crossing finishing operations just found, thus cause the waste product of intelligence manufacture to increase, productivity decline the problems such as.
Therefore, based on the above issues, need to design a kind of intelligent flexible production line and its working method.
Summary of the invention
The object of the present invention is to provide a kind of intelligent flexible production line and its working methods.
In order to solve the above-mentioned technical problems, the present invention provides a kind of intelligent flexible production lines, comprising:
Host computer, transportation manipulator and repairing station and several processing stations arranged by manufacturing procedure, respectively process work
It is connected between position by conveying mechanism；
Work Piece Verification System Based is provided at conveying mechanism；
The workpiece that the Work Piece Verification System Based is suitable for circulating to conveying mechanism detects；
The host computer and Work Piece Verification System Based are electrically connected, and after determining that workpiece is unqualified, will by transportation manipulator
Piece-holder is transported to repairing station；
After repairing station to be done completes workpiece repairing, piece-holder is transported back conveying mechanism to enter by transportation manipulator
Next processing stations.
Further, the Work Piece Verification System Based includes: detection station, close to switch, positioning and releasing mechanism, in detection work
The side of position is provided with the background target continuously arranged along flexible production line, is provided with multiple correspondences on background target and currently examines
The mark point of station is surveyed, the other side of detection station is provided with LED area light source matrix, and the LED area light source matrix and host computer are logical
Letter connection, background target and LED area light source matrix collectively form back lighting environment.
Further, the Work Piece Verification System Based further include: area array cameras, and successively communicated to connect with area array cameras
One photo-electric conversion element, optical fiber slip ring, the second photo-electric conversion element, wherein the sensation lens axis and detection station of area array cameras
Circulation direction is vertically arranged, and the image of area array cameras acquisition is after photoelectric conversion, by the second photo-electric conversion element by image
Electric signal is sent to host computer.
Further, the host computer includes: image processing unit, characteristic vector pickup unit, deep neural network unit
And computer control unit；Wherein
Described image processing unit is suitable for receiving the image of detection station on area array cameras acquisition testing station, and to reception
The image arrived carries out resolution scan, obtains the sensitizing range image of current detection station, and goes to sensitizing range image
It makes an uproar, then the sensitizing range image after denoising is sent to characteristic vector pickup unit；
Characteristic vector pickup unit carries out edge detection to sensitizing range image, forms target area, and pass through public affairs respectively
Formula (1) to (3) calculates the edge area A for obtaining target areaE, edge shape factor E and target area mean radius μR, then
In addition the Hu not bending moment of preceding 3 dimension, constituting tool, there are four the feature vectors of the sensitizing range of characteristic variable, to reflect current detection work
The workpiece quality information of position, feature vector are sent to deep neural network unit as input layer；
In above formula, parameter M and N are the marginal point number of target area,Wherein t (x,
It y) is the gray value of each marginal point；Parameter L is the perimeter of target area；It is the marginal point number on target area boundaries with reference to K,
(xk,yk) indicate the pixel coordinate being located on target area boundaries,The center-of-mass coordinate for indicating target area, can be by as follows
Formula is calculated:
Wherein, parameter A indicates the area of sensitizing range, and is suitable for obtaining it when recognizing sensitizing range in image procossing
Size；
Deep neural network unit is based on neural network algorithm and constructs manufacturing defect prediction model, to the image of detection station
Feature vector is trained, learns to identify the manufacturing defect type of the workpiece for measurement on current detection station, and will divide with classification
Class result feeds back to computer control unit；
The computer control unit is suitable for control transportation manipulator and piece-holder is transported to repairing station；
And manufacturing defect type is sent to repairing station, it is repaired with the defect to workpiece.
Another aspect, the present invention also provides a kind of working methods of intelligent flexible production line, comprising:
On-line checking is carried out to the workpiece of conveying mechanism circulation；
If it is determined that the workpiece is transported through repairing after workpiece is unqualified；
After the completion of workpiece is transported and repaired, which is transported into back conveying mechanism.
Further, the working method is suitable for using the intelligent flexible production line.
Beneficial effects of the present invention are as follows:
Intelligent flexible production line of the invention and its working method can accurately judge workpiece in process of production lack
It falls into, and after detecting that workpiece is unqualified and there is manufacturing defect, workpiece can be transported to repairing station repair and return
Workpiece is transported retrogradation producing line, the disqualification rate of work pieces process is effectively reduced, avoids product first by work after workpiece qualification
Process it is unreasonable after also by multiple working process.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the layout top view of intelligent flexible production line of the invention；
Fig. 2 is the control principle block diagram of intelligent flexible production line of the invention.
Transportation manipulator 1, repairing station 2, processing stations 3, conveying mechanism 4, Work Piece Verification System Based 5, workpiece 6, detection work
Position 501, background target 502, LED area light source matrix 503, area array cameras 504.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.On the contrary, this
The embodiment of invention includes all changes fallen within the scope of the spiritual and intension of attached claims, modification and is equal
Object.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", " length ", " width ",
" thickness ", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom" "inner", "outside", " axial direction ",
The orientation or positional relationship of the instructions such as " radial direction ", " circumferential direction " is to be based on the orientation or positional relationship shown in the drawings, merely to just
In description the present invention and simplify description, rather than the device or element of indication or suggestion meaning must have a particular orientation, with
Specific orientation construction and operation, therefore be not considered as limiting the invention.
In addition, term " first ", " second " etc. are used for description purposes only, it is not understood to indicate or imply relatively important
Property.In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, term " connected ", " connection " are answered
It is interpreted broadly, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, it can also be indirectly connected through an intermediary.For the common of this field
For technical staff, the concrete meaning of above-mentioned term in the present invention can be understood with concrete condition.In addition, in description of the invention
In, unless otherwise indicated, the meaning of " plurality " is two or more.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Fig. 1 is the layout top view of intelligent flexible production line of the invention；
Fig. 2 is the control principle block diagram of intelligent flexible production line of the invention.
As depicted in figs. 1 and 2, a kind of intelligent flexible production line is present embodiments provided, comprising: host computer, conveyance
Tool hand 1 and repairing station 2 and several processing stations 3 arranged by manufacturing procedure, pass through conveyer between each processing stations 3
Structure 4 is connected；Work Piece Verification System Based 5 is provided at conveying mechanism 4；The Work Piece Verification System Based 5 is suitable for circulating to conveying mechanism 4
Workpiece 6 detected；The host computer and Work Piece Verification System Based 5 are electrically connected, and after determining that workpiece is unqualified, by transporting
Piece-holder is transported to repairing station 2 by manipulator 1；After repairing station 2 to be done completes workpiece repairing, transportation manipulator 1 will
Piece-holder transports back conveying mechanism 4 to enter next processing stations 3.
Intelligent flexible production line of the invention and its working method can accurately judge workpiece in process of production lack
It falls into, and after detecting that workpiece is unqualified and there is manufacturing defect, workpiece can be transported to repairing station repair and return
Workpiece is transported retrogradation producing line, the disqualification rate of work pieces process is effectively reduced, avoids product first by work after workpiece qualification
Process it is unreasonable after also by multiple working process.
The Work Piece Verification System Based 5 includes: detection station 501, close to switch, positioning and releasing mechanism, in detection station
501 side is provided with the background target 502 continuously arranged along flexible production line, and it is multiple right to be provided on background target 502
Should before detection station 501 mark point, the other side of detection station 501 is provided with LED area light source matrix 503, the face the LED light
Source matrix 503 and host computer communicate to connect, and background target 502 and LED area light source matrix 503 collectively form back lighting environment.
In a preferred embodiment, it is communicated to connect with host computer close to switch, positioning with releasing mechanism, wherein positioning and releasing mechanism
The control instruction of host computer is received, driving workpiece for measurement to move in detection station 501 makes it reach preset detection position, touching
Hair carries out Image Acquisition by host computer starting Work Piece Verification System Based 5 close to switching and sending trigger signal to host computer.
In the present embodiment, the label of multiple corresponding current detection stations 501 can also be provided on background target 502
Point, in a preferred embodiment, the typical manufacturing defect information that the workpiece for measurement based on current detection station 501 is likely to occur,
Mark point position on background target 502 is set, the identification and segmentation in order to subsequent image processing to sensitizing range.In addition,
The other side of detection station 501 is provided with LED area light source matrix 503, and the LED area light source matrix 503 and host computer communicate to connect,
Lighting and extinguishing for the LED area light source matrix 503 in intelligent control the entire production line may be implemented in host computer, transports in workpiece for measurement
When moving the predeterminated position of current detection station 501, host computer sends instructions to the LED area light source of the corresponding detection station 501
Matrix 503, lights it, and background target 502 and LED area light source matrix 503 collectively form back lighting environment, is conducive to be measured
The intensity contrast with background target 502, edge detection when reducing image procossing are time-consuming on the image for workpiece.
Work Piece Verification System Based 5 further includes the area array cameras 504 successively communicated to connect, the first photo-electric conversion element, fiber slide
Ring and the second photo-electric conversion element, wherein when workpiece for measurement moves to preset detection position in detection station 501 and connects
When the shooting central region of nearly area array cameras 504, triggers close to switch and send trigger signal to host computer；Area array cameras 504
Sensation lens axis and detection station 501 direction that circulates be vertically arranged, it is contemplated that in intelligent flexible production line, the fortune of equipment
Station 501 may be detected by, which turning, generates certain vibration, to influence the area array cameras being fixedly connected with detection station 501
504 shooting precision, in order to obtain clear reliable shooting effect, the image that area array cameras 504 of the invention is shot is being sent
To before image processing unit, carried out respectively by the first photo-electric conversion element, optical fiber slip ring and the second photo-electric conversion element
Photoelectric conversion, so that the received electric image signal of host computer tends to be complete and reliable.
In the present embodiment, the host computer includes: image processing unit, characteristic vector pickup unit, depth nerve net
Network unit and computer control unit.
For Work Piece Verification System Based 5, wherein the first photo-electric conversion element and the second photo-electric conversion element be provided with it is identical
The input terminal of quantity and multiple output ends, the quantity of optical fiber slip ring are corresponding to it, each input of the first photo-electric conversion element
End is connect with area array cameras 504 respectively, each output end passes through an optical fiber slip ring and the second photo-electric conversion element respectively
Input terminal connection, each output end of the second photo-electric conversion element are connect with image processing unit.
Image processing unit receives after the image of photoelectric conversion, and in order to improve image processing efficiency, the present invention is first
It first passes through resolution scan to obtain the sensitizing range image of current detection station 501 and carry out image segmentation, then only to obtaining
The sensitizing range obtained carries out denoising and edge detection, traditional image procossing, is all to be denoised to carry out again to whole image
Image dividing processing, the present invention is partitioned into sensitizing range first, then carries out local denoising, can effectively improve image procossing effect
Rate.
Specifically, image processing unit is automatically positioned out the center of multiple mark points on image, determines background
The angle of target 502 and horizontal direction, to calculate the deflection angle between area array cameras 504 and background target 502, image
Processing unit controls the image and carries out resolution scan along deflection angle, to complete the sensitivity to current detection station 501
The identification in region, after identifying the sensitizing range, image processing unit carries out image segmentation operations to the image, to obtain new
Image to be processed, in a preferred embodiment, it is contemplated that the backlight that background target 502 and LED area light source matrix 503 are constituted
Lighting environment, the sensitizing range of image and the contrast of background are higher, and gray value difference is larger, and the present invention is used based on region
Image segmentation algorithm is partitioned into the sensitizing range image.
When being denoised to the sensitizing range image being partitioned into, in order to enable the sensitizing range image has more natural put down
Sliding effect enhances the treatment effect to the random noise of sensitizing range image, and the present invention is using gaussian filtering method to the sensitizing range
Area image is denoised；After obtaining smooth sensitizing range image, characteristic vector pickup unit is to treated sensitizing range
Image further processes.
Specifically, the purpose of characteristic vector pickup unit be the dimensional images information characterized by pixel set is reduced to
Vector set is combined into the low-dimensional image information of feature, in order to computer processing and guarantee deep neural network unit classification
Accuracy；In the present invention, the Flexible Production feature based on intelligence manufacture, using this bullet image feature of shape feature come
Obtain workpiece for measurement intelligence manufacture information, be it is proper, in order to capture the system of workpiece for measurement sensitizing range image comprehensively
Information is made, the present invention considers the external margin information and interior zone information of target area simultaneously, by the edge of target area
Bending moment is not used as characterization sensitizing range image to the Hu of area, the edge shape factor and target area mean radius and preceding 3 dimension
Characteristic variable be input to based on deep neural network unit and in this, as the feature vector of sensitizing range image.
In a preferred embodiment, the edge attributes based on sensitizing range image, characteristic vector pickup unit is first to quick
Sensillary area area image carries out edge detection, obtains target area, and calculates the side for obtaining target area by formula (1)-(3) respectively
Edge area AE, edge shape factor E and target area mean radius μR, bending moment, composition do not have four to the Hu tieed up along with preceding 3
The feature vector of the sensitizing range of a characteristic variable, with the manufacturing qualities such as the processing or the assembly that reflect current production detection platform letter
Breath, feature vector are sent to deep neural network unit as input layer；
In above formula, parameter M and N are the marginal point number of target area,Wherein t (x,
It y) is the gray value of each marginal point；Parameter L is the perimeter of target area, and the Chain-Code-Method that can be used in image processing techniques is counted
It calculates and obtains, be the marginal point number on target area boundaries, (x with reference to Kk,yk) indicate that the pixel being located on target area boundaries is sat
Mark,The center-of-mass coordinate for indicating target area, can be calculated by the following formula:
Wherein, parameter A indicates the area of sensitizing range, and it is big that it is obtained when can recognize sensitizing range in image procossing
It is small.
In addition, Hu not bending moment global characteristics important as image, are not influenced by light, noise, have good several
Why not deform, can effectively describe the more complicated subject image of shape, it is contemplated that the typical manufacturing defect of intelligence manufacture
The efficiency of matter and image procossing chooses preceding 3 dimension Hu not characteristic variable one of of the bending moment as workpiece for measurement sensitizing range image,
Be it is effective, specific calculation can refer to the Normal practice in image processing techniques, will not repeat them here.
In a preferred embodiment, deep neural network unit of the invention, specifically based on the system of deep neural network
Make bug prediction model, and including three-layer neural network, be input layer, hidden layer and output layer respectively, wherein input layer with it is defeated
Layer scale having the same out, input interface of the input layer as manufacturing defect prediction model receive the spy of workpiece for measurement image
Vector is levied, by information coding, reaches hidden layer, using information decoded transform to output layer, the present invention can be using classics
Coding and decoding formula model；Before deep neural network unit formally carries out the manufacturing defect classification of workpiece for measurement, need
Learning training is first carried out, specifically, for the typical manufacturing defect that 501 workpiece for measurement of current detection station is likely to occur, is established
Master sample image library；In a preferred embodiment, master sample image library may include manufacture qualification, manufacturing defect I, manufacture
Four kinds of sample database types such as defect II, manufacturing defect III, in this, as the training sample database of deep neural network；Before being similar to
Image in master sample image library is equally carried out edge detection by the extraction workpiece for measurement feature vector stated, the present invention, and according to
Secondary edge area, edge standard deviation, form factor and the Hu not characteristic variables such as bending moment for extracting image, composing training sample database
Feature vector；Finally, the input layer of deep neural network unit reads the feature vector in training sample database, based on depth mind
Coding and decoding through network unit carry out depth to the corresponding manufacture information of image in each master sample image library
It practises, to obtain the manufacturing defect prediction model of current detection station 501.
After deep neural network unit carries out learning training to training sample database, current detection station 501 can be used to
Workpiece for measurement carries out the classification prediction of manufacturing defect information, to identify the system of the workpiece for measurement in current production detection platform
Defect type is made, and the manufacturing defect information of workpiece for measurement is further transmitted to computer control unit, the computer control
Unit processed is suitable for control transportation manipulator 1 and piece-holder is transported to repairing station 2；And manufacturing defect type is sent to repairing
Station 2 is repaired with the defect to workpiece.
In a preferred embodiment, computer control unit includes PLC controller, and with product testing platform, face battle array phase
Machine 504 and deep neural network unit communicate to connect respectively, and computer control unit is according to the classification of deep neural network unit
As a result indicated manufacturing defect information determines resource needed for repairing the manufacturing defect in conjunction with history Maintenance plan, and
The maintenance plan for repairing the defect is automatically generated for the defect type, defective locations, defect level, maintenance personnel's selection etc.
Slightly, it and forms corresponding work order and is sent to PLC controller, specific maintenance operation is executed by PLC controller, and in history
Addition one maintenance record for being directed to the executive condition is updated in Maintenance plan.
In the present embodiment, processing stations 3 and repairing station 2 are each equipped with corresponding process equipment, and each process equipment is equal
By PC control.
The present embodiment additionally provides a kind of working method of intelligent flexible production line, i.e.,
On-line checking is carried out to the workpiece that conveying mechanism 4 circulates；
If it is determined that the workpiece is transported through repairing after workpiece is unqualified；
After the completion of workpiece is transported and repaired, which is transported into back conveying mechanism 4.
Include: in the step of carrying out on-line checking to workpiece
The manufacturing defect prediction model based on deep neural network is constructed, deep neural network is carried out by sample image
Training study；
Positioning and releasing mechanism drive workpiece for measurement in detection station 501 under the control instruction of computer control module
Movement makes it reach preset detection position, triggers close to switch, and send trigger signal to computer control unit；
Computer control unit sends instruction, LED area light source to LED area light source matrix 503 and Work Piece Verification System Based 5 respectively
Matrix 503 opens illumination, shoots according to a pre-set procedure with parameter, area array cameras 504 to workpiece for measurement, and photoelectricity turns
After changing, the image of generation is sent to image processing unit；
Image processing unit identifies and is partitioned into the sensitizing range of current detection station 501, carries out for the regional area
Image denoising processing, by the sensitizing range, image is sent to characteristic vector pickup unit after denoising；
Characteristic vector pickup unit carries out edge detection to sensitizing range, forms target area, and is obtained respectively by calculating
Target area edge area, the edge shape factor and target area mean radius, in conjunction with the Hu not bending moment of preceding 3 dimension, structure
At tool, there are four the feature vectors of the sensitizing range of characteristic variable；
The diagnosis for being carried out manufacture information to feature vector based on trained deep neural network, is predicted and work to be measured of classifying
The manufacturing defect of part, and classification results are fed back into computer control unit；
Computer control unit is based on classification results and history Maintenance plan information, determines to repair the manufacturing defect
Maintenance policy, send work order to PLC controller, specific maintenance operation is executed by PLC controller.
The manufacturing defect prediction model based on deep neural network is constructed, deep neural network is carried out by sample image
The method of training study further comprises: manufacturing defect prediction model of the building based on deep neural network, the depth nerve
Network is 3 layers of neural network, including input layer, output layer, hidden layer, wherein input layer and output layer rule having the same
Mould；For the typical manufacturing defect that current detection station 501 is likely to occur, establish master sample image library, including manufacture it is qualified,
Four kinds of sample database types such as manufacturing defect I, manufacturing defect II, manufacturing defect III, the training sample as deep neural network
Library；Image in master sample image library is subjected to edge detection, and successively extract the edge area of image, edge standard deviation,
Form factor and Hu not characteristic variables such as bending moment, the feature vector of composing training sample database；Deep learning network unit it is defeated
Enter layer and read feature vector in the training sample database, and the corresponding manufacture of image in each master sample image library is believed
Breath carries out deep learning, to obtain the manufacturing defect prediction model of current detection station 501.
In conclusion the present invention for the first time believes Image Acquisition and the product manufacturing defect of the soft production line of image processing techniques
In breath identification and diagnosis, by the manufacturing defect prediction model constructed based on deep neural network, to intelligent flexible production line
In product carry out real time image collection, processing and feature extraction, the feature vector for obtaining Product Manufacturing Information indicates, thus real
Showed Product Manufacturing Information during intelligence manufacture and can show, defect is diagnosable, maintenance policy can automatically generate, formed and produced
The real-time analysis of the real-time monitoring, defect conclusion of product manufacture, the automatic implementation of maintenance policy organic integral system, to have
Solves one of widely applied factor of existing restriction intelligent Manufacturing Technology to effect；And the present invention is in order to accurately be schemed
As processing result is effectively solved by application resolution scanning technique in several optical fiber slip rings of application and image processing process
It has determined the microvibration bring Image Acquisition precision of the lathe and detection station 501 that are likely to occur in intelligent flexible production line
And the deflection lens problem of area array cameras 504, improve the online accident defect diagnosis of product manufacturing of the invention and identification
Precision；And the present invention is likely to occur on current detection station 501 to reduce the time-consuming of image procossing based on workpiece for measurement
Typical manufacturing defect, pointedly be arranged background target 502 mark point position so that can be direct in postorder image procossing
The sensitizing range image for orienting current detection station 501 carries out image procossing and characteristic variable for this regional area
It extracts and calculates, image processing efficiency is greatly improved, so that On-line Product failure prediction system of the invention and method phase
Processing and the extraction of characteristic variable are carried out to whole image compared with traditional, it appears more in real time and efficiently.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, not to the schematic representation of the term
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiment or examples in can be combined in any suitable manner.
Taking the above-mentioned ideal embodiment according to the present invention as inspiration, through the above description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (6)
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811651017.8A CN109693140B (en) | 2018-12-31 | 2018-12-31 | An intelligent flexible production line and its working method |
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| CN114055181A (en) * | 2021-10-28 | 2022-02-18 | 深圳精匠云创科技有限公司 | Automatic cutter machining, detecting and reworking system and method |
| CN114912747A (en) * | 2022-03-31 | 2022-08-16 | 广州明珞装备股份有限公司 | Welding spot quality monitoring method, system, equipment and storage medium |
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| CN114912747A (en) * | 2022-03-31 | 2022-08-16 | 广州明珞装备股份有限公司 | Welding spot quality monitoring method, system, equipment and storage medium |
| CN115922519A (en) * | 2022-12-21 | 2023-04-07 | 贵州安大航空锻造有限责任公司 | Intelligent processing method and device for workpiece defects, production line management and control system and medium |
| CN115922519B (en) * | 2022-12-21 | 2024-12-13 | 贵州安大航空锻造有限责任公司 | Workpiece defect intelligent processing method, device, production line control system and medium |
| CN115826547A (en) * | 2023-02-21 | 2023-03-21 | 青岛环球重工科技有限公司 | Control system of flexible segment production line |
| CN116653193A (en) * | 2023-05-30 | 2023-08-29 | 绵阳中研磨具有限责任公司 | Tray forming device and method |
| CN116653193B (en) * | 2023-05-30 | 2025-08-26 | 四川中研新材料科技有限责任公司 | Tray forming device and method |
| CN116587043A (en) * | 2023-07-18 | 2023-08-15 | 太仓德纳森机电工程有限公司 | A workpiece conveying system for industrial automation production and processing |
| CN116587043B (en) * | 2023-07-18 | 2023-09-15 | 太仓德纳森机电工程有限公司 | Workpiece conveying system for industrial automatic production and processing |
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