CN111975438A - Machine tool emergency stop method and device based on distance sensor and machine vision - Google Patents

Machine tool emergency stop method and device based on distance sensor and machine vision Download PDF

Info

Publication number
CN111975438A
CN111975438A CN202010847699.0A CN202010847699A CN111975438A CN 111975438 A CN111975438 A CN 111975438A CN 202010847699 A CN202010847699 A CN 202010847699A CN 111975438 A CN111975438 A CN 111975438A
Authority
CN
China
Prior art keywords
jaw chuck
distance
module
emergency stop
foreign
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010847699.0A
Other languages
Chinese (zh)
Inventor
姜衍
杨有松
樊辉
李晓源
沈亚楠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nantong University
Original Assignee
Nantong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nantong University filed Critical Nantong University
Priority to CN202010847699.0A priority Critical patent/CN111975438A/en
Publication of CN111975438A publication Critical patent/CN111975438A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q11/00—Accessories fitted to machine tools for keeping tools or parts of the machine in good working condition or for cooling work; Safety devices specially combined with or arranged in, or specially adapted for use in connection with, machine tools
    • B23Q11/0078—Safety devices protecting the operator, e.g. against accident or noise
    • B23Q11/0092—Safety devices protecting the operator, e.g. against accident or noise actuating braking or stopping means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23B—TURNING; BORING
    • B23B31/00—Chucks; Expansion mandrels; Adaptations thereof for remote control
    • B23B31/02—Chucks
    • B23B31/10—Chucks characterised by the retaining or gripping devices or their immediate operating means
    • B23B31/103—Retention by pivotal elements, e.g. catches, pawls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q11/00—Accessories fitted to machine tools for keeping tools or parts of the machine in good working condition or for cooling work; Safety devices specially combined with or arranged in, or specially adapted for use in connection with, machine tools
    • B23Q11/0078—Safety devices protecting the operator, e.g. against accident or noise
    • B23Q11/0089—Safety devices protecting the operator, e.g. against accident or noise actuating operator protecting means, e.g. closing a cover element, producing an alarm signal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses a machine tool emergency stop method based on a distance sensor and machine vision, which comprises the following steps: before the processing of S1 begins, training and learning different types of foreign body image data by using a machine learning algorithm and acquiring a set of edge points of the three-jaw chuck when no foreign body exists so as to obtain the central position of the three-jaw chuck; s2, after the processing is started, acquiring the current distance measured by the distance sensor and the image of the periphery of the three-jaw chuck; s3, comparing the current distance with a distance threshold value, judging whether foreign matters exist in the safe distance of the three-jaw chuck, if so, entering S5, and if not, entering S4; s4, analyzing the image by using a machine vision algorithm, judging whether foreign matters belonging to the emergency stop category exist in the safety distance of the three-jaw chuck, if so, entering S5, and if not, entering S2; s5 the machine tool immediately alarms and stops working.

Description

Machine tool emergency stop method and device based on distance sensor and machine vision
Technical Field
The invention relates to the technical field of machine tool safety monitoring, in particular to a machine tool emergency stop method and device based on a distance sensor and machine vision.
Background
In machine tool production practice, safety hazards arise from improper operation, particularly in the three-jaw chuck portion of the machine tool. The three-jaw chuck rotates at a high speed, an operator inadvertently causes hands or hairs and the like to approach the three-jaw chuck rotating at the high speed, and the three-jaw chuck rotating at the high speed can injure the whole hands or the head, so that a great safety accident can occur. When a safety accident occurs, people who are injured by twisting cannot stop the machine, and when people around hear the help calling sound and then press the emergency stop switch, casualties are inevitable.
The existing machine tool braking devices comprise: a motor braking device for the broken molybdenum wire of linear cutting machine is composed of current transformer, switch tube and time-base circuit, the phase line of AC power supply passes through the primary coil of current transformer, the secondary coil of current transformer is connected to rectifying and voltage-stabilizing circuit, which is connected to switch tube and time-base circuit, the output of time-base circuit is connected to the input of single-chip computer, the output of single-chip computer is connected to braking circuit of motor, which is composed of switch tube and relay, the normally open contact of relay is connected serially to the phase lines of DC power supply and AC power supply, the motor braking circuit has the advantages that the molybdenum wire running motor running current is detected through the molybdenum wire breakage detection circuit, the molybdenum wire running motor braking circuit can be started in real time when the molybdenum wire is broken, the motor is quickly braked to rotate, and the molybdenum wire is protected from being influenced; the other emergency braking device for machine tool equipment has one DC power source connected serially to the normally opened contact of the emergency stop contactor, the DC power source consists of transformer and rectifier circuit, the transformer has its primary coil connected to the two phase incoming lines of the motor power source and its secondary coil connected to the input contact of the rectifier circuit, and when the emergency stop button is pressed, the two stator windings of the motor are cut off from the AC power source to form loop with the DC power source to generate one static magnetic field in the stator windings, the rotor rotates in the field to produce induced electromotive force and the rotor current and the static magnetic field produce opposite torque to resist the rotor rotating force caused by the inertia of the equipment and brake the rotor braking force to the inertial rotating force of the electromechanical equipment, thereby completing the entire braking process.
The braking process of the machine tool controls a motor braking circuit by detecting internal current, the existing early warning function in the machine tool is used for detecting whether the placement position of a tool deviates or not, whether parts in the machine tool need to be maintained or not and the like at present, and the problem that safety accidents are avoided by accurately controlling the machine tool to suddenly stop in real time according to the peripheral conditions of the three-jaw chuck cannot be solved.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a machine tool emergency stop method and device based on ultrasonic distance sensors and vision, wherein the distance sensors distributed on two sides of a three-jaw chuck are used for detecting the current distance, and meanwhile, whether foreign matters belonging to the emergency stop category exist in the safety distance of the three-jaw chuck is analyzed by combining images, if the current distance of the distance sensors is detected to be smaller than a distance threshold value or the foreign matters belonging to the emergency stop category exist in the safety distance of the three-jaw chuck, an alarm command is sent to an alarm module, and a stop command is sent to a stop module, so that the problem that the machine tool emergency stop cannot be accurately controlled in real time according to the peripheral conditions of the three-jaw chuck is solved, and safety accidents are avoided.
In order to achieve the purpose, the invention adopts the following technical scheme:
a machine tool emergency stop method based on a distance sensor and machine vision comprises the following steps:
s1, before processing, training and learning the foreign body image data of different types by using a machine learning algorithm and acquiring a three-jaw chuck edge point set { C ] when no foreign body existsiTo obtain the center position C of the three-jaw chuck0；
Wherein
Figure BDA0002643640180000021
(C0x,C0y) Indicating the center position C of the three-jaw chuck0(ii) position coordinates of (C)ix,Ciy) Representing a single point C in a three-jaw chuck edge point setiM represents the total number of the edge points of the three-jaw chuck;
s2, after the processing is started, obtaining the current distance h measured by the distance sensor1And an image of the periphery of the three-jaw chuck;
s3 comparing the current distance h1And a distance threshold H0Judging whether foreign matters exist in the safe distance of the three-jaw chuck or not, if so, entering S5, and if not, entering S4;
s4, analyzing the image by using a machine vision algorithm, judging whether a foreign matter belonging to the emergency stop category exists in the safe distance of the three-jaw chuck, if so, entering S5, and if not, entering S2;
s5, the machine tool immediately alarms and stops working.
Further, step S4 includes the steps of:
s41 reading abnormal parameter threshold D by using machine vision algorithm0Preprocessing the image around the three-jaw chuck;
s42, judging whether the foreign matter exists or not by using the difference value of the current image and the image without the foreign matter, if the foreign matter is not detected, executing S2; if the foreign matter is detected to be present, executing S43;
s43, identifying the foreign body type by using the machine learning model trained in the step S1, and executing S2 if the foreign body type does not belong to the emergency stop category and executing S44 if the foreign body type belongs to the emergency stop category;
s44, utilizing Sobel operator or Canny operator to carry out image edge detection, obtaining the current image edge outline and forming an edge point set { G }jRemoving a three-jaw chuck edge point set { C }iThus, a foreign object edge point set G is calculatedkIn which { G }j}＝{Ci}+{Gk}；
S45: calculating the center position C of the three-jaw chuck0And foreign matter edge point set { GkThe distance between the two is used for judging whether foreign matters exist in the safe distance of the three-jaw chuck, and the value is determined by the Euclidean distance between the two:
Figure BDA0002643640180000031
wherein (G)kx,Gky) Representing a single point G in a set of foreign object edge pointskK ∈ [1, n ] of]N represents the total number of foreign matter edge points;
s46, by dividing the Euclidean distance DkAnd a threshold value D0Comparing the parameters, judging whether foreign matters exist in the safe distance of the three-jaw chuck, if Dk＜D0Then there is a foreign object within the safe distance performs S5, if Dk≥D0And k < n, no foreign matter exists within the safe distance to S45, if Dk≥D0And k-n, there is no foreign object within the safety distance to enter S2.
Further, the preprocessing in the step S43 includes gray scale conversion, noise reduction, and enhancement.
Further, the emergency stop category in the step S4 includes hair and fingers.
Further, the machine learning algorithm is a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms.
The invention also provides a machine tool emergency stop device based on the distance sensor and machine vision, which comprises a main control module, a machine vision analysis processing server, an image acquisition module, an alarm module, a stop module and a distance sensor, wherein the image acquisition module, the alarm module, the stop module and the distance sensor are connected with the machine vision analysis processing server;
the image acquisition module is arranged right opposite to the three-jaw chuck of the machine tool and used for acquiring images of the periphery of the three-jaw chuck and sending the images to the machine vision analysis processing server;
an alarm module: the alarm device is used for sending alarm information after receiving the alarm command;
a shutdown module: the system is used for stopping the machine tool after receiving a stop command;
the distance sensor is arranged right above the three-jaw chuck and positioned at two sides of the three-jaw chuck, and is used for measuring the distance between the distance sensor and the nearest barrier and sending the result to the main control module;
the machine vision analysis processing server includes:
a preprocessing module: the three-jaw chuck is used for carrying out gray level conversion, noise reduction and enhancement processing on the acquired image at the periphery of the three-jaw chuck;
a foreign matter detection module: the device is used for detecting whether foreign matters exist around the three-jaw chuck or not and sending the result to the main control module;
a type identification module: the device is used for identifying the type of the foreign matter and sending the result to the main control module;
a safety detection module: calculating Euclidean distance between the center position of the three-jaw chuck and the current foreign matter edge point set, comparing the Euclidean distance with an abnormal parameter threshold value, judging whether foreign matters exist in the safety distance around the three-jaw chuck, and sending the result to the main control module; the center position of the three-jaw chuck is determined by the mean value of edge points before the machine tool starts to process and when no foreign matter exists around the three-jaw chuck, and the current foreign matter edge point set is determined by removing the three-jaw chuck edge point set from the current image edge point set;
the main control module: the device is used for connecting each module and receiving the output results of the foreign matter detection module, the type identification module, the safety detection module and the distance sensor; and if the current distance of the distance sensor is smaller than the distance threshold value or foreign matters belonging to the category of emergency stop exist in the safe distance of the three-jaw chuck, sending an alarm command to the alarm module and sending a stop command to the stop module.
Further, the foreign object detection module judges whether the foreign object exists or not according to a difference value between the current image and the foreign object-free image.
Further, the type identification module identifies the type of the foreign matter through a machine learning model formed by a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms, and acquires the edge of the current image contour by using a Canny operator or a Sobel operator.
The invention has the beneficial effects that:
the current distance is detected by utilizing the distance sensors distributed on two sides of the three-jaw chuck, whether hair or hands exist in the safe distance of the three-jaw chuck is analyzed by combining a machine vision algorithm, if the current distance detected by the distance sensors is smaller than a distance threshold value or whether foreign matters belonging to the category of emergency stop category exist in the safe distance of the three-jaw chuck, an alarm command is sent to the alarm module, and a stop command is sent to the stop module, so that the safe production of the machine tool is realized, and meanwhile, the machine vision algorithm can judge whether the foreign matters belong to the category of emergency stop so as to avoid frequent emergency stop of the machine tool, and the production efficiency is improved on the premise of ensuring the safe production.
Drawings
FIG. 1 is a structural diagram of an emergency stop device of a machine tool according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for stopping a machine tool suddenly according to an embodiment of the present invention;
FIG. 3 is a flow chart of a machine vision algorithm according to an embodiment of the present invention;
FIG. 4 is a block diagram of a control system according to an embodiment of the present invention;
the figures are identified as: the device comprises a 1-three-jaw chuck, a 2-distance sensor and a 3-image acquisition module.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations. . It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
As shown in fig. 2, an embodiment of the present invention provides a method for stopping a machine tool suddenly based on a distance sensor and machine vision, including the following steps:
s1, before processing, training and learning the foreign body image data of different types by using a machine learning algorithm and acquiring a three-jaw chuck edge point set { C ] when no foreign body existsiTo obtain the center position C of the three-jaw chuck0；
Wherein
Figure BDA0002643640180000051
(C0x,C0y) Indicating the center position C of the three-jaw chuck0(ii) position coordinates of (C)ix,Ciy) Representing a single point C in a three-jaw chuck edge point setiPosition ofSetting coordinates, wherein m represents the total number of edge points of the three-jaw chuck;
s2, after the processing is started, obtaining the current distance h measured by the distance sensor1And an image of the periphery of the three-jaw chuck;
s3 comparing the current distance h1And a distance threshold H0Judging whether foreign matters exist in the safe distance of the three-jaw chuck or not, if so, entering S5, and if not, entering S4;
s4, analyzing the image by using a machine vision algorithm, judging whether a foreign matter belonging to the emergency stop category exists in the safe distance of the three-jaw chuck, if so, entering S5, and if not, entering S2;
s5, the machine tool immediately alarms and stops working.
The machine learning algorithm in the step S1 is a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms, and in the step S1, a Sobel operator or a Canny operator is adopted to obtain a three-jaw chuck edge point set { C ] when no foreign body existsi}; the emergency stop category in step S4 includes hair and hands.
As shown in fig. 3, step S4 includes the following steps:
s41 reading abnormal parameter threshold D by using machine vision algorithm0Preprocessing the image around the three-jaw chuck;
s42: judging whether the foreign matter exists or not by using the difference value of the current image and the foreign matter-free image, and if the foreign matter is not detected, executing S2; if the foreign matter is detected to be present, executing S43;
s43, identifying the foreign body type by using the machine learning model trained in the step S1, and executing S2 if the foreign body type does not belong to the emergency stop category and executing S44 if the foreign body type belongs to the emergency stop category;
s44, utilizing Sobel operator or Canny operator to carry out image edge detection, obtaining the current image edge outline and forming an edge point set { G }jRemoving a three-jaw chuck edge point set { C }iThus, a foreign object edge point set G is calculatedkIn which { G }j}＝{Ci}+{Gk}；
S45: calculating the center position C of the three-jaw chuck0And foreign matter edge point set { GkThe distance between the two is used for judging whether foreign matters exist in the safe distance of the three-jaw chuck, and the value is determined by the Euclidean distance between the two:
Figure BDA0002643640180000061
wherein (G)kx,Gky) Representing a single point G in a set of foreign object edge pointskK ∈ [1, n ] of]N represents the total number of foreign matter edge points;
s46, by dividing the Euclidean distance DkAnd a threshold value D0Comparing the parameters, judging whether foreign matters exist in the safe distance of the three-jaw chuck, if Dk＜D0Then there is a foreign object within the safe distance performs S5, if Dk≥D0And k < n, no foreign matter exists within the safe distance to S45, if Dk≥D0And k-n, there is no foreign object within the safety distance to enter S2.
The preprocessing in step S43 includes gradation conversion, noise reduction, and enhancement.
As shown in fig. 1 and fig. 4, based on the above machine tool emergency stop method, an embodiment of the present invention further provides a machine tool emergency stop device based on a distance sensor and machine vision, including a main control module, a machine vision analysis processing server, and an image acquisition module 3, an alarm module, a stop module, and a distance sensor 2 connected to the machine vision analysis processing server;
the image acquisition module 3 is arranged right opposite to the three-jaw chuck 1 of the machine tool, and is used for acquiring images around the three-jaw chuck 1 and sending the images to the machine vision analysis processing server;
an alarm module: the alarm device is used for sending alarm information after receiving the alarm command;
a shutdown module: the system is used for stopping the machine tool after receiving a stop command;
the distance sensor 2 is arranged right above the three-jaw chuck 1 and positioned at two sides of the three-jaw chuck 1, and is used for measuring the distance between the distance sensor 2 and the nearest barrier and sending the result to the main control module;
the machine vision analysis processing server includes:
a preprocessing module: the three-jaw chuck is used for carrying out gray level conversion, noise reduction and enhancement processing on the acquired image around the three-jaw chuck 1;
a foreign matter detection module: the detection device is used for detecting whether foreign matters exist around the three-jaw chuck 1 or not and sending the result to the main control module;
a type identification module: the device is used for identifying the type of the foreign matter and sending the result to the main control module;
a safety detection module: calculating the Euclidean distance between the center position of the three-jaw chuck 1 and the current foreign matter edge point set, comparing the Euclidean distance with an abnormal parameter threshold value, judging whether foreign matters exist in the safety distance around the three-jaw chuck 1, and sending the result to a main control module; the center position of the three-jaw chuck 1 is determined by the mean value of edge points before the machine tool starts to process and when no foreign matter exists around the three-jaw chuck 1, and the current foreign matter edge point set is determined by removing the edge point set of the three-jaw chuck 1 from the current image edge point set;
the main control module: the device is used for connecting each module and receiving the output results of the foreign matter detection module, the type identification module, the safety detection module and the distance sensor 2; if the current distance of the distance sensor 2 is smaller than the distance threshold value or foreign matters belonging to the category of emergency stop exist in the safe distance of the three-jaw chuck 1, an alarm command is sent to the alarm module, and a stop command is sent to the stop module.
The foreign matter detection module judges whether the foreign matter exists or not according to the difference value of the current image and the foreign matter-free image.
The type identification module identifies the type of the foreign matter through a machine learning model formed by a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms, and acquires the edge of the current image contour by using a Canny operator or a Sobel operator.
It should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.

Claims (8)

1. A machine tool emergency stop method based on a distance sensor and machine vision is characterized by comprising the following steps:
s1, before processing, training and learning the foreign body image data of different types by using a machine learning algorithm and acquiring a three-jaw chuck edge point set { C ] when no foreign body existsiTo obtain the center position C of the three-jaw chuck0；
Wherein
Figure FDA0002643640170000011
(C0x,C0y) Indicating the center position C of the three-jaw chuck0(ii) position coordinates of (C)ix,Ciy) Representing a single point C in a three-jaw chuck edge point setiM represents the total number of the edge points of the three-jaw chuck;
s2, after the processing is started, obtaining the current distance h measured by the distance sensor1And an image of the periphery of the three-jaw chuck;
s3 comparing the current distance h1And a distance threshold H0Judging whether foreign matters exist in the safe distance of the three-jaw chuck or not, if so, entering S5, and if not, entering S4;
s4, analyzing the image by using a machine vision algorithm, judging whether foreign matters belonging to the emergency stop category exist in the safety distance of the three-jaw chuck, if so, entering S5, and if not, entering S2;
s5: the machine tool immediately alarms and stops working at the same time.
2. The method for machine tool emergency stop based on distance sensor and machine vision according to claim 1, characterized in that step S4 includes the following steps:
s41 reading abnormal parameter threshold D by using machine vision algorithm0Preprocessing the image around the three-jaw chuck;
s42: judging whether the foreign matter exists or not by using the difference value of the current image and the foreign matter-free image, and if the foreign matter is not detected, executing S2; if the foreign matter is detected to be present, executing S43;
s43, identifying the foreign body type by using the machine learning model trained in the step S1, and executing S2 if the foreign body type does not belong to the emergency stop category and executing S44 if the foreign body type belongs to the emergency stop category;
s44, utilizing Sobel operator or Canny operator to carry out image edge detection, obtaining the current image edge outline and forming an edge point set { G }jRemoving a three-jaw chuck edge point set { C }iThus, a foreign object edge point set G is calculatedkIn which { G }j}＝{Ci}+{Gk}；
S45: calculating the center position C of the three-jaw chuck0And foreign matter edge point set { GkThe distance between the two is used for judging whether foreign matters exist in the safe distance of the three-jaw chuck, and the value is determined by the Euclidean distance between the two:
Figure FDA0002643640170000012
wherein (G)kx,Gky) Representing a single point G in a set of foreign object edge pointskK ∈ [1, n ] of]N represents the total number of foreign matter edge points;
s46, by dividing the Euclidean distance DkAnd a threshold value D0Comparing the parameters, judging whether foreign matters exist in the safe distance of the three-jaw chuck, if Dk＜D0Then there is a foreign object within the safe distance performs S5, if Dk≥D0And k < n, no foreign matter exists within the safe distance to S45, if Dk≥D0And k-n, there is no foreign object within the safety distance to enter S2.
3. The method for machine tool emergency stop based on distance sensor and machine vision according to claim 2, characterized in that the preprocessing in step S43 includes gray scale conversion, noise reduction and enhancement.
4. The machine tool emergency stop method based on the distance sensor and the machine vision as claimed in claim 1, wherein the emergency stop category in the step S4 includes hair and hands.
5. The machine tool emergency stop method based on the distance sensor and the machine vision is characterized in that the machine learning algorithm is a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms.
6. A machine tool emergency stop device based on the method of any one of claims 1 to 5, characterized by comprising a main control module, a machine vision analysis processing server, and an image acquisition module, an alarm module, a stop module and a distance sensor which are connected with the machine vision analysis processing server;
the image acquisition module is arranged right opposite to the three-jaw chuck of the machine tool and used for acquiring images of the periphery of the three-jaw chuck and sending the images to the machine vision analysis processing server;
an alarm module: the alarm device is used for sending alarm information after receiving the alarm command;
a shutdown module: the system is used for stopping the machine tool after receiving a stop command;
the distance sensor is arranged right above the three-jaw chuck and close to one side of an operator, and is used for measuring the distance between the distance sensor and the nearest barrier and sending the result to the main control module;
the machine vision analysis processing server includes:
a preprocessing module: the three-jaw chuck is used for carrying out gray level conversion, noise reduction and enhancement processing on the acquired image at the periphery of the three-jaw chuck;
a foreign matter detection module: the device is used for detecting whether foreign matters exist around the three-jaw chuck or not and sending the result to the main control module;
a type identification module: the device is used for identifying the type of the foreign matter and sending the result to the main control module;
a safety detection module: calculating Euclidean distance between the center position of the three-jaw chuck and the current foreign matter edge point set, comparing the Euclidean distance with an abnormal parameter threshold value, judging whether foreign matters exist in the safety distance around the three-jaw chuck, and sending the result to the main control module; the center position of the three-jaw chuck is determined by the mean value of edge points before the machine tool starts to process and when no foreign matter exists around the three-jaw chuck, and the current foreign matter edge point set is determined by removing the three-jaw chuck edge point set from the current image edge point set;
the main control module: the device is used for connecting each module and receiving the output results of the foreign matter detection module, the type identification module, the safety detection module and the distance sensor; and if the current distance of the distance sensor is smaller than the distance threshold value or foreign matters belonging to the category of emergency stop exist in the safe distance of the three-jaw chuck, sending an alarm command to the alarm module and sending a stop command to the stop module.
7. The machine tool emergency stop device according to claim 6, wherein the foreign object detection module determines the presence or absence of a foreign object by a difference value between the current image and the foreign object-free image.
8. The machine tool emergency stop device according to claim 6, wherein the type identification module identifies the type of the foreign object through a machine learning model formed by a BP neural network, an RBF neural network, a deep learning algorithm or other related algorithms, and acquires the edge point set of the current image contour by using a Canny operator or a Sobel operator.
CN202010847699.0A 2020-08-21 2020-08-21 Machine tool emergency stop method and device based on distance sensor and machine vision Pending CN111975438A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010847699.0A CN111975438A (en) 2020-08-21 2020-08-21 Machine tool emergency stop method and device based on distance sensor and machine vision

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010847699.0A CN111975438A (en) 2020-08-21 2020-08-21 Machine tool emergency stop method and device based on distance sensor and machine vision

Publications (1)

Publication Number Publication Date
CN111975438A true CN111975438A (en) 2020-11-24

Family

ID=73443942

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010847699.0A Pending CN111975438A (en) 2020-08-21 2020-08-21 Machine tool emergency stop method and device based on distance sensor and machine vision

Country Status (1)

Country Link
CN (1) CN111975438A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107088971A (en) * 2016-02-17 2017-08-25 株式会社迪思科 Processing unit (plant)
CN206578557U (en) * 2017-01-18 2017-10-24 浙江工业职业技术学院 A kind of novel lathe with visual protective apparatus
CN108229548A (en) * 2017-12-27 2018-06-29 华为技术有限公司 A kind of object detecting method and device
CN110728655A (en) * 2019-09-06 2020-01-24 重庆东渝中能实业有限公司 Machine vision-based numerical control machine tool workpiece abnormity detection method and device
KR20200064947A (en) * 2018-11-29 2020-06-08 (주)코어센스 Apparatus for tracking position based optical position tracking system and method thereof

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107088971A (en) * 2016-02-17 2017-08-25 株式会社迪思科 Processing unit (plant)
CN206578557U (en) * 2017-01-18 2017-10-24 浙江工业职业技术学院 A kind of novel lathe with visual protective apparatus
CN108229548A (en) * 2017-12-27 2018-06-29 华为技术有限公司 A kind of object detecting method and device
KR20200064947A (en) * 2018-11-29 2020-06-08 (주)코어센스 Apparatus for tracking position based optical position tracking system and method thereof
CN110728655A (en) * 2019-09-06 2020-01-24 重庆东渝中能实业有限公司 Machine vision-based numerical control machine tool workpiece abnormity detection method and device

Similar Documents

Publication Publication Date Title
Kumar et al. Driver drowsiness monitoring system using visual behaviour and machine learning
CN109460699A (en) A kind of pilot harness&#39;s wearing recognition methods based on deep learning
CN111680613B (en) Method for detecting falling behavior of escalator passengers in real time
CN109741815A (en) A kind of medical guide robot and its implementation
CN111046583A (en) Switch machine fault diagnosis method based on DTW algorithm and ResNet network
CN109353923B (en) Elevator layer door lock short circuit detects and positioner
CN108776452B (en) Special equipment field maintenance monitoring method and system
CN107832721A (en) Method and apparatus for output information
CN110728655A (en) Machine vision-based numerical control machine tool workpiece abnormity detection method and device
CN115649234A (en) Intelligent operation and maintenance system for subway
CN108875617A (en) Auxiliary driving method and device, vehicle
CN111975438A (en) Machine tool emergency stop method and device based on distance sensor and machine vision
CN111062238A (en) Escalator flow monitoring method and system based on human skeleton information and multi-target tracking
KR101643812B1 (en) Apparatus and method for guaranteeing robot safety using robot guardian module
CN111731979A (en) Automatic staircase takes advantage of personnel identification system that tumbles based on computer vision
CN112307920B (en) High-risk worker behavior early warning device and method
CN111531581B (en) Industrial robot fault action detection method and system based on vision
CN114998874A (en) Driver abnormal behavior detection method based on deep learning
CN105984774B (en) The fault detection method of elevator doorway protective device
CN114140819A (en) Device and method for assisting safety management in power grid operation
CN113096660A (en) Personnel safety protection method and device, electronic equipment and storage medium
CN111055890B (en) Intelligent detection method and detection system for railway vehicle anti-slip
CN115013386B (en) Hydraulic system protection device control method and control device based on visual recognition
Zhai et al. A detection model for driver's unsafe states based on real-time face-vision
CN108861423A (en) Belt conveyer electric control system based on PLC

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20201124