CN113189953B - Winding machine process optimization method based on double-code joint control and winding machine - Google Patents
Winding machine process optimization method based on double-code joint control and winding machine Download PDFInfo
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Abstract
The invention belongs to the field of numerical control machining process optimization, and particularly discloses a winding machine process optimization method based on double-code joint control and a winding machine, which comprise the following steps: s1, acquiring a first processing code and a winding machine structure parameter, and further estimating the speed; s2, identifying a speed fluctuation interval in the estimated speed, smoothing the speed fluctuation interval to obtain a processing feed speed, and obtaining a second processing code according to the processing feed speed; and S3, controlling the winding machine to process the part through the first processing code and the second processing code together, and finishing the process optimization of the winding machine. The invention is based on double-code joint control, directly smoothens winding processing from a speed layer, avoids speed fluctuation and machine tool vibration, and improves the processing quality of a winding machine and the service life of the machine tool.
Description
Technical Field
The invention belongs to the field of numerical control machining process optimization, and particularly relates to a winding machine process optimization method based on double-code joint control and a winding machine.
Background
In traditional numerical control winding course of working, the G code will directly influence numerical control system's speed planning, and the not good G code of quality often can cause the speed fluctuation in the course of working, leads to aspects such as machining efficiency, profile accuracy, vibration and surface quality to receive harmful effects to produce a large amount of unqualified products, the resource-wasting, the violent vibration of course of working can even also cause the destruction of certain degree to the lathe body.
The numerical control machining is a relatively complex process, and comprises the work of determining the process scheme and the machining code in the early stage, optimizing the process parameters in the later stage and the like, and the process optimization in the later stage adjusts the process scheme and the machining code in the early stage. Therefore, the optimization of the process parameters of the numerical control machining is an extremely important part of work. In recent years, many studies on process optimization have been made in the academic world, but most of the studies have been made on a method for generating a smoothing processing path, which can only smooth a processing path to improve the continuity of the processing speed of each axis of a machine tool, but cannot improve the speed fluctuation of each axis.
However, a small number of optimization methods directly studied from the perspective of machining speed also have many disadvantages, for example, patent CN110568761A discloses a fuzzy control-based feed speed online optimization method, which implements constant power control of a cutting process by manufacturing a constant power fuzzy controller and applying a fuzzy control algorithm. For example, patent CN106094737A discloses a numerical control machining speed optimization control method under a specified machining error condition, which determines whether to perform coarse interpolation analysis according to a shape line, and then performs polygonal line machining speed optimization control. However, both of the two methods cannot find a speed fluctuation interval and perform targeted optimization, and the application objects are cutting processing, but winding processing is different from cutting processing, winding processing is to perform speed planning on a single shaft instead of planning the speed of a synthetic shaft, and the winding processing speed is generally higher than that of metal cutting, and the influence of speed fluctuation on processing quality is larger, so that the method is difficult to use in numerical control winding processing, and the research on the speed fluctuation of the numerical control winding processing is very urgent.
Theoretically, the numerical control system needs to optimize the feed rate profile under the condition of satisfying the machine tool performance constraints, so as to reduce the machining time as much as possible while obtaining a smooth feed motion. However, in the actual machining process, fluctuations in the feed speed are always unavoidable, so that a completely smooth feed motion is not present. The reason for causing the feed speed fluctuation is mainly the limitation of an interpolation algorithm, the traditional numerical control system only provides linear and circular interpolation, and the linear and circular interpolation can be well fitted for simple curves such as linear curves, circular curves and the like, so that the machining requirement can be met; however, for complex curves which are not straight lines and circular arcs, if straight lines or circular arcs are adopted for approximation, the obtained result is an approximate value of a theoretical processing path, so that the theoretical feeding speed is not completely consistent with the actual feeding speed, and the fluctuation of the feeding speed is generated. The feed speed fluctuation brings many difficulties and hazards to processing, such as generating large acceleration and jerkiness to cause tool shake, making a processing path unsmooth and reducing the processing quality grade of a workpiece. Therefore, a process optimization method for numerical control winding processing capable of solving the above problems is needed.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a winding machine process optimization method based on double-code joint control and a winding machine, and aims to optimize the winding machine process directly from the angle of processing speed, reduce the fluctuation of the feeding speed in the processing process of the winding machine and improve the processing quality.
In order to achieve the above object, according to an aspect of the present invention, a method for optimizing a winding machine process based on dual code joint control is provided, which includes the following steps:
s1, acquiring a first processing code and a winding machine structure parameter, and further estimating the speed;
s2, identifying a speed fluctuation interval in the estimated speed, smoothing the speed fluctuation interval to obtain a processing feed speed, and obtaining a second processing code according to the processing feed speed;
and S3, controlling the winding machine to process the part through the first processing code and the second processing code together, and finishing the process optimization of the winding machine.
Further preferably, when the speed estimation is performed, the geometric constraint estimated speed and the physical constraint estimated speed are calculated, and then the minimum value of the geometric constraint estimated speed, the physical constraint estimated speed and the feeding speed in the first processing code is used as the estimated speed.
As a further preferred, calculating the geometric constraint estimated speed comprises the steps of:
unifying the stroke units of the driving shaft and each following shaft, then performing arc fitting on the driving shaft and each following shaft in a two-dimensional space respectively to obtain the synthesis speed of the driving shaft and each following shaft, calculating the component of the synthesis speed on the driving shaft, and taking the minimum value of the component of the synthesis speed on the driving shaft as the geometric constraint estimation speed.
As a further preferred, calculating the physical constraint estimated speed comprises the steps of:
(1) and (3) speed verification: calculating the synthetic feeding limit speed corresponding to each following shaft according to the feeding limit speed of each following shaft, and taking the component of the lowest synthetic feeding limit speed on the driving shaft as the initial driving shaft speed constraint limit;
(2) and (3) acceleration verification: inversely calculating the movement speed of each following shaft according to the initial driving shaft speed constraint limit, and calculating the movement speed increment of each following shaft according to the first processing code so as to obtain the acceleration of each following shaft; and combining the motion speed, the acceleration and the acceleration limit of each following shaft to obtain the feed speed of each following shaft, further obtaining a plurality of driving shaft speed constraints, and taking the minimum value in the driving shaft speed constraints as the physical constraint estimated speed.
As a further preference, identifying a speed fluctuation interval in the estimated speed comprises the steps of:
and sliding a search interval window at a preset moving interval, searching the whole interval of the estimated speed to obtain multiple groups of corresponding maximum values and minimum values in the estimated speed interval, and further obtaining a speed fluctuation interval between constant speed intervals in the estimated speed.
Further preferably, the searching the estimated speed interval specifically includes the following steps:
(1) sliding a search interval window, if the current interval is a constant-speed interval, not recording, and moving the search interval window to the right to continue searching; if the current interval is not a constant speed interval, namely a speed change point is encountered for the first time, the local maximum pointer and the local minimum pointer respectively point to the first maximum and minimum of the current search interval window;
(2) continuously sliding the search interval window, and searching a maximum value and a minimum value according to the following method;
when searching for the maximum value, comparing the speed maximum values in the current interval and the previous interval, if the speed maximum value in the current interval is not less than the speed maximum value in the previous interval, pointing the local maximum pointer to the maximum value in the current interval, and continuing to search for the maximum value; if the maximum speed value in the current interval is smaller than the maximum speed value in the previous interval, the maximum value in the previous interval is the maximum value, the local maximum pointer is cleared, and then the minimum value corresponding to the maximum value is searched;
when searching the minimum value, comparing the minimum values of the speeds in the current and previous intervals, if the minimum value of the speed in the current interval is not greater than the minimum value of the speed in the previous interval, pointing the local maximum pointer to the minimum value in the current interval, and continuing to search the minimum value; if the speed minimum value in the current interval is greater than the speed minimum value in the previous interval, taking the minimum value in the previous interval as a minimum value, clearing the local minimum value pointer, and then searching for a maximum value corresponding to the minimum value;
(3) repeating the step (2) until the current interval is the constant-speed interval again, and finishing one-time interval searching;
(4) and (4) repeating the steps (1) to (3) until the whole estimated speed interval is searched to obtain multiple groups of corresponding maximum values and minimum values, wherein a speed fluctuation interval is formed between each group of maximum values and minimum values.
Preferably, the smoothing process for the speed fluctuation section includes the following steps:
carrying out smoothing processing on a speed fluctuation interval in the estimated speed, comparing the speed after the smoothing processing with the estimated speed, if the speed after the smoothing processing is higher than the estimated speed at a certain position, further searching a minimum planning interval which is not overspeed around the speed fluctuation interval, and carrying out smoothing processing on the planning interval to finish the smoothing processing; and if the speed after the smoothing processing is not greater than the estimated speed, directly finishing the smoothing processing.
Further preferably, the minimum planning section around the speed fluctuation section in which overspeed is not generated is searched by a binary search method.
More preferably, the speed fluctuation section is smoothed by S-shaped acceleration/deceleration curve planning.
According to another aspect of the invention, a winding machine is provided, which comprises a processor and a dual-code joint control process optimization module, wherein the dual-code joint control process optimization module executes the dual-code joint control-based winding machine process optimization method when being called by the processor.
Generally, compared with the prior art, the above technical solution conceived by the present invention mainly has the following technical advantages:
1. the invention optimizes the process of the winding machine from the angle of the processing speed, specifically, each speed fluctuation interval in the estimated speed is firstly identified, then each speed fluctuation interval is respectively subjected to smooth processing, the winding processing is smoothened directly from the speed level, and the process parameters are directly optimized by adopting a second processing code, so that the speed fluctuation is avoided, the machine tool vibration caused by overlarge acceleration due to the speed fluctuation is avoided, the processing quality of the winding machine is improved, and the service life of the machine tool is prolonged.
2. The invention adopts double-code joint control, combines the original G code and the second processing code obtained according to the processing feeding speed in the numerical control system for processing, can optimize the process of the winding machine under the condition of not modifying the original G code, realizes the process optimization with lower cost, and is convenient for practical popularization and application.
3. Because the processing of the winding machine can be divided into a rotating shaft (namely a driving shaft for speed planning) and other moving shafts (namely a following shaft for following the driving shaft to move), the invention provides a specific speed estimation method aiming at the characteristics of the winding machine and simultaneously meets the requirements of geometric space, physical limit and preset limit speed; and after the smoothing, the speed planning interval is checked and adjusted according to the estimated speed, so that the smooth speed is not overspeed.
4. The method for identifying the speed fluctuation interval is high in efficiency, the identified interval is accurate, the speed cannot be overspeed after being optimized, and the influence on the processing efficiency can be reduced as much as possible.
Drawings
Fig. 1 is a flow chart of a winding machine process optimization method based on dual-code joint control according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of estimating the geometric constraints of the velocity planning axes according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of estimating physical constraints according to an embodiment of the present invention;
FIG. 4 is a velocity waveform diagram and a search queue interval for a first step in identifying a fluctuation interval according to an embodiment of the present invention;
FIG. 5 is a data structure diagram of the graph of FIG. 4 in a code implementation;
FIG. 6 is a velocity waveform diagram and a search queue interval at the second step of the fluctuation interval identification according to the embodiment of the present invention;
FIG. 7 is a data structure diagram of the graph of FIG. 6 in a code implementation;
FIG. 8 is a velocity waveform diagram and a search queue interval for the third step in identifying a fluctuation interval according to an embodiment of the present invention;
FIG. 9 is a velocity waveform diagram and a search queue interval for the fourth step in identifying a fluctuation interval in accordance with the present invention;
FIG. 10 is a data structure diagram of the graph of FIG. 9 in a code implementation;
FIG. 11 is a velocity waveform diagram and a search queue interval for the case of the fifth step when performing the fluctuation interval identification according to the embodiment of the present invention;
FIG. 12 is a velocity waveform diagram and a search queue interval for the case of the sixth step in identifying a fluctuation interval according to the embodiment of the present invention;
FIG. 13 is a data structure diagram of the code implementation of the graph of FIG. 12;
FIG. 14 is a velocity waveform diagram and a search queue interval in the case of the seventh step when performing the fluctuation interval identification in the embodiment of the present invention;
FIG. 15 is a data structure diagram of the code implementation of the graph of FIG. 14.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention provides a winding machine process optimization method based on double-code joint control, which comprises the following steps as shown in figure 1:
s1, acquiring a first processing code G code and a winding machine structure parameter;
and specifically, acquiring the point position and line number information of the G code, and reading the maximum feeding limit speed and the maximum acceleration limit of each shaft of the winding machine tool.
S2, estimating the speed, which comprises the following steps:
(1) calculating the geometric constraint estimation speed:
unifying the stroke units of the driving shaft and each following shaft, then performing arc fitting on the driving shaft and each following shaft in a two-dimensional space respectively to obtain the synthesis speed of the driving shaft and each following shaft, calculating the component of the synthesis speed on the driving shaft, and taking the minimum value of the component of the synthesis speed on the driving shaft as the geometric constraint estimation speed. Specifically, the method comprises the following steps:
the unit unification is carried out, the linear axis and the rotating axis are unified, for example, the C axis and the Z axis are fitted, and then the C axis stroke unit is converted into mm from degree.
Performing arc fitting on a driving shaft and each following shaft in a two-dimensional space, wherein the driving shaft refers to a shaft for speed planning, and the following shaft refers to a shaft for speed planning according to the speed of the driving shaft, namely a shaft moving along with the driving shaft; the axis C of the rotating shaft is used as a driving shaft, and other axes are used as following shafts.
Taking the C-axis and the Z-axis as examples, the resultant velocity of the C-axis and the Z-axis
Wherein R isczThe radius of an arc obtained after arc fitting is carried out on a C axis and a Z axis in a two-dimensional space, and a is a centripetal acceleration limit; the resultant velocity is then resolved into a C-axis component, the C-axis and Z-axis components of the resultant velocity
Where len is the arc length increment and Δ C is the increment of the C-axis stroke.
Thirdly, performing the above calculation on all the following axes to obtain the feeding speed meeting the geometric constraint of each following axis, and as shown in fig. 2, taking the minimum value endv of all the feeding speeds min { Vcz _ c, Vcx _ c, and Vcy _ c.
(2) Calculating the physical constraint estimated speed, comprising the following steps:
firstly, speed verification: calculating the synthetic feeding limit speed corresponding to each following shaft according to the feeding limit speed of each following shaft, and taking the component of the lowest synthetic feeding limit speed on the driving shaft as the initial driving shaft speed constraint limit; specifically, the method comprises the following steps:
estimating the physically constrained speed limit from the feed direction of each G-code: an increment inc of G-code data is { Δ X, Δ Y, Δ z. }, assuming feeding in inc direction, the maximum composite feed speed for each axis can be estimated in case each axis is fed at its speed limit. Taking the x-axis as an example, the composite feed limit speed is obtained according to the x-axis feed limit speed max VX
Wherein theta is an included angle between the X-axis feeding direction and the synthetic axis feeding direction, and delta X is the feeding increment of one G code segment of the X axis; therefore, given that the feed limit speed of each axis is { maxVX, maxVY, maxVZ, maxVA, maxVB, maxVC }, the composite feed limit corresponding to each axis can be calculated: { fdmaxX, fdmaxY, fdmaxZ, fdmaxA, fdmaxB, fdmaxC }. The lowest composite feed speed is resolved to the C-axis, and the C-axis speed constraint limit Vp1 is obtained.
Secondly, acceleration verification: inversely calculating the movement speed of each following shaft according to the initial driving shaft speed constraint limit, and calculating the movement speed increment of each following shaft according to the first processing code so as to obtain the acceleration of each following shaft; and combining the motion speed, the acceleration and the acceleration limit of each following shaft to obtain the feed speed of each following shaft, further obtaining a plurality of driving shaft speed constraints, and taking the minimum value in the driving shaft speed constraints as the physical constraint estimated speed. Specifically, the method comprises the following steps:
and inversely calculating the movement speeds { Vx, Vy … } of other follow-up axes according to the speed constraint limit Vp1 and the movement direction inc of the speed planning axis, wherein the following equations are as follows:
further, according to the motion speed, a motion speed increment { Δ Vx, Δ Vy … } of the following axis is calculated by a plurality of G code points, and further, following axis acceleration { ax, ay, … } is obtained, as follows:
ax＝ΔVx/Δt
ay＝ΔVy/Δt
wherein, Δ t is an interpolation period, which is set according to the G code programming speed F value;
limiting the acceleration of each following axis according to the acceleration limit { max, max ay, … }, and calculating the motion speed of each following axis according to the acceleration after speed limiting as shown in fig. 3, taking the z axis as an example, the motion speed Vz' of the z axis is calculated by the following formula:
ΔVz'＝max az×Δt
and then calculating a speed planning axis movement speed limit Vp 'according to the following axis movement speed, and then physically constraining the minimum Vp' calculated for each axis.
(3) And taking the minimum value of the geometric constraint estimated speed, the physical constraint estimated speed and the G code programming speed F value as the estimated speed.
S3, identifying a speed fluctuation interval in the estimated speed: the method identifies the speed fluctuation interval based on the sliding window; specifically, a sliding window is named as a search interval window, points which need to be used as the boundary of a second-class speed planning interval are named as second-class speed planning interval nodes, local maximum and minimum values mentioned in the method refer to maximum values and minimum values of data in the search interval window, local maximum pointers and local minimum pointers refer to pointers pointing to the local maximum and minimum value data in code implementation, and maximum pointers and minimum pointers refer to pointers pointing to maximum value and minimum value data in code implementation. There are some cases as follows:
first, the window of search interval is uniform interval
After finding the maximum value and the minimum value of the search interval window, if the interval is judged to be a constant-speed interval, no record is made, and the queue is moved to the right;
searching interval window meets speed change point for the first time
Finding out the local maximum and minimum values of a search interval window, judging that the interval is not a constant speed interval, wherein a local maximum pointer and a local minimum pointer point to the first maximum and minimum values of the current search interval window, if the last interval is the constant speed interval, the last time is the first time when a speed change point is met, and therefore a pointer of a second type of speed planning interval is newly built to point to the front point of the rightmost local maximum value of the current queue interval;
searching interval window updating maximum point
Finding out the minimum value of the local maximum value of a search queue interval, judging that the interval is not a uniform-speed interval, if the maximum value of the interval is not more than or equal to the last local maximum value, determining that the last local maximum value is a maximum value point and updating the maximum value point, newly building a maximum value pointer pointing to the last maximum value, newly building a second-type speed planning interval node pointer pointing to a point before the maximum value, emptying the local maximum value pointer, enabling the local minimum value pointer to point to the minimum value of the current queue interval, and changing the next extreme value to be searched into a minimum value;
searching window updating minimum value point of interval
If the current extreme point needing to be searched is a minimum value, finding the local maximum and minimum value of the search queue interval, judging that the interval is not a uniform-speed interval, and the minimum value of the current interval is not less than or equal to the last local minimum value, so that a newly-built minimum value pointer points to the last local minimum value, a local minimum value pointer is emptied, the local maximum value pointer points to the maximum value of the current queue interval, and the next extreme value needing to be searched is changed into a maximum value;
searching interval window ending one-time interval searching
Finding out the minimum value of the local maximum value of the search queue interval, judging that the interval is a constant speed interval, but not the last time, finishing the interval search this time, newly building a maximum value pointer to point to the local maximum value of the current time, and newly building a node pointer of a second type of speed planning interval to point to the next point of the latest extreme point. And finishing the searching of the second-class speed planning interval at one time.
And repeating the process until the search of the whole estimated speed interval is completed to obtain multiple groups of maximum values and minimum values, wherein a speed fluctuation interval is formed between the maximum values and the minimum values of each group.
And S4, smoothing the speed fluctuation section: by means of the second type speed planning function of the Huazhong numerical control system, namely, the user specifies the speed of a section of second type speed planning interval and the interval boundary, and the interval is subjected to smooth S-shaped acceleration and deceleration according to the speed specified by the user through S-shaped acceleration and deceleration speed planning. In the invention, each speed fluctuation interval is taken as a designated second-class speed planning interval, and whether the second-class speed planning interval exceeds the speed is checked and adjusted to ensure that the second-class speed planning does not exceed the speed, and the method specifically comprises the following steps:
(1) carrying out smoothing treatment on the second type speed planning interval, comparing the speed after the smoothing treatment with the estimated speed curve, and if the speed curve after the smoothing treatment has a position larger than the estimated speed curve (namely overspeed), turning to the step (2); and (4) if the speed after the smoothing processing is not greater than the estimated speed, the current second-class speed planning interval is the final second-class speed planning interval, and the step (3) is carried out.
(2) The minimum interval which can not cause the overspeed of the artificially planned speed is found by using binary search, and the specific method comprises the following steps: taking the moving left node of the second type speed plan as an example, it is denoted as N2, and its neighboring second type speed plan node is N1;
calculating new node Nm as (N1+ N2)/2, rounding the new node Nm downwards, then replacing N2 with Nm to update the node of the second speed planning interval, carrying out smoothing processing on the new interval, if the new interval is still overspeed, carrying out the second step, otherwise carrying out the third step;
making N2 equal to Nm-1, repeating the step (i) until N1 equal to N2, obtaining a new second-type speed planning node N2, and further determining a final second-type speed planning interval;
and thirdly, making N1 equal to Nm, repeating the processes until N1 equal to N2, obtaining a new second-type speed planning node N2, and further determining a final second-type speed planning interval.
(3) And smoothing the final second-class speed planning interval, and smoothing all the second-class speed planning intervals in the estimated speed to obtain the processing feeding speed.
And S5, outputting a second processing code according to the processing feeding speed, wherein the second processing code refers to codes except the G code, which guide processing together with the G code, the second processing code used in the invention is the i code of the Huazhong numerical control system, and the double-code joint control is utilized to improve the processing defect and complete the process optimization of the winding machine.
In order to make the process of identifying the speed fluctuation interval in the estimated speed in step S3 of the present invention clearer, taking a typical speed reduction and speed increase interval as an example, the code implementation links the estimated speed data with a data structure of a linked list, and the specific steps are as follows:
first, as shown in fig. 4 and 5, searching for the minimum value of the local maximum value of the queue interval, determining that the interval is a constant-speed interval, and moving the queue to the right;
secondly, as shown in fig. 6 and 7, searching the local maximum and minimum values of the queue interval, judging that the interval is not a uniform speed interval, wherein the local maximum pointer and the local minimum pointer point to the maximum value and the minimum value of the current queue, and the last interval is the uniform speed interval, so that the pointer of the newly-built second-type speed planning interval points to the front point of the rightmost local maximum value of the current queue interval;
thirdly, as shown in fig. 8, the current extreme point to be searched is uncertain, the local maximum and minimum values of the queue interval are searched, the judgment interval is not a uniform-speed interval, the maximum value of the current interval is greater than or equal to the last local maximum value, and the minimum value of the current interval is less than or equal to the last local minimum value, so that the local maximum pointer and the local minimum pointer point to the maximum value and the minimum value of the current queue interval;
fourthly, as shown in fig. 9 and 10, searching the local maximum and minimum values of the queue interval, judging that the interval is not a uniform-speed interval, and the maximum value of the current interval is not more than or equal to the last local maximum value, so that a newly-built maximum pointer points to the last maximum value, a newly-built second-type speed planning interval node pointer points to the previous point of the maximum value, clearing the local maximum pointer, a local minimum pointer points to the minimum value of the current queue interval, and the next extremum to be searched is changed into the minimum value;
fifthly, as shown in fig. 11, the current extreme point to be searched is a minimum value, the local maximum minimum value of the queue interval is searched, the judgment interval is not a uniform-speed interval, the minimum value of the current interval is less than or equal to the last local minimum value, and therefore the local minimum pointer points to the current minimum value of the queue;
sixthly, as shown in fig. 12 and 13, the current extreme point to be searched is a minimum value, the local maximum and minimum values of the queue interval are searched, the interval is judged not to be a uniform-speed interval, the minimum value of the current interval is not less than or equal to the last local minimum value, so that a newly-built minimum value pointer points to the last local minimum value, a local minimum value pointer is emptied, the local maximum value pointer points to the maximum value of the current queue interval, and the next extreme value to be searched is changed into a maximum value;
seventhly, as shown in fig. 14 and 15, the current extreme point to be searched is a maximum value, the local maximum value and the minimum value of the queue interval are searched, and the interval is judged to be a constant-speed interval, but not last time, so that a newly-built maximum pointer points to the local maximum value of the current time, a newly-built second-type speed planning interval node pointer points to the next point of the latest extreme point, and the next extreme value to be searched is changed to be uncertain; and finishing one interval search.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A winding machine process optimization method based on double-code joint control is characterized by comprising the following steps:
s1, acquiring a first processing code and a winding machine structure parameter, and further estimating the speed;
s2, identifying a speed fluctuation interval in the estimated speed, smoothing the speed fluctuation interval to obtain a processing feed speed, and obtaining a second processing code according to the processing feed speed;
specifically, identifying a speed fluctuation interval in the estimated speed includes the steps of:
sliding a search interval window at a preset moving interval, searching the whole interval of the estimated speed to obtain multiple groups of corresponding maximum values and minimum values in the estimated speed interval, and further obtaining a speed fluctuation interval between constant speed intervals in the estimated speed;
searching the estimated speed interval specifically comprises the following steps:
(1) sliding a search interval window, if the current interval is a constant-speed interval, not recording, and moving the search interval window to the right to continue searching; if the current interval is not a constant speed interval, namely a speed change point is encountered for the first time, the local maximum pointer and the local minimum pointer respectively point to the first maximum and minimum of the current search interval window;
(2) continuously sliding the search interval window, and searching a maximum value and a minimum value according to the following method;
when searching for the maximum value, comparing the speed maximum values in the current interval and the previous interval, if the speed maximum value in the current interval is not less than the speed maximum value in the previous interval, pointing the local maximum pointer to the maximum value in the current interval, and continuing to search for the maximum value; if the maximum speed value in the current interval is smaller than the maximum speed value in the previous interval, the maximum value in the previous interval is the maximum value, the local maximum pointer is cleared, and then the minimum value corresponding to the maximum value is searched;
when searching the minimum value, comparing the minimum values of the speeds in the current and previous intervals, if the minimum value of the speed in the current interval is not greater than the minimum value of the speed in the previous interval, pointing the local maximum pointer to the minimum value in the current interval, and continuing to search the minimum value; if the minimum speed value in the current interval is greater than the minimum speed value in the previous interval, taking the minimum value in the previous interval as a minimum value, clearing the local minimum value pointer, and then searching for a maximum value corresponding to the minimum value;
(3) repeating the step (2) until the current interval is the constant-speed interval again, and finishing one-time interval searching;
(4) repeating the steps (1) to (3) until the whole estimated speed interval is searched to obtain multiple groups of corresponding maximum values and minimum values, wherein a speed fluctuation interval is formed between each group of maximum values and minimum values;
the speed fluctuation interval is subjected to smoothing treatment, and the method comprises the following steps:
carrying out smoothing processing on a speed fluctuation interval in the estimated speed, comparing the speed after the smoothing processing with the estimated speed, if a certain position of the speed after the smoothing processing is larger than the estimated speed, further searching a minimum non-overspeed planning interval around the speed fluctuation interval, and carrying out smoothing processing on the planning interval to finish the smoothing processing; if the speed after the smoothing is not greater than the estimated speed, directly finishing the smoothing;
and S3, controlling the winding machine to process the part through the first processing code and the second processing code together, and finishing the process optimization of the winding machine.
2. The method for optimizing a winding machine process based on dual code simultaneous control as claimed in claim 1, wherein the speed estimation is performed by calculating a geometric constraint estimated speed and a physical constraint estimated speed, and then using the minimum value of the geometric constraint estimated speed, the physical constraint estimated speed and the feeding speed in the first processing code as the estimated speed.
3. The method for optimizing the process of the winding machine based on the joint control of the two codes as claimed in claim 2, wherein the calculation of the geometric constraint estimation speed comprises the following steps:
unifying the stroke units of the driving shaft and each following shaft, then performing arc fitting on the driving shaft and each following shaft in a two-dimensional space respectively to obtain the synthesis speed of the driving shaft and each following shaft, calculating the component of the synthesis speed on the driving shaft, and taking the minimum value of the component of the synthesis speed on the driving shaft as the geometric constraint estimation speed.
4. The method for optimizing the process of the winding machine based on the joint control of the two codes as claimed in claim 2, wherein the calculation of the physical constraint estimation speed comprises the following steps:
(1) and (3) speed verification: calculating the synthetic feeding limit speed corresponding to each following shaft according to the feeding limit speed of each following shaft, and taking the component of the lowest synthetic feeding limit speed on the driving shaft as the initial driving shaft speed constraint limit;
(2) and (3) acceleration verification: inversely calculating the movement speed of each following shaft according to the initial driving shaft speed constraint limit, and calculating the movement speed increment of each following shaft according to the first processing code so as to obtain the acceleration of each following shaft; and combining the motion speed, the acceleration and the acceleration limit of each following shaft to obtain the feed speed of each following shaft, further obtaining a plurality of driving shaft speed constraints, and taking the minimum value in the driving shaft speed constraints as the physical constraint estimated speed.
5. The method for optimizing a winding machine process based on joint control of two codes according to claim 1, wherein a minimum planning interval which does not overspeed around a speed fluctuation interval is searched by a binary search method.
6. The winding machine process optimization method based on double code joint control as claimed in claim 1 or 5, wherein an S-shaped acceleration and deceleration curve plan is adopted to smooth the speed fluctuation interval.
7. A winding machine, characterized by comprising a processor and a dual code joint control process optimization module, wherein the dual code joint control process optimization module executes the dual code joint control-based winding machine process optimization method according to any one of claims 1 to 6 when being called by the processor.
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