CN117798731A - Intelligent processing system for special-shaped bent pipe - Google Patents
Intelligent processing system for special-shaped bent pipe Download PDFInfo
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- CN117798731A CN117798731A CN202410224268.7A CN202410224268A CN117798731A CN 117798731 A CN117798731 A CN 117798731A CN 202410224268 A CN202410224268 A CN 202410224268A CN 117798731 A CN117798731 A CN 117798731A
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- 238000012545 processing Methods 0.000 title claims abstract description 25
- 238000005520 cutting process Methods 0.000 claims abstract description 179
- 238000012502 risk assessment Methods 0.000 claims abstract description 52
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- 238000011156 evaluation Methods 0.000 claims abstract description 26
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- 230000007246 mechanism Effects 0.000 description 10
- 238000005299 abrasion Methods 0.000 description 8
- 238000012544 monitoring process Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
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- 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
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- 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
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
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Abstract
The invention relates to the technical field of pipe fitting processing, in particular to an intelligent processing system for a special-shaped bent pipe, which comprises a cutting component, an acquisition module, a track capturing module and a risk assessment module, wherein a cutting track of a cutter end node is constructed through a construction unit, the cutting feedback type of a current cutting track section is distinguished through a dominant distinguishing unit, the current operation parameters of a cutter are selected and kept through the risk assessment module, or audio information corresponding to a determined characteristic aggregation track section is acquired to draw an acoustic wave time domain diagram, whether the characteristic aggregation track section meets a preset standard is judged based on the evaluation parameters of the acoustic wave time domain diagram, so that a buzzer is controlled to give an alarm, the cutter state in the running process of the special cutting track is screened, the cutter with an obvious characterization state is accurately monitored, the cutting process which does not meet the cutting requirement is timely prompted, the reliability of the system is ensured, and the quality of the processed special-shaped bent pipe is improved.
Description
Technical Field
The invention relates to the technical field of pipe fitting machining, in particular to an intelligent machining system for a special-shaped bent pipe.
Background
With the general application of bent pipes in the fields of automobile manufacturing, household appliance manufacturing and industrial equipment, continuous processing production of bent pipes is increasingly receiving attention of technicians in related fields, in application scenes in all fields, special custom-made requirements of bent pipes with different bending degrees are increasingly increased, particularly in the application fields with requirements on part strength, special-shaped bent pipes which are integrally formed and have sufficient strength are required, the requirements of traditional bent pipe equipment cannot be met, and a processing center is required to perform concentrated cutting processing preparation.
For example, chinese patent: CN104191246a, the invention discloses a processing machine tool for special-shaped pipes, comprising a frame, a positioning mechanism, a clamping mechanism, a pipe end forming mechanism, a punching and cutting mechanism, a cutting mechanism and a control system, wherein the positioning mechanism is arranged on the frame and consists of a plurality of positioning modules, and the special-shaped pipes are limited on the positioning modules; the clamping mechanism comprises a head limiting device, a tail limiting device and a middle limiting device, and can clamp or loosen the special pipe; the pipe end forming mechanism can process and form two ends of the special pipe; the punching and cutting mechanism comprises a pushing device and a punching device, and the pushing device can push the punching device to punch holes on the special pipe; the cutting mechanism comprises a pushing device and a cutting device, and the pushing device can push the cutting device to move and cut the special-shaped pipe; the control system coordinates the control actions and sequences.
The prior art has the following problems;
in the cutting process, different cutting tracks can cause different stress of the cutter, especially when cutting a pipe fitting, the bending tracks are more, the complex cutting tracks not only abrade the cutter in the advancing direction, but also abrade the cutter in the radial direction of the tracks, so that the cutter is easy to damage, the state of the cutter under the special cutting tracks cannot be screened out in the prior art, the cutter with obvious characterization state cannot be monitored by accurate means, the accuracy of evaluating the state of the cutter is affected, and the cutter which does not meet the requirement cannot be timely reminded to an operator.
Disclosure of Invention
Therefore, the invention provides an intelligent processing system for a special-shaped bent pipe, which is used for solving the problems that the state of a cutter under a special cutting track cannot be screened, the cutter with an obvious characterization state cannot be monitored by an accurate means, and the accuracy of the cutter state evaluation is affected in the prior art.
In order to achieve the above purpose, the invention provides an intelligent processing system for a special-shaped bent pipe, comprising:
a cutting assembly including a tool and a tool holder for securing the tool;
the acquisition module comprises a stress detection unit, an image acquisition unit and an audio acquisition unit, wherein the stress detection unit is arranged at the joint of the cutter and the cutter rest and used for detecting feedback force born by the cutter, the image acquisition unit is arranged on the cutter rest and used for acquiring depth images of cutting targets of the cutter, and the audio acquisition unit is used for acquiring audio information;
the track capturing module is connected with the acquisition module and comprises a construction unit and a dominant distinguishing unit, wherein the construction unit is used for constructing a cutting track of a cutter during cutting of a special-shaped bent pipe based on the depth image, the dominant distinguishing unit is used for dividing the cutting track into a plurality of cutting track sections, distinguishing the cutting feedback type of the current cutting track section based on the average curvature of the cutting track section and the fluctuation value of feedback force, and the cutting feedback type comprises a strong dominant feedback type and a weak dominant feedback type;
a risk assessment module connected with the dominant distinguishing unit for performing risk assessment, comprising,
dividing a current cutting track segment into a plurality of track segments, determining a characteristic aggregation track segment based on the average curvature of the track segment, acquiring audio information corresponding to the characteristic aggregation track segment, drawing an acoustic wave time domain graph, and judging whether a cutting process is at risk based on an evaluation parameter of the acoustic wave time domain graph so as to control a buzzer to send out an alarm, wherein the evaluation parameter comprises an acoustic wave peak value average value and an acoustic wave peak value dispersion.
Further, the dominant distinguishing unit determines a track segment corresponding to the maximum value of the average curvature of the track segment as a characteristic aggregation track segment, screens the maximum value and the minimum value of the feedback force borne by the cutter, and determines the difference value between the maximum value and the minimum value of the feedback force borne by the cutter as a feedback force fluctuation value.
Further, the dominant discriminating unit is further configured to calculate a cutting feedback dominant characterization coefficient according to equation (1),
，
in the formula (1), U is the dominant characterization coefficient of the cutting feedback, C av C is the average curvature of the cutting track segment 0 Is a preset curvature reference value of the cutting track segment, F d For the feedback force fluctuation value of the cutting track section, F d0 And (3) for a preset feedback force fluctuation value reference value, mu is a curvature weight coefficient, gamma is a feedback force fluctuation weight coefficient, and the conditions that mu+gamma=1 are met and e is a constant are met.
Further, the dominant discriminating unit compares the cutting feedback dominant characterization coefficient with a preset cutting feedback dominant characterization coefficient threshold,
if the dominant characterization coefficient of the cutting feedback is smaller than or equal to the dominant characterization coefficient threshold of the cutting feedback, the dominant distinguishing unit determines that the current cutting track segment is of a weak dominant feedback type;
and if the dominant characterization coefficient of the cutting feedback is larger than the threshold value of the dominant characterization coefficient of the cutting feedback, the dominant distinguishing unit determines that the current cutting track section is of a strong dominant feedback type.
Further, the risk assessment module is further configured to determine whether to perform risk assessment based on the determination result of the explicit distinction unit, wherein,
and if the dominant distinguishing unit judges that the current cutting track section is of a strong dominant feedback type, the risk assessment module determines that risk assessment is required.
Further, the risk assessment module screens the average curvature of each track segment in the current cutting track segment, and determines the track segment corresponding to the maximum value of the average curvature of the track segment as the characteristic aggregation track segment.
Further, the risk assessment module is further configured to compare each peak value in the acoustic time domain graph with a preset peak value screening range,
if the wave crest value is in the wave crest value screening range, the risk assessment module determines that the wave crest value is a characteristic wave crest value;
wherein the peak value screening range is determined based on an average value of a plurality of peak values in the acoustic wave time domain graph.
Further, the risk assessment module determines the average value of the screened characteristic wave peaks as the average value of the wave peaks.
Further, the risk assessment module is further configured to calculate a characteristic peak value dispersion according to formula (2), and determine the characteristic peak value dispersion as the acoustic wave peak value dispersion;
，
in the formula (2), D c D, for the characteristic wave peak value dispersion i Peak value d of the ith characteristic peak av And n is the total number of the characteristic wave peaks and is the average value of wave peak values of the sound waves.
Further, the risk assessment module pre-stores sample assessment parameters of the cutter cutting the special-shaped bent pipe with the same specification, the risk assessment module compares each sub-parameter in the assessment parameters with the corresponding sample assessment sub-parameter in the sample assessment parameters,
if the difference between any one of the sub-parameters and the corresponding sample evaluation sub-parameter is larger than a preset difference threshold, the risk evaluation module judges that the characteristic aggregation track segment does not accord with a preset standard, and controls the buzzer to give an alarm.
Compared with the prior art, the method has the beneficial effects that the cutting assembly, the acquisition module, the track capturing module and the risk assessment module are arranged, the cutting track of the end node of the cutter is constructed through the construction unit, the cutting feedback type of the current cutting track section is distinguished through the dominant distinguishing unit based on the average curvature of the cutting track section and the feedback force fluctuation value, the current operation parameters of the cutter are selected and kept through the risk assessment module based on the feedback type of the current cutting track section, or the audio information corresponding to the characteristic aggregation track section is acquired based on the determined characteristic aggregation track section, so that an acoustic wave time domain diagram is drawn, whether the characteristic aggregation track section meets the preset standard or not is judged based on the evaluation parameters of the acoustic wave time domain diagram, the buzzer is controlled to give an alarm, further, the screening of the cutter state in the special cutting track operation process is realized, the accurate means of the cutter with obvious characterization state is monitored, the prompt is timely given to the cutting process which does not meet the cutting requirements, and the quality of the special-shaped bent pipe is improved.
In particular, the invention calculates the dominant characterization coefficient of cutting feedback based on the average curvature of the cutting track section and the fluctuation value of feedback force by the dominant differentiating unit, in the actual cutting process of the special-shaped bent pipe, the cutting track is a track with curvature or a linear track, under different cutting tracks, the wear part of the cutter and the wear speed of the cutter can be influenced differently due to the different tracks and the material property difference of the special-shaped bent pipe due to the change of the cutting track of the cutter, the larger the curvature of the cutting track, the wear of the cutter is not only in the cutting travelling direction, but also in the radial direction of the track, the wear of the cutter can be aggravated, in addition, the stress state of the cutter on the curved complex cutting track is more obvious, and the average curvature of the cutting track section and the fluctuation value of the feedback force are combined, so that the cutter state in the running process of the special cutting track is screened, and the accuracy of the state assessment of the cutter is improved.
In particular, the cutting feedback type of the current cutting track section is distinguished by calculating the cutting feedback dominant characterization coefficient, in the actual special-shaped bent pipe cutting process, as the cutter of the cutting track section with larger curvature is stressed in a complex manner, the abrasion degree is difficult to directly calculate, the more stable the cutter is stressed, the smaller the stress fluctuation value is, otherwise, the more complex the cutter is stressed, the more the stress condition changes, the larger the stress fluctuation value is, especially the audio data has higher data characterizability under the condition, and further, the accurate means of monitoring the cutter with obvious characterization state is realized by carrying out corresponding analysis on the audio data, and the accuracy of cutter state evaluation is improved.
In particular, the invention determines the characteristic polymerization track segments through the risk assessment module, acquires the audio information corresponding to the characteristic polymerization track segments, screens out the cutting track segment corresponding to the strongest data characterization, further judges by collecting the sound wave signals in the cutting process with obvious data characterization, and in the cutting process of the special-shaped bent pipe, the contact between the cutter and the special-shaped bent pipe is not uniform any more due to the abrasion of the cutter, the sound wave signals can characterize the contact condition of the cutter and the cutting target in the cutting process, thereby realizing the monitoring of accurate means on the cutter with obvious characterization state and improving the precision of the cutter state assessment.
In particular, the wave crests in the acoustic wave spectrogram are screened through the risk assessment module, in the actually collected acoustic wave time domain graph, the wave crest value difference of each wave crest in the acoustic wave time domain graph is larger due to the change of the cutting track, and the working state of the cutter corresponding to the larger or smaller wave crest value is not representative, so that the wave crest value of the wave crest value which is not in a preset range is removed, the collected wave crest is guaranteed to be scientifically representative, further, the condition of the cutter in the running process of the special cutting track is screened, and the accurate means of monitoring of the cutter with the obvious representation state are realized.
In particular, the invention compares the average value of the acoustic wave peak value and the dispersion of the acoustic wave peak value in the acoustic wave time domain graph with a pre-stored standard sample value, in the actual comparison process, the average value of the acoustic wave peak value represents the cutting sound of a cutter in the current state, the more serious the abrasion of the cutter is, the rougher the surface contacted with the special-shaped bent pipe is, the higher the generated sound is, and the dispersion of the acoustic wave peak value represents the fluctuation condition of the cutting sound of the cutter in the current state, the more serious the abrasion of the cutter is, the more uneven the surface contacted with the special-shaped bent pipe is, the more obvious the generated sound fluctuation is, the stronger the data representation of audio data is, and further, the invention realizes the screening of the cutter state in the running process of special cutting tracks, monitors the accurate means of the cutter in the obvious representation state, timely prompts the cutting process which does not meet the cutting requirement, and improves the quality of processing the special-shaped bent pipe.
Drawings
FIG. 1 is a system block diagram of an intelligent processing system for a special-shaped bent pipe, according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a functional unit of a trace capturing module according to an embodiment of the present invention;
FIG. 3 is a logic flow diagram of a dominant discriminating unit discriminating the feedback type of the current cutting trajectory segment according to the embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a risk assessment module determining a peak screening range according to an embodiment of the invention.
Detailed Description
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.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 and 2, fig. 1 is a system block diagram of an intelligent processing system for a special-shaped bent pipe according to an embodiment of the present invention, and fig. 2 is a schematic diagram of a functional unit of a track capturing module according to an embodiment of the present invention, where the intelligent processing system for a special-shaped bent pipe of the present invention includes:
a cutting assembly including a tool and a tool holder for securing the tool;
the acquisition module comprises a stress detection unit, an image acquisition unit and an audio acquisition unit, wherein the stress detection unit is arranged at the joint of the cutter and the cutter rest and used for detecting feedback force born by the cutter, the image acquisition unit is arranged on the cutter rest and used for acquiring depth images of cutting targets of the cutter, and the audio acquisition unit is used for acquiring audio information;
the track capturing module is connected with the acquisition module and comprises a construction unit and a dominant distinguishing unit, wherein the construction unit is used for constructing a cutting track of a cutter during cutting of a special-shaped bent pipe based on the depth image, the dominant distinguishing unit is used for dividing the cutting track into a plurality of cutting track sections, distinguishing the cutting feedback type of the current cutting track section based on the average curvature of the cutting track section and the fluctuation value of feedback force, and the cutting feedback type comprises a strong dominant feedback type and a weak dominant feedback type;
a risk assessment module connected with the dominant distinguishing unit for performing risk assessment, comprising,
dividing a current cutting track segment into a plurality of track segments, determining a characteristic aggregation track segment based on the average curvature of the track segment, acquiring audio information corresponding to the characteristic aggregation track segment, drawing an acoustic wave time domain graph, and judging whether a cutting process is at risk based on an evaluation parameter of the acoustic wave time domain graph so as to control a buzzer to send out an alarm, wherein the evaluation parameter comprises an acoustic wave peak value average value and an acoustic wave peak value dispersion.
Specifically, the specific structure of the stress detection unit is not limited, and of course, preferably, the structure in this embodiment is composed of a pressure collector and a data transmission module, so that the data collected by the pressure collector is transmitted to the predetermined unit through the data transmission module, which is in the prior art and is not described herein.
Specifically, the specific structure of the image acquisition unit is not limited, and preferably, in the embodiment of the invention, a camera with structured light or binocular function can be selected, and the camera is widely used in the field of industrial manufacturing and the intelligent AGV, and is not repeated here.
Specifically, the specific structure of the audio acquisition unit is not limited, and only the sound around the cutter can be acquired, which is the prior art and is not described herein.
Specifically, the method for acquiring the audio information adopted by the audio acquisition unit is not limited, and preferably, in the embodiment, the acoustic wave receiver receives the acoustic wave signal, and the received acoustic wave signal is amplified, filtered, digitized and the like to obtain the acoustic wave time domain diagram, and the technology is widely applied in the fields of industrial nondestructive testing, exploration and the like and is not repeated herein.
Specifically, the specific structures of the track capturing module and the risk assessment module are not limited, and the track capturing module and the risk assessment module can be formed by logic components, wherein the logic components can be field programmable logic components, microprocessors, processors used in computers and the like, and are not described herein.
Specifically, the method for analyzing and acquiring the evaluation parameters of the acoustic wave time domain image in the track capturing module and the risk evaluating module is not limited, and preferably, in the embodiment of the invention, an Matlab image processing tool can be selected for analysis and processing, which is the prior art and is not described herein.
Specifically, the specific structure of the cutting assembly is not limited, and the cutting assembly can be a cutter in a processing machine tool and a corresponding cutter rest, which are not described in detail in the prior art.
Specifically, in the embodiment of the present invention, the construction unit constructs the acoustic time domain graph with the acoustic signal intensity as the vertical axis and the time as the horizontal axis.
Specifically, the explicit differentiating unit determines a track segment corresponding to a maximum value of an average curvature of the track segments as a characteristic aggregated track segment, screens a maximum value of feedback force and a minimum value of feedback force of the cutter, and determines a difference between the maximum value of feedback force and the minimum value of feedback force as a feedback force fluctuation value F d 。
In particular, referring to fig. 3, which is a logic flow diagram of a dominant differentiating unit for differentiating feedback types of current cutting trajectory segments according to an embodiment of the present invention, the dominant differentiating unit is further configured to calculate a cutting feedback dominant characterization coefficient according to formula (1),
，
in the formula (1), U is the dominant characterization coefficient of the cutting feedback, C av C is the average curvature of the cutting track segment 0 Is a preset curvature reference value of the cutting track segment, F d For the feedback force fluctuation value of the cutting track section, F d0 And (3) for a preset feedback force fluctuation value reference value, mu is a curvature weight coefficient, gamma is a feedback force fluctuation weight coefficient, and the conditions that mu+gamma=1 are met and e is a constant are met.
Preferably, in the embodiment of the present invention, the preset curvature reference value C of the cutting track segment 0 The value range of (2) is [3,5 ]]In m -1 Preset feedback force fluctuation value reference value F d0 Based on the pre-test, pre-testing the fluctuation value of the feedback force applied by the cutter in the preset time length on the linear cutting track, and determining the fluctuation value of the feedback force applied in the preset time length as the feedback force fluctuation value reference value F d0 The value range of the preset time length is [3,5 ]]The interval unit is s.
Specifically, the calculation mode of the average curvature of the cutting track section is not limited, preferably, the explicit differentiating unit may calculate the curvature of each curve section with a sufficiently small length by using an interpolation method, characterize the curve curvature at the starting point of each curve section, calculate the average value of the curve curvature at the starting point of each curve section, determine the average value as the average curvature of the cutting track section, and calculate the slope and the curvature of a random curve by using the interpolation method as the prior art, which is not described herein.
Specifically, the invention calculates the dominant characterization coefficient of cutting feedback based on the average curvature of the cutting track section and the fluctuation value of feedback force through the dominant differentiating unit, in the actual special-shaped bent pipe cutting process, the cutting track is a track with curvature or a linear track, under different cutting tracks, the wear part of the cutter and the wear speed of the cutter can all cause different influence results due to different tracks and the material property difference of the special-shaped bent pipe due to the change of the cutting track of the cutter, the larger the curvature of the cutting track, the wear of the cutter is not only in the cutting travelling direction, but also in the radial direction of the track, the wear of the cutter can be aggravated, in addition, the stress state of the cutter on the curved complex cutting track is more obvious, and the average curvature of the cutting track section and the fluctuation value of the feedback force are combined, so that the cutter state in the running process of the special cutting track is screened, and the accuracy of the state evaluation of the cutter is improved.
Specifically, the dominant differentiating unit compares the cutting feedback dominant characterization coefficient U with a preset cutting feedback dominant characterization coefficient threshold U 0 In the comparison of the two types of materials,
if the cutting feedback dominant characterization coefficient U is less than or equal to the cutting feedback dominant characterization coefficient threshold U 0 The dominant distinguishing unit determines that the current cutting track section is of a weak dominant feedback type;
if the dominant characterization coefficient U of the cutting feedback is greater than the dominant characterization coefficient threshold U of the cutting feedback 0 And the dominant distinguishing unit determines that the current cutting track segment is of a strong dominant feedback type.
Preferably, in an embodiment of the present invention, the cutting feedback dominant characterization coefficient threshold value U 0 The value range of (5) is [1.15,1.2 ]]。
Specifically, the cutting feedback type of the current cutting track section is distinguished by calculating the cutting feedback dominant characterization coefficient, in the actual special-shaped bent pipe cutting process, as the cutter of the cutting track section with larger curvature is stressed in a complex manner, the abrasion degree is difficult to directly calculate, the more stable the cutter is stressed, the smaller the stress fluctuation value is, otherwise, the more complex the cutter is stressed, the more the stress condition changes, the larger the stress fluctuation value is, especially the audio data has higher data characterizability under the condition, and further, the accurate means of monitoring the cutter with obvious characterization state is realized by carrying out corresponding analysis on the audio data, and the accuracy of cutter state evaluation is improved.
In particular, the risk assessment module is further configured to determine whether to perform risk assessment based on the determination result of the dominant distinguishing unit, wherein,
and if the dominant distinguishing unit judges that the current cutting track section is of a strong dominant feedback type, the risk assessment module determines that risk assessment is required.
Specifically, the risk assessment module screens the average curvature of each track segment in the current cutting track segment, and determines the track segment corresponding to the maximum value of the average curvature of the track segment as the characteristic aggregation track segment.
Specifically, the method determines the characteristic polymerization track segments through the risk assessment module, acquires the audio information corresponding to the characteristic polymerization track segments, screens out the cutting track segment corresponding to the strongest data characterization, further judges by acquiring the sound wave signals in the cutting process with obvious data characterization, and in the cutting process of the special-shaped bent pipe, the contact between the cutter and the special-shaped bent pipe is not uniform due to the abrasion of the cutter, the condition that the cutter is contacted with a cutting target in the cutting process can be represented by the sound wave signals, and further, the accurate means of monitoring the cutter with obvious characterization state is realized, and the precision of cutter state assessment is improved.
Specifically, referring to fig. 4, which is a schematic diagram illustrating a risk assessment module determining a peak screening range according to an embodiment of the present invention, the risk assessment module is further configured to compare each peak d in the acoustic time domain graph with a preset peak screening range,
if the crest value d is in the crest value screening range, the risk assessment module determines that the crest value is a characteristic crest value;
wherein the peak value screening range is based on the average value d of a plurality of peak values in the acoustic wave time domain diagram m And (5) determining.
Preferably, in the embodiment of the present invention, the peak value of the peak is an acoustic signal intensity value corresponding to the peak apex of the peak, and the filtering range of the peak value is [0.5d ] m ,1.5d m ]For example, please continue to refer to fig. 4, which is a schematic diagram illustrating a risk assessment module determining a peak screening range according to an embodiment of the present invention, in fig. 4 d 1 、d 2 D 3 Is the characteristic peak value.
Specifically, the wave crests in the acoustic wave spectrogram are screened through the risk assessment module, in the actually collected acoustic wave time domain graph, the wave crest value difference of each wave crest in the acoustic wave time domain graph is larger or smaller due to the change of the cutting track, and the working state of the cutter corresponding to the larger or smaller wave crest value is not representative, so that the wave crest value of the wave crest value which is not in a preset range is removed, the collected wave crest is guaranteed to be scientifically representative, further, the condition of the cutter in the running process of the special cutting track is screened, and the accurate means of monitoring of the cutter with the obvious representation state are realized.
Specifically, the risk assessment module determines the average value of the screened characteristic wave peaks as the average value of the wave peaks.
Specifically, the risk assessment module is further configured to calculate a characteristic peak value dispersion according to formula (2), and determine the characteristic peak value dispersion as the acoustic wave peak value dispersion;
，
in the formula (2), D c D, for the characteristic wave peak value dispersion i Peak value d of the ith characteristic peak av And n is the total number of the characteristic wave peaks and is the average value of wave peak values of the sound waves.
Specifically, the method and the device for processing the special-shaped bent pipe have the advantages that the average value of the acoustic wave peak value and the dispersion of the acoustic wave peak value in the acoustic wave time domain graph are collected and compared with the pre-stored standard sample value, in the actual comparison process, the average value of the acoustic wave peak value represents the cutting sound of the cutter in the current state, the more serious the abrasion of the cutter is, the rougher the surface contacted with the special-shaped bent pipe is, the higher the generated sound is, the dispersion of the acoustic wave peak value represents the fluctuation condition of the cutting sound of the cutter in the current state, the more serious the abrasion of the cutter is, the more uneven the surface contacted with the special-shaped bent pipe is, the more obvious the generated sound fluctuation is, the stronger the data representation of audio data is, the cutter state in the running process of the special cutting track is further screened, the cutter with obvious representation state is monitored, the cutting process which does not meet the cutting requirement is timely prompted, and the quality of the special-shaped bent pipe is improved.
In particular, the risk assessment module pre-stores sample assessment parameters of the cutter cutting the special-shaped bent pipe with the same specification, the risk assessment module compares each sub-parameter in the assessment parameters with the corresponding sample assessment sub-parameter in the sample assessment parameters,
if the difference between any one of the sub-parameters and the corresponding sample evaluation sub-parameter is larger than a preset difference threshold, the risk evaluation module judges that the characteristic aggregation track segment does not accord with a preset standard, and controls the buzzer to give an alarm.
Preferably, in the embodiment of the present invention, the sample evaluation parameter is obtained by pre-testing, the acoustic wave time domain diagram of the cutter in the same track section for cutting the same-specification special-shaped bent pipe is measured in an experimental environment, the evaluation parameter in the experiment is extracted, the extracted evaluation parameter is determined as the sample evaluation parameter, the sample evaluation parameter includes a sample acoustic wave peak value average value and a sample acoustic wave peak value dispersion, the difference threshold includes a difference threshold of the acoustic wave peak value average value and a difference threshold of the acoustic wave peak value dispersion, and in this embodiment, the difference threshold of the acoustic wave peak value average value does not exceed 10% of the sample acoustic wave peak value average value in the sample evaluation parameter, and the difference threshold of the acoustic wave peak value dispersion does not exceed 15% of the sample acoustic wave peak value dispersion in the sample evaluation parameter.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. Intelligent processing system of dysmorphism return bend, its characterized in that includes:
a cutting assembly including a tool and a tool holder for securing the tool;
the acquisition module comprises a stress detection unit, an image acquisition unit and an audio acquisition unit, wherein the stress detection unit is arranged at the joint of the cutter and the cutter rest and used for detecting feedback force born by the cutter, the image acquisition unit is arranged on the cutter rest and used for acquiring depth images of cutting targets of the cutter, and the audio acquisition unit is used for acquiring audio information;
the track capturing module is connected with the acquisition module and comprises a construction unit and a dominant distinguishing unit, wherein the construction unit is used for constructing a cutting track of a cutter during cutting of a special-shaped bent pipe based on the depth image, the dominant distinguishing unit is used for dividing the cutting track into a plurality of cutting track sections, distinguishing the cutting feedback type of the current cutting track section based on the average curvature of the cutting track section and the fluctuation value of feedback force, and the cutting feedback type comprises a strong dominant feedback type and a weak dominant feedback type;
a risk assessment module connected with the dominant distinguishing unit for performing risk assessment, comprising,
dividing a current cutting track segment into a plurality of track segments, determining a characteristic aggregation track segment based on the average curvature of the track segment, acquiring audio information corresponding to the characteristic aggregation track segment, drawing an acoustic wave time domain graph, and judging whether a cutting process is at risk based on an evaluation parameter of the acoustic wave time domain graph so as to control a buzzer to send out an alarm, wherein the evaluation parameter comprises an acoustic wave peak value average value and an acoustic wave peak value dispersion.
2. The special-shaped bent pipe intelligent processing system according to claim 1, wherein the explicit distinguishing unit determines a track segment corresponding to the maximum value of the average curvature of the track segment as a characteristic aggregation track segment, screens the maximum value and the minimum value of the feedback force applied to the cutter, and determines the difference value between the maximum value and the minimum value of the feedback force applied to the cutter as a feedback force fluctuation value.
3. The intelligent processing system for the special-shaped bent pipe according to claim 2, wherein the dominant distinguishing unit is further used for calculating a cutting feedback dominant characterization coefficient according to a formula (1),
，
in the formula (1), U is the dominant characterization coefficient of the cutting feedback, C av C is the average curvature of the cutting track segment 0 For a predetermined cutting railTrace segment curvature reference value, F d For the feedback force fluctuation value of the cutting track section, F d0 And (3) the feedback force fluctuation value is a preset feedback force fluctuation value reference value, mu is a curvature weight coefficient, gamma is a feedback force fluctuation weight coefficient, and e is a constant.
4. The intelligent processing system for the special-shaped bent pipe according to claim 3, wherein the dominant distinguishing unit compares the cutting feedback dominant characterization coefficient with a preset cutting feedback dominant characterization coefficient threshold value,
if the dominant characterization coefficient of the cutting feedback is smaller than or equal to the dominant characterization coefficient threshold of the cutting feedback, the dominant distinguishing unit determines that the current cutting track segment is of a weak dominant feedback type;
and if the dominant characterization coefficient of the cutting feedback is larger than the threshold value of the dominant characterization coefficient of the cutting feedback, the dominant distinguishing unit determines that the current cutting track section is of a strong dominant feedback type.
5. The shaped elbow intelligent processing system according to claim 1, wherein the risk assessment module is further configured to determine whether to perform risk assessment based on the determination of the explicit distinction unit, wherein,
and if the dominant distinguishing unit judges that the current cutting track section is of a strong dominant feedback type, the risk assessment module determines that risk assessment is required.
6. The intelligent processing system of the special-shaped bent pipe according to claim 1, wherein the risk assessment module screens the average curvature of each track segment in the current cutting track segment, and determines the track segment corresponding to the maximum value of the average curvature of the track segment as the characteristic aggregation track segment.
7. The intelligent processing system of the special-shaped bent pipe according to claim 6, wherein the risk assessment module is further configured to compare each peak value in the acoustic wave time domain graph with a preset peak value screening range,
if the wave crest value is in the wave crest value screening range, the risk assessment module determines that the wave crest value is a characteristic wave crest value;
wherein the peak value screening range is determined based on an average value of a plurality of peak values in the acoustic wave time domain graph.
8. The profiled elbow intelligent machining system according to claim 7, wherein the risk assessment module determines an average of the screened plurality of characteristic wave peaks as an average of wave peaks.
9. The profiled elbow intelligent machining system of claim 8, wherein the risk assessment module is further configured to calculate a characteristic peak value dispersion according to formula (2), and determine the characteristic peak value dispersion as the acoustic wave peak value dispersion;
，
in the formula (2), D c D, for the characteristic wave peak value dispersion i Peak value d of the ith characteristic peak av And n is the total number of the characteristic wave peaks and is the average value of wave peak values of the sound waves.
10. The intelligent processing system of the special-shaped bent pipe according to claim 9, wherein the risk assessment module is pre-stored with sample assessment parameters of the cutter cutting the special-shaped bent pipe with the same specification, the risk assessment module compares each sub-parameter in the assessment parameters with the corresponding sample assessment sub-parameter in the sample assessment parameters,
if the difference between any one of the sub-parameters and the corresponding sample evaluation sub-parameter is larger than a preset difference threshold, the risk evaluation module judges that the characteristic aggregation track segment does not accord with a preset standard, and controls the buzzer to give an alarm.
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