CN117392771B - Monitoring alarm system and method for sand core placement robot - Google Patents
Monitoring alarm system and method for sand core placement robot Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 79
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G07C3/005—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles during manufacturing process
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G07C3/14—Quality control systems
- G07C3/146—Quality control systems during manufacturing process
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a monitoring alarm system for a robot for placing sand cores, and relates to the technical field of monitoring alarm; the environment monitoring unit, the action monitoring unit and the alarm management unit are used for accurately evaluating the influence of the environment on the placement of the sand cores by the robot and the accuracy and smoothness of the placement of the sand cores by monitoring and analyzing the environment information and the thermal action of the machine, providing evaluation indexes for the placement quality of the sand cores and finding and correcting the deviation or error possibly occurring in the placement process of the sand cores so as to improve the accuracy and precision of the placement of the sand cores; the quality monitoring unit and the state management unit are used for judging the working state of the robot by analyzing the quality, the sand core complexity and the placement time of castings after the sand cores are placed in the history mode of the robot, and pairing and replacing the robot with the abnormal state with the standby robot, so that the robot with poor working state can be timely found and replaced to improve the efficiency and accuracy of placing the sand cores by the robot.
Description
Technical Field
The invention relates to the technical field of monitoring and alarming, in particular to a monitoring and alarming system and method for a robot for placing sand cores.
Background
The accurate placement of the sand core is very important for casting quality, the placement position and the shape of the sand core are required to be accurate, so that the quality and the performance of a final cast product are ensured, and if the sand core is placed inaccurately or has deviation, the problems of inconsistent size, defects or insufficient structural strength of a casting and the like can be caused; therefore, the monitoring alarm system of the sand core robot is particularly important;
In actual operation, due to complex environmental conditions and the limitation of the accuracy of the robot action, errors exist in the sand core placement process, and the errors can cause defects and faults in the production process of products, so that economic losses are brought to enterprises; therefore, a monitoring alarm system capable of accurately monitoring the action of the robot for placing the sand core and giving an alarm to abnormal conditions and providing corresponding optimization measures is needed to solve the problem of inaccurate sand core placement.
Disclosure of Invention
The invention mainly aims to provide a monitoring alarm system and a monitoring alarm method for a sand core placing robot, so as to solve the problem of inaccurate sand core placement caused by environmental influence and robot action errors.
To achieve the above object, according to one aspect of the present invention, there is provided a monitoring alarm system for a sand core placement robot, comprising: the system comprises a data acquisition unit, a database, an environment monitoring unit, an action monitoring unit, an alarm management unit, a quality monitoring unit and a state management unit;
The data acquisition unit acquires the action of taking the sand core, the action of placing the sand core and the environmental information, and sends the action and the environmental information to the database for storage;
The environment monitoring unit is used for analyzing the environment information of the environment where the robot is located to obtain an environment influence value and sending the environment influence value to the alarm management unit;
The action monitoring unit is used for analyzing the action of taking the sand core and the action of placing the sand core of the robot to obtain a sand core placement value, and specifically comprises the following steps:
101: scanning the casting mould to obtain the shape and filling position of the filling sand core; setting the sand cores with each shape to correspond to a pre-fetching sand core action; matching the shape of the filling sand core with the shape of all sand cores in a database to obtain the storage position, the number and the pre-fetching sand core action of the filling sand core;
102: generating a plurality of motion paths by taking the position of the current robot as a starting position and the position of the filling sand core as a destination, selecting one motion path as a target motion path by using Dijkstra algorithm, reaching a sand core storage position according to the target motion path, and taking the sand core according to the action of pre-taking the sand core;
103: the method comprises the steps of extracting a sand core taking action of a robot, and establishing an actual movement track and a preset movement track by utilizing a kinematic analysis method through the sand core taking action and the pre-sand core taking action; aligning the actual motion track with the preset motion track, calculating a two-dimensional Euclidean distance and an included angle value between the actual track point and the preset track point by point, and marking the two-dimensional Euclidean distance and the included angle value as G1 and G2 respectively; calculating to obtain an action difference coefficient GZ1 by using a set formula GZ1=g1×G1+g2×G2, wherein G1 and G2 are set proportional coefficients respectively;
104: the method comprises the steps of extracting a sand core action and a sand core placing action of a robot, decomposing the two actions to obtain j small actions, calculating the speed and the acceleration of each small action, and marking the speed and the acceleration as Vj and Aj respectively; wherein j=1, 2,3 … … n3, n3 is a positive integer, n3 represents the total number of small actions; substituting the velocity Vj and the acceleration Aj into a set formula Calculating to obtain an action smoothing coefficient GZ2, wherein g3 and g4 are respectively set proportion coefficients,/>For the velocity average of j small actions,/>Acceleration average value for j small actions;
105: calculating an action difference coefficient GZ1 and an action smoothing coefficient GZ2 through a set formula FGZ =g5×GZ1+g6×GZ2 to obtain a sand core placement value FGZ, wherein g5 and g6 are set proportional coefficients respectively; sending the sand core placement value to an alarm management unit;
The alarm management unit calculates an alarm value FHZ by substituting the received environmental impact value HZ and the sand core placement value FGZ into a set formula FHZ =f1×hz+f2× FGZ, wherein f1 and f2 are set proportionality coefficients respectively; comparing and analyzing the alarm value with a set threshold value to generate an alarm signal; wherein the alarm signal comprises a primary error alarm and a secondary error alarm; when a first-level error alarm is generated, transferring the mould with the sand core to a correction robot, and taking out the sand core from the casting mould by the correction robot and placing again until the error alarm of the robot is eliminated; when the secondary error alarm is generated, the robot is controlled to take out the sand core from the casting and to reset the sand core until the error alarm of the robot is eliminated.
Further, the environmental information of the environment where the robot is located is analyzed to obtain an environmental impact value, which is specifically as follows:
201: taking the position of the robot as the center, taking R as the radius to establish a spherical range, extracting all devices in the spherical range, marking the devices as influence devices, counting the number of the influence devices, and marking the devices as H1;
202: respectively extracting a noise value, a vibration value and a linear distance between the noise value and the robot, which are generated by influencing equipment, and dividing the noise value and the vibration value by the linear distance between the noise value and the robot to obtain an effective noise value and an effective vibration value;
203: extracting all influence devices in a spherical range, and respectively carrying out summation calculation on effective noise values and effective vibration values of all influence devices at the same moment to obtain a noise influence value and a vibration influence value at the moment; establishing a change relation graph of the noise influence value and the vibration influence value along with time in a monitoring period by taking time as an abscissa and taking the noise influence value and the vibration influence value as an ordinate respectively;
204: extracting a relation graph of the noise influence value with time in a monitoring period, calculating the slope of a line segment formed by the noise influence values at adjacent moments by using a least square method, summing the slopes greater than zero to obtain an increasing trend value, taking the absolute value of the slope smaller than zero, summing the absolute value to obtain a decreasing trend value, dividing the increasing trend value by the decreasing trend value to obtain an increasing trend ratio, and marking the increasing trend ratio as h1; extracting a minimum noise influence value and a maximum noise influence value in a relation chart of the noise influence value in the monitoring period along with time, carrying out difference value calculation on the minimum noise influence value and the maximum noise influence value to obtain a maximum difference value of noise, and marking the maximum difference value as h2; carrying out average value calculation on noise influence values corresponding to all moments in a monitoring period to obtain a noise influence average value, and marking the noise influence average value as h3; substituting the increasing and decreasing trend ratio H1, the noise maximum difference value H2 and the noise influence mean value H3 into a set formula H2 = beta 2 x H3 < + > (-1) n2 x beta 1 x H2 x H1 to calculate to obtain a noise coefficient H2, wherein beta 1 and beta 2 are set proportional coefficients respectively; when the increasing and decreasing trend ratio is greater than one, indicating that the noise influence value integrally shows increasing trend in the monitoring period, and taking the value of n2 as an even number; when the increasing and decreasing trend ratio is smaller than one, the noise influence value integrally shows a decreasing trend in the monitoring period, and then the n2 value is an odd number; when the increasing and decreasing trend ratio is equal to one, the noise influence value is indicated to be in a stable state integrally in the monitoring period, and then the value of n2 is zero;
205: extracting a time-dependent change relation graph of vibration influence values in a monitoring period, comparing and analyzing the vibration influence values with a set vibration interval to obtain high vibration influence values, medium vibration influence values and low vibration influence values, and respectively marking the corresponding moments of the high vibration influence values, the medium vibration influence values and the low vibration influence values as high influence moments, medium influence value moments and low influence moments; counting the number of high influence time, medium influence time and low influence time in a monitoring period respectively, and marking the numbers as h4, h5 and h6 respectively; summing the high vibration influence value corresponding to the high influence moment, the medium vibration influence value corresponding to the medium influence moment and the low vibration influence value corresponding to the low influence moment to obtain a high influence total value, a medium influence total value and a low influence total value, and respectively marking the high influence total value, the medium influence total value and the low influence total value as h7, h8 and h9; substituting the number h4 of high influence moments, the number h5 of medium influence moments, the number h6 of low influence moments, the high influence total value h7, the medium influence total value h8 and the low influence total value h9 into a set formula Calculating to obtain a vibration coefficient H3, wherein beta 3, beta 4, beta 5 and beta 6 are set proportionality coefficients respectively;
206: calculating the number H1 of influencing devices, the noise coefficient H2, and the vibration coefficient H3 by a set formula hz=a1×h1+a2×h2+a3×h3 to obtain an environmental influence value HZ; wherein a1, a2 and a3 are set proportionality coefficients, respectively.
Further, the system also comprises a quality monitoring unit and a state management unit;
the quality management unit analyzes the cast formed by casting after the sand core is placed by the robot to obtain a quality coefficient, and sends the quality coefficient to the state management unit;
The state management unit analyzes the process of placing the sand core by the robot to obtain a state value, judges the working state of the robot according to the state value and generates a corresponding execution strategy.
Further, the cast formed by casting after the sand core is placed by the robot is analyzed to obtain the quality coefficient, and the quality coefficient is specifically as follows:
401: the casting numbers and the detection results are called, the number of castings corresponding to the robot is counted, and the number of castings is recorded as R1;
402: extracting the number of castings which are qualified and unqualified in detection results in the number of castings corresponding to the robot, and marking the number of castings as R2 and R3 respectively;
403: substituting the casting quantity R1, the casting quantity R2 which is qualified in detection and the casting quantity R3 which is unqualified in detection into a set formula The quality coefficient RZ1 is calculated, wherein r1 and r2 are set proportional coefficients respectively, and the set proportional coefficients are sent to the state management unit.
Further, a state value is obtained by analyzing the process of placing the sand core by the robot, the working state of the robot is judged according to the state value, and a corresponding execution strategy is generated, specifically as follows:
501: extracting a monitoring period of the historical placement sand core of the robot, and calculating a difference value between the starting time and the ending time of the monitoring period to obtain a placement duration Q1; setting a sand core complex value corresponding to each sand core, matching the sand cores with all the set sand cores to obtain the corresponding sand core complex value, and marking the corresponding sand core complex value as QZ1;
502: the sand core complex value QZ1 and the sand core placement duration Q1 pass through a set formula Calculating to obtain a sand core placement efficiency ratio QZ2, wherein q1 and q5 are set proportionality coefficients respectively; comparing and analyzing the sand core placement efficiency ratio with a set efficiency interval to generate high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement; counting the times of high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively, and marking the times as R4, R5 and R6 respectively;
503: substituting the quality coefficient RZ1, the times R4 for placing the high-efficiency sand cores, the times R5 for placing the medium-efficiency sand cores and the times R6 for placing the low-efficiency sand cores into a set formula Calculating to obtain a state value RQZ, wherein r3 and r4 are set proportionality coefficients respectively; comparing and analyzing the state value with a set state threshold, outputting a robot number when the state value is larger than the set state threshold, and marking the robot as a state abnormal robot; sequencing the corresponding state anomaly robots according to the sequence of the state values from large to small to obtain a state anomaly robot sequence table; the robot with the minimum state value is recorded as a correcting robot;
504: setting a plurality of standby robots, wherein each standby robot corresponds to a replacement value, and sequencing the standby robots according to the sequence from the large replacement value to the small replacement value to generate a standby robot sequence table;
505: traversing each abnormal state robot in the abnormal state robot sequence table, pairing with the standby robots in the standby robot sequence table one by one, and replacing the successfully paired abnormal state robots and standby robots.
Further, the calculation steps of the complex value of the sand core are as follows:
601: acquiring the historical times of placing sand cores of a robot, and the weight and the shape of the sand cores when the sand cores are placed each time;
602: scanning the surface of the sand core by using a three-dimensional scanner, acquiring tens of thousands of data points to form point cloud data, and generating a surface model according to the point cloud data;
603: fitting the curved surface to the point cloud data by using a least square method to obtain a curvature equation, and conducting derivative calculation on the curvature equation to obtain a curvature value at each point;
604: calculating the average value of curvature values of all points to obtain the average value of curvature of the sand core, and marking the average value as Q2;
605: comparing and analyzing the curvature value of each point on the surface of the sand core with a set curvature threshold value, when the curvature value is larger than the set curvature threshold value, marking the point as a sharp point, counting the number of the sharp points existing on the surface of the sand core, and marking the sharp point as Q3;
606: extracting the weight of the sand core, and marking the weight as Q4; calculating the average value Q2 of the curvature of the sand core, the number Q3 of sharp points and the weight Q4 of the sand core through a set formula QZ1=q2×Q2+q3×Q3+q4×Q4 to obtain a complex value QZ1 of the sand core, wherein Q2, Q3 and Q4 are set proportionality coefficients respectively.
Further, the replacement value calculation step of the standby robot is as follows:
701: the using times, the using time and the maintaining times of each use, and the maintaining time of each maintenance of the standby robot are called; calculating the average value of the using time length of each use, obtaining the average value of the using time length, and marking the average value as B1;
702: calculating the average value of the maintenance time length of each maintenance to obtain the average value of the maintenance time length, and marking the average value as B2;
703: extracting the maintenance time of each maintenance, calculating the difference value between the maintenance time and the maintenance time of the last maintenance to obtain maintenance interval duration, and calculating the average value of all the maintenance interval durations to obtain a maintenance interval average value B3;
704: substituting the using time length average value B1, the maintaining time length average value B2 and the maintaining interval average value B3 into a set formula The replacement value BZ is calculated, wherein b1, b2 and b3 are respectively set scaling coefficients.
In order to achieve the above object, according to another aspect of the present invention, there is provided a monitoring and alarming method for a robot for placing a sand core, comprising the steps of:
801: collecting sand core taking action, sand core placing action and environment information;
802: the influence of the environment on the placement of the sand core by the robot is quantified according to the environment information so as to obtain an environment influence value;
803: analyzing the difference between the sand core taking action and the pre-coring action of the robot to obtain an action difference coefficient; meanwhile, decomposing the action of taking the sand core and the action of placing the sand core into a plurality of small actions, and analyzing the smoothness degree between the small actions to obtain an action smoothness coefficient; comprehensively analyzing the motion difference coefficient and the motion smoothing coefficient to obtain a sand core placement value;
804: comprehensively analyzing the environmental impact value and the sand core placement value to obtain an alarm value, and generating an alarm signal according to the alarm value, wherein the alarm signal comprises a primary error alarm and a secondary error alarm; when a first-level error alarm is generated, transferring the die with the sand core to a robot with the minimum alarm value, and taking out the sand core from the casting die by the robot and placing the sand core again until the error alarm of the robot is eliminated; when the secondary error alarm is generated, the robot is controlled to take out the sand core from the casting and to reset the sand core until the error alarm of the robot is eliminated;
805: the quality analysis is carried out on the casting cast by the robot after the sand core is placed, so as to obtain the quality coefficient;
806: generating a surface model by carrying out three-dimensional scanning on the sand core, and analyzing according to the surface model to obtain a complex value of the sand core; comprehensively analyzing the complex sand core value and the sand core placement time length to obtain a sand core placement efficiency ratio, and judging the efficiency of the robot for placing the sand core according to the sand core placement efficiency ratio, wherein the efficiency of placing the sand core is high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively; counting the times of high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively; comprehensively analyzing the quality coefficient, the times of placing the high-efficiency sand cores, the times of placing the medium-efficiency sand cores and the times of placing the low-efficiency sand cores to obtain a robot with abnormal states; and replacing the matching value of the robot with the abnormal state corresponding to the standby robot.
The invention has the beneficial effects that:
(1) The environment monitoring unit, the action monitoring unit and the alarm management unit monitor and analyze the environment information and the thermal action of the machine to accurately evaluate the influence of the environment on the placement of the sand cores by the robot, the placement accuracy and smoothness of the sand cores, provide evaluation indexes for the placement quality of the sand cores, and find and correct the deviation or error possibly occurring in the sand core placement process, thereby improving the placement accuracy and accuracy of the sand cores, further avoiding casting defects caused by the placement error of the sand cores and improving the product quality;
(2) The quality monitoring unit and the state management unit are used for judging the working state of the robot by analyzing the quality, the sand core complexity and the placement time of castings after the sand cores are placed in the history mode of the robot, and pairing and replacing the robot with the standby robot in abnormal state, so that the robot with poor working state can be timely found and replaced to improve the efficiency and accuracy of placing the sand cores by the robot, and the stable operation of the robot and the sand core placement quality are guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system module connection of the present invention
Fig. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention also provides a monitoring alarm system for the sand core placing robot, and the monitoring alarm system for the sand core placing robot can be used for executing the monitoring alarm method for the sand core placing robot provided by the embodiment of the invention; the monitoring alarm system for the sand core placing robot provided by the embodiment of the invention is introduced as follows;
Fig. 1 is a schematic view of a monitoring alarm system for a sand core placement robot according to an embodiment of the present invention, as shown in fig. 1, including: the system comprises a data acquisition unit, a database, an environment monitoring unit, an action monitoring unit, an alarm management unit, a quality monitoring unit and a state management unit;
Numbering the robot and the corresponding operator as mi; wherein i=1, 2,3 … … n1, n1 is a positive integer, n1 represents the total number of robots; the data acquisition unit is in communication connection with the high-definition camera to acquire the action of taking the sand core and the action of placing the sand core of the robot, and sends the actions into the database for storage; the data acquisition unit is also in communication connection with the environment sensor to acquire environment information, wherein the environment information comprises: ambient temperature, device vibration value, and device noise value;
Taking the moment of the robot for scanning the casting mould as a starting moment and the moment of the robot for placing the sand core as an ending moment, and forming a monitoring period between the starting moment and the ending moment;
the environment monitoring unit analyzes the environment in which the robot is positioned by analyzing to obtain an environment influence value, specifically:
Step one: taking the position of the robot as the center, taking R as the radius to establish a spherical range, extracting all devices in the spherical range and recording the devices as influencing devices; counting the number of influencing devices and marking the number as H1;
Step two: respectively extracting a noise value, a vibration value and a linear distance between the noise value and the robot, which are generated by influencing equipment, and dividing the noise value and the vibration value by the linear distance between the noise value and the robot to obtain an effective noise value and an effective vibration value;
Step three: extracting all influence devices in a spherical range, and respectively carrying out summation calculation on effective noise values and effective vibration values of all influence devices at the same moment to obtain a noise influence value and a vibration influence value at the moment; establishing a change relation graph of the noise influence value and the vibration influence value along with time in a monitoring period by taking time as an abscissa and taking the noise influence value and the vibration influence value as an ordinate respectively;
Step four: extracting a relation graph of the noise influence values with time in a monitoring period, calculating the slope of a line segment formed by the noise influence values at adjacent moments by using a least square method, comparing and analyzing the slope with zero, and when the slope is larger than zero, indicating that the noise influence values corresponding to the adjacent two moments are increasing trends at the moment, and marking the slope as an increasing slope; when the slope is smaller than zero, the noise influence values corresponding to the adjacent two moments at the moment are the decreasing trend, and the slope is recorded as the decreasing slope; summing the increasing slope to obtain an increasing trend, taking the absolute value of the decreasing slope, summing to obtain a decreasing trend, dividing the increasing trend by the decreasing trend to obtain an increasing and decreasing trend ratio, and marking the increasing trend ratio as h1;
Obtaining a minimum noise influence value and a maximum noise influence value by monitoring a time-dependent change relation graph of the noise influence value in the period, calculating the difference value of the minimum noise influence value and the maximum noise influence value to obtain a maximum difference value of the noise in the period, and marking the maximum difference value as h2;
carrying out average value calculation on noise influence values corresponding to all moments in a monitoring period to obtain a noise influence average value, and marking the noise influence average value as h3;
calculating by a set formula h2=β2×h3+ (-1) n2 ×β1×h2×h1 to obtain a noise coefficient H2, wherein β1 and β2 are set proportionality coefficients respectively; when the increasing and decreasing trend ratio is greater than one, indicating that the noise influence value integrally shows increasing trend in the monitoring period, and taking the value of n2 as an even number; when the increasing and decreasing trend ratio is smaller than one, the noise influence value integrally shows a decreasing trend in the monitoring period, and then the n2 value is an odd number; when the increasing and decreasing trend ratio is equal to one, the noise influence value is indicated to be in a stable state integrally in the monitoring period, and then the value of n2 is zero;
Step five: extracting a time-dependent change relation graph of vibration influence values in a monitoring period, comparing and analyzing the vibration influence values with a set vibration interval, and when the vibration influence values are larger than the maximum value in the set vibration interval, indicating that the vibration influence values are larger at the moment and the possibility of influencing robots is larger, marking the vibration influence values as high vibration influence values and marking the time corresponding to the high vibration influence values as high influence time; when the vibration influence value is within the set vibration interval, the vibration influence value is marked as a middle vibration influence value, and the time corresponding to the middle vibration influence value is marked as a middle influence value time; when the vibration influence value is smaller than the minimum value in the set vibration interval, marking the vibration influence value as a low influence vibration value, and marking the moment corresponding to the low influence vibration value as a low influence moment; counting the number of high influence time, medium influence time and low influence time in a monitoring period respectively, and marking the numbers as h4, h5 and h6 respectively; summing the high vibration influence value corresponding to the high influence moment, the medium vibration influence value corresponding to the medium influence moment and the low vibration influence value corresponding to the low influence moment to obtain a high influence total value, a medium influence total value and a low influence total value, and respectively marking the high influence total value, the medium influence total value and the low influence total value as h7, h8 and h9; using a set formula Calculating to obtain a vibration coefficient H3, wherein beta 3, beta 4, beta 5 and beta 6 are set proportionality coefficients respectively;
Step six: calculating the number H1 of influence sets, the noise coefficient H2, and the vibration coefficient H3 by a set formula hz=a1×h1+a2×h2+a3×h3 to obtain an environmental influence value HZ; wherein a1, a2 and a3 are respectively set proportionality coefficients;
the action monitoring unit analyzes the action of the robot for placing the sand core to obtain an action error coefficient, and specifically comprises the following steps:
Step one: the robot scans the casting mould by using a laser scanner to obtain the shape and the filling position of the filling sand core; setting the sand cores with each shape to correspond to a pre-fetching sand core action; it should be noted that each shape sand core corresponds to a pre-fetching sand core action to prevent the sand core from being damaged or deformed due to improper sand core fetching action, so as to safely and accurately take out the sand core; matching the shape of the filling sand core with the shape of all sand cores in a database to obtain the storage position, the number and the pre-fetching sand core action of the filling sand core;
Step two: generating a plurality of motion paths by taking the position of the current robot as a starting position and the position of the filling sand core as a destination, selecting one motion path as a target motion path by using Dijkstra algorithm, reaching a sand core storage position according to the target motion path, and taking the sand core according to the action of pre-taking the sand core;
Step three: acquiring a robot coring action through a high-definition camera, and establishing an actual motion track and a preset motion track by utilizing a kinematic analysis method through the robot coring action and the pre-coring action; the length of the actual motion track is consistent with that of the preset motion track through interpolation or cutting, the actual motion track is aligned with the preset motion track, and after the alignment, the two-dimensional Euclidean distance and the included angle value between the actual track point and the preset track point are calculated point by point; it should be noted that, the two-dimensional euclidean distance refers to a linear distance between an actual track point and a preset track point, and the included angle value refers to an included angle between an actual track point and the preset track point;
Respectively carrying out average value calculation on the two-dimensional Euclidean distances and the included angle values of all the track points to obtain Euclidean difference values and angle difference values, and respectively marking the Euclidean difference values and the angle difference values as G1 and G2; calculating to obtain an action difference coefficient GZ1 by using a set formula GZ1=g1×G1+g2×G2, wherein G1 and G2 are set proportional coefficients respectively; the formula shows that the larger the two-dimensional Euclidean distance between the actual track point and the preset track point, which means that the longer the straight line distance between the actual track point and the preset track point is, the larger the action difference coefficient between the two tracks is; the larger the deviation of the included angle between the actual track point and the preset track point, which means that the larger the difference in direction or steering exists, the larger the action difference coefficient between the two tracks;
Step four: extracting a sand core taking action and a sand core placing action of a robot, decomposing the two actions to obtain j small actions, calculating the speed and the acceleration of each small action, and marking the speeds and the accelerations as Vj and Aj respectively; wherein j=1, 2,3 … … n3, n3 is a positive integer, n3 represents the total number of small actions;
using a set formula Calculating to obtain an action smoothing coefficient GZ2, wherein g3 and g4 are respectively set proportion coefficients,/>For the velocity average of j small actions,/>Acceleration average value for j small actions; when the motion smoothing coefficient is smaller, the motion smoothness coefficient indicates that each motion of the robot is smoother, the motion is coherent, the motion error is smaller, and the accuracy of sand core placement is higher; otherwise, the larger the error of the sand core placement is;
Step five: calculating an action difference coefficient GZ1 and an action smoothing coefficient GZ2 through a set formula FGZ =g5×GZ1+g6×GZ2 to obtain a sand core placement value FGZ, wherein g5 and g6 are set proportional coefficients respectively; sending the sand core placement value to an alarm management unit;
The alarm management unit performs deepening analysis on the received environmental impact value HZ and the sand core placement value FGZ to generate an alarm signal and execute a corresponding management strategy, and specifically comprises the following steps:
The alarm value FHZ is calculated by using a set formula FHZ =f1×hz+f2× FGZ, wherein f1 and f2 are set proportionality coefficients respectively; comparing and analyzing the alarm value with a set alarm interval, and when the alarm value is larger than the maximum value in the set alarm interval, indicating that the possibility of inaccurate sand core placement position caused by environmental influence and errors caused by the action of the robot for placing the sand cores at the moment is higher, generating a first-level error alarm; generating a secondary error alarm when the alarm value is within the set alarm interval; when the alarm value is smaller than the minimum value in the set alarm interval, the influence of the environment in the monitoring period and the influence of the action of the robot for placing the sand core on the accuracy of the sand core position are small, the influence can be ignored, and no operation is performed;
when a first-level error alarm is generated, transferring the mould with the sand core to a correction robot, and taking out the sand core from the casting mould by the correction robot and placing again until the error alarm of the robot is eliminated;
when the secondary error alarm is generated, the robot is controlled to take out the sand core from the casting and to reset the sand core until the error alarm of the robot is eliminated;
The quality monitoring unit is in communication connection with a monitoring instrument of the casting to obtain a detection result, wherein the detection result comprises qualified detection and unqualified detection; the casting is formed by pouring after a sand core is placed in a casting mould by a robot, and the number of the casting is consistent with the corresponding number of the sand core;
Calling the number and the detection result of the casting; counting the number of castings corresponding to the robot, and marking the number as R1;
Extracting the number of castings which are qualified and unqualified in detection results in the number of castings corresponding to the robot, and marking the number of castings as R2 and R3 respectively;
using a set formula Calculating to obtain a quality coefficient RZ1, wherein r1 and r2 are respectively set proportional coefficients, and the quality coefficients are sent to a state management unit;
The state management unit analyzes the process of placing the sand core by the robot to judge the working state of the robot and generates a corresponding execution strategy according to the working state, and the method specifically comprises the following steps:
step one: acquiring historical times of sand core placement of a robot, monitoring period, sand core weight and sand core shape when the sand core is placed each time, calculating a difference value between starting time and ending time of the monitoring period to obtain placement duration, and marking the placement duration as Q1;
step two: scanning the surface of the sand core by using a three-dimensional scanner, acquiring tens of thousands of data points to form point cloud data, and generating a surface model according to the point cloud data; the point cloud data consists of a series of points in three-dimensional space, each point having coordinate information;
Step three: fitting the curved surface to the point cloud data by using a least square method to obtain a curvature equation, and conducting derivative calculation on the curvature equation to obtain a curvature value at each point; the curvature value represents information about the degree of curvature and sharpness of the curved surface at that point;
Step four: calculating the average value of the curvature value of each point to obtain the average value of the curvature of the sand core, and marking the average value as Q2;
Step five: comparing and analyzing the curvature value of each point on the surface of the sand core with a set curvature threshold value, and when the curvature value is larger than the set curvature threshold value, indicating that the curvature value of the point is larger, and the degree of taxes and profits of the surface of the sand core around the point is larger; then the point is noted as taxes and profits points; counting the number of sharp points existing on the surface of the sand core, and marking the number as Q3;
Step six: extracting the weight of the sand core, and marking the weight as Q4; calculating the average value Q2 of the curvature of the sand core, the number Q3 of sharp points and the weight Q4 of the sand core through a set formula QZ1=q2×Q2+q3×Q3+q4×Q4 to obtain a complex value QZ1 of the sand core, wherein Q2, Q3 and Q4 are set proportionality coefficients respectively; the larger the complex value of the sand core is, the more complex the geometric shape of the sand core is, and the higher the placement accuracy requirement of the sand core is; calculating a sand core complex value through indexes such as a curvature value, the number of sharp points, the weight of the sand core and the like, wherein the value can reflect the geometric complexity degree of the sand core, and further provides a reference basis for the accuracy requirement of the robot for placing the sand core;
step seven: the sand core complex value QZ1 and the sand core placement duration Q1 pass through a set formula Calculating to obtain a sand core placement efficiency ratio QZ2, wherein q1 and q5 are set proportionality coefficients respectively; comparing and analyzing the sand core placement efficiency ratio with a set efficiency interval, and recording the high-efficiency sand core placement when the sand core placement efficiency ratio is larger than the maximum value in the set efficiency interval, wherein the sand core placement efficiency of the robot is higher; when the sand core placement efficiency ratio is within the set efficiency interval, recording the placement of the medium-efficiency sand core once; when the sand core placement efficiency ratio is smaller than the minimum value in the set efficiency interval, recording one time of low-efficiency sand core placement; counting the times of high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively, and marking the times as R4, R5 and R6 respectively;
Step eight: using a set formula Calculating to obtain a state value RQZ, wherein r3 and r4 are set proportionality coefficients respectively; comparing and analyzing the state value with a set state threshold value, when the state value is larger than the set state threshold value, indicating that the working state of the robot for placing the sand core does not meet the production requirement, outputting a robot number, and marking the robot as a state abnormality robot; sequencing the corresponding state anomaly robots according to the sequence of the state values from large to small to obtain a state anomaly robot sequence table; the robot with the minimum state value is recorded as a correcting robot;
Step nine: and retrieving robot information of the standby robot from the data, wherein the robot information includes: the number of times of use, the use time of each use, the number of times of maintenance, and the maintenance time of each maintenance; calculating the average value of the using time length of each use, obtaining the average value of the using time length, and marking the average value as B1; calculating the average value of the maintenance time length of each maintenance to obtain the average value of the maintenance time length, and marking the average value as B2;
Extracting the maintenance time of each maintenance, calculating the difference value between the maintenance time and the maintenance time of the last maintenance to obtain maintenance interval duration, and calculating the average value of all the maintenance interval durations to obtain a maintenance interval average value B3;
using a set formula Calculating to obtain a replacement value BZ, wherein b1, b2 and b3 are respectively set proportionality coefficients; thermally sequencing the corresponding standby machines according to the sequence from the high replacement value to the low replacement value to obtain a standby robot sequence table;
Step ten: traversing each abnormal state robot in the abnormal state robot sequence table, pairing with the standby robots in the standby robot sequence table one by one, and replacing the successfully paired abnormal state robots and standby robots to improve the efficiency and accuracy of sand core placement of the robots; the method comprises the following steps: the state anomaly robot sequence table is m1, m2 and m3; the standby robot sequence table is as follows: m1, M2, M3, M4 and M5, pairing the first abnormal robot M1 in the abnormal state robot sequence table with the first standby robot in the standby robot sequence table; pairing the second abnormal state robot M2 in the abnormal state robot sequence table with the second standby robot M2 in the standby robot sequence table; and so on until all the state anomaly robots in the state anomaly robot sequence table are traversed.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (7)
1. The monitoring alarm system for the sand core placing robot comprises a data acquisition unit and a database; the data acquisition unit acquires the action of taking the sand core, the action of placing the sand core and environmental information; characterized by further comprising:
The environment monitoring unit is used for analyzing the environment information of the environment where the robot is located to obtain an environment influence value and sending the environment influence value to the alarm management unit;
The action monitoring unit is used for analyzing the action of taking the sand core and the action of placing the sand core of the robot to obtain a sand core placement value, and specifically comprises the following steps:
101: scanning the casting mould to obtain the shape and filling position of the filling sand core; setting the sand cores with each shape to correspond to a pre-fetching sand core action; matching the shape of the filling sand core with the shape of all sand cores in a database to obtain the storage position, the number and the pre-fetching sand core action of the filling sand core;
102: generating a plurality of motion paths by taking the position of the current robot as a starting position and the position of the filling sand core as a destination, selecting one motion path as a target motion path by using Dijkstra algorithm, reaching a sand core storage position according to the target motion path, and taking the sand core according to the action of pre-taking the sand core;
103: the method comprises the steps of extracting a sand core taking action of a robot, and establishing an actual movement track and a preset movement track by utilizing a kinematic analysis method through the sand core taking action and the pre-sand core taking action; aligning the actual motion track with the preset motion track, calculating a two-dimensional Euclidean distance and an included angle value between the actual track point and the preset track point by point, and carrying out numerical processing on the two to obtain an action difference coefficient;
104: the method comprises the steps of extracting a sand core action and a sand core placing action of a robot, decomposing the two actions to obtain j small actions, and calculating the speed and the acceleration of each small action; performing numerical processing on the speed and the acceleration of the small motion to obtain a motion smoothing coefficient;
105: carrying out formulated calculation analysis on the action difference coefficient and the action smoothing coefficient to obtain a sand core placement value, and sending the sand core placement value to an alarm management unit;
The alarm management unit performs normalization processing on the received environmental impact value and the sand core placement value and obtains the numerical value of the environmental impact value and the sand core placement value, analyzes the numerical value to obtain an alarm value, and generates an alarm signal according to the alarm value; wherein the alarm signal comprises a primary error alarm and a secondary error alarm; when a first-level error alarm is generated, transferring the mould with the sand core to a correction robot, and taking out the sand core from the casting mould by the correction robot and placing again until the error alarm of the robot is eliminated; when the secondary error alarm is generated, the robot is controlled to take out the sand core from the casting and to reset the sand core until the error alarm of the robot is eliminated;
the quality management unit analyzes the cast formed by casting after the sand core is placed by the robot to obtain a quality coefficient, and sends the quality coefficient to the state management unit;
The state management unit analyzes the process of placing the sand core by the robot to obtain a state value, judges the working state of the robot according to the state value and generates a corresponding execution strategy.
2. The monitoring and alarm system for a robot with a sand core according to claim 1, wherein the environmental impact value is obtained by analyzing environmental information of the environment in which the robot is located, specifically as follows:
201: taking the position of the robot as the center, taking R as the radius to establish a spherical range, extracting all devices in the spherical range, marking the devices as influence devices, and counting the number of the influence devices;
202: respectively extracting a noise value, a vibration value and a linear distance between the noise value and the robot, which are generated by influencing equipment, and dividing the noise value and the vibration value by the linear distance between the noise value and the robot to obtain an effective noise value and an effective vibration value;
203: extracting all influence devices in a spherical range, and respectively carrying out summation calculation on effective noise values and effective vibration values of all influence devices at the same moment to obtain a noise influence value and a vibration influence value at the moment; establishing a change relation graph of the noise influence value and the vibration influence value along with time in a monitoring period by taking time as an abscissa and taking the noise influence value and the vibration influence value as an ordinate respectively;
204: extracting a relation graph of the noise influence value with time in a monitoring period, calculating the slope of a line segment formed by the noise influence values at adjacent moments by using a least square method, summing the slopes greater than zero to obtain an increasing trend value, taking the absolute value of the slope smaller than zero, summing the absolute value to obtain a decreasing trend value, and dividing the increasing trend value by the decreasing trend value to obtain an increasing trend ratio; extracting a minimum noise influence value and a maximum noise influence value in a relation chart of the noise influence value in the monitoring period along with time, and carrying out difference calculation on the minimum noise influence value and the maximum noise influence value to obtain a maximum difference value of noise; carrying out average value calculation on noise influence values corresponding to all moments in a monitoring period to obtain a noise influence average value; performing numerical processing on the increasing and decreasing trend ratio, the noise maximum difference value and the noise influence mean value to obtain a noise coefficient;
205: extracting a time-dependent change relation graph of vibration influence values in a monitoring period, comparing and analyzing the vibration influence values with a set vibration interval to obtain high vibration influence values, medium vibration influence values and low vibration influence values, and respectively marking the corresponding moments of the high vibration influence values, the medium vibration influence values and the low vibration influence values as high influence moments, medium influence value moments and low influence moments; respectively counting the quantity of high influence time, medium influence time and low influence time in a monitoring period; summing the high vibration influence value corresponding to the high influence moment, the medium vibration influence value corresponding to the medium influence moment and the low vibration influence value corresponding to the low influence moment to obtain a high influence total value, a medium influence total value and a low influence total value; carrying out formulated calculation analysis on the number of high influence moments, the number of medium influence moments, the number of low influence moments, the high influence total value, the medium influence total value and the low influence total value to obtain a vibration coefficient;
206: the number of influencing devices, the noise coefficient and the vibration coefficient are numerically analyzed to obtain an environmental influence value.
3. The monitoring and warning system for a sand core placing robot according to claim 1, wherein the quality coefficient of the cast formed by casting after the sand core is placed by the robot is obtained by analyzing, specifically:
401: calling the number and the detection result of the casting; counting the number of castings corresponding to the robot;
402: extracting the number of castings which are qualified and unqualified in detection from the number of castings corresponding to the robot;
403: and carrying out numerical analysis on the number of castings, the number of castings which are qualified in detection and unqualified in detection to obtain a quality coefficient, and sending the quality coefficient to a state management unit.
4. The monitoring and alarm system for a sand core placing robot according to claim 1, wherein the state value is obtained by analyzing a process of placing the sand core by the robot, and the working state of the robot is determined according to the state value and a corresponding execution strategy is generated, specifically as follows:
501: extracting a monitoring period of the historical placement sand core of the robot, and calculating a difference value between the starting time and the ending time of the monitoring period to obtain the placement time; setting a sand core complex value corresponding to each sand core, and matching the sand cores with all the set sand cores to obtain the corresponding sand core complex values;
502: carrying out formula calculation analysis on the complex value of the sand core and the sand core placement time to obtain a sand core placement efficiency ratio; comparing and analyzing the sand core placement efficiency ratio with a set efficiency interval to generate high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement; counting the times of high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively;
503: the quality coefficient, the times of placing the high-efficiency sand cores, the times of placing the medium-efficiency sand cores and the times of placing the low-efficiency sand cores are subjected to numerical analysis to obtain state values; comparing and analyzing the state value with a set state threshold, outputting a robot number when the state value is larger than the set state threshold, and marking the robot as a state abnormal robot; sequencing the corresponding state anomaly robots according to the sequence of the state values from large to small to obtain a state anomaly robot sequence table; the robot with the minimum state value is recorded as a correcting robot;
504: setting a plurality of standby robots, wherein each standby robot corresponds to a replacement value, and sequencing the standby robots according to the sequence from the large replacement value to the small replacement value to generate a standby robot sequence table;
505: traversing each abnormal state robot in the abnormal state robot sequence table, pairing with the standby robots in the standby robot sequence table one by one, and replacing the successfully paired abnormal state robots and standby robots.
5. The monitoring and alarm system for a robot for placing sand cores according to claim 4, wherein the calculating step of the complex value of the sand cores is as follows:
601: extracting the historical times of placing sand cores of the robot, and the weight and the shape of the sand cores when the sand cores are placed each time;
602: scanning the surface of the sand core by using a three-dimensional scanner, acquiring tens of thousands of data points to form point cloud data, and generating a surface model according to the point cloud data;
603: fitting the curved surface to the point cloud data by using a least square method to obtain a curvature equation, and conducting derivative calculation on the curvature equation to obtain a curvature value at each point;
604: calculating the average value of curvature values of all points to obtain the average value of the curvature of the sand core;
605: comparing and analyzing the curvature value of each point on the surface of the sand core with a set curvature threshold value, when the curvature value is larger than the set curvature threshold value, marking the point as a sharp point, and counting the number of the sharp points on the surface of the sand core;
606: and extracting the weight of the sand core, and carrying out formula calculation analysis on the weight of the sand core and the average value of the curvature of the sand core and the number of sharp points to obtain a complex value of the sand core.
6. The monitoring and warning system for a sand core placement robot according to claim 5, characterized in that the replacement value calculation step of the standby robot is as follows:
701: the using times, the using time and the maintaining times of each use, and the maintaining time of each maintenance of the standby robot are called; calculating the average value of the using time length of each use to obtain the average value of the using time length;
702: calculating the average value of the maintenance time length of each maintenance to obtain the average value of the maintenance time length;
703: extracting the maintenance time of each maintenance, calculating the difference value between the maintenance time and the maintenance time of the last maintenance to obtain maintenance interval duration, and calculating the average value of all the maintenance interval durations to obtain a maintenance interval average value;
704: and carrying out numerical analysis on the using time length average value, the maintenance time length average value and the maintenance interval average value to obtain a replacement value.
7. The monitoring and alarming method for the sand core placing robot is characterized by comprising the following steps of:
801: collecting sand core taking action, sand core placing action and environment information;
802: the influence of the environment on the placement of the sand core by the robot is quantified according to the environment information so as to obtain an environment influence value;
803: analyzing the difference between the sand core taking action and the pre-coring action of the robot to obtain an action difference coefficient; meanwhile, decomposing the action of taking the sand core and the action of placing the sand core into a plurality of small actions, and analyzing the smoothness degree between the small actions to obtain an action smoothness coefficient; comprehensively analyzing the motion difference coefficient and the motion smoothing coefficient to obtain a sand core placement value;
804: comprehensively analyzing the environmental impact value and the sand core placement value to obtain an alarm value, and generating an alarm signal according to the alarm value, wherein the alarm signal comprises a primary error alarm and a secondary error alarm; when a first-level error alarm is generated, transferring the die with the sand core to a robot with the minimum alarm value, and taking out the sand core from the casting die by the robot and placing the sand core again until the error alarm of the robot is eliminated; when the secondary error alarm is generated, the robot is controlled to take out the sand core from the casting and to reset the sand core until the error alarm of the robot is eliminated;
805: the quality analysis is carried out on the casting cast by the robot after the sand core is placed, so as to obtain the quality coefficient;
806: generating a surface model by carrying out three-dimensional scanning on the sand core, and analyzing according to the surface model to obtain a complex value of the sand core; comprehensively analyzing the complex sand core value and the sand core placement time length to obtain a sand core placement efficiency ratio, and judging the efficiency of the robot for placing the sand core according to the sand core placement efficiency ratio, wherein the efficiency of placing the sand core is high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively; counting the times of high-efficiency sand core placement, medium-efficiency sand core placement and low-efficiency sand core placement respectively; comprehensively analyzing the quality coefficient, the times of placing the high-efficiency sand cores, the times of placing the medium-efficiency sand cores and the times of placing the low-efficiency sand cores to obtain a robot with abnormal states; and replacing the matching value of the robot with the abnormal state corresponding to the standby robot.
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