CN116203857B - Cross-process precision monitoring system and cross-process precision monitoring method - Google Patents
Cross-process precision monitoring system and cross-process precision monitoring methodInfo
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- CN116203857B CN116203857B CN202111449831.3A CN202111449831A CN116203857B CN 116203857 B CN116203857 B CN 116203857B CN 202111449831 A CN202111449831 A CN 202111449831A CN 116203857 B CN116203857 B CN 116203857B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24024—Safety, surveillance
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Electrical Discharge Machining, Electrochemical Machining, And Combined Machining (AREA)
Abstract
The invention provides a cross-process precision monitoring system and a cross-process precision monitoring method. The process accuracy monitoring system comprises electrochemical machining equipment, electric discharge machining equipment, a storage unit and a processing unit. The electrochemical machining equipment is used for carrying out an electrochemical machining process on a workpiece. The electric discharge machining equipment is used for continuously carrying out an electric discharge machining process on the workpiece. The processing unit is used for executing a linear regression model to estimate a first removal area of the workpiece. The processing unit is used for executing an electric discharge machining precision prediction model to estimate a second removal area of the workpiece. The processing unit adjusts at least one of the discharge voltage and the discharge current according to the first removed area and the second removed area.
Description
Technical Field
The present invention relates to a monitoring system, and more particularly, to a cross-process accuracy monitoring system and a cross-process accuracy monitoring method.
Background
The existing electrochemical machining process and the existing electric discharge machining process are used for measuring in an off-line mode, so that the problem of poor process efficiency is generally caused. Furthermore, the workpiece also has the problem that the process accuracy cannot be mastered in real time in the process of manufacturing the workpiece through the electrochemical machining process and the electric discharge machining process, and the problem that the process error is larger in the process of manufacturing the workpiece through the electric discharge machining process is easy to cause.
Disclosure of Invention
The invention aims at a process precision monitoring system and a process precision monitoring method, which can instantly estimate the processing quality parameters of a workpiece in the electrochemical processing process of the electrochemical processing equipment, and can also instantly estimate the processing quality parameters of the workpiece in the electric discharge processing process of the electric discharge processing equipment.
According to an embodiment of the invention, the process accuracy monitoring system comprises electrochemical processing equipment, a storage unit and a processing unit. The electrochemical machining device is used for carrying out an electrochemical machining process on the workpiece. The storage unit is used for storing the linear regression model. The processing unit is coupled with the electrochemical processing equipment and the storage unit. The processing unit is used for detecting the working voltage and the working current of the electrochemical machining equipment in the electrochemical machining process and executing a linear regression model. The processing unit inputs the working voltage and the working current into the linear regression model so that the linear regression model estimates the processing quality parameters of the workpiece.
According to the embodiment of the invention, the process accuracy monitoring method comprises the steps of carrying out electrochemical machining on a workpiece through electrochemical machining equipment, detecting working voltage and working current of the electrochemical machining equipment in the electrochemical machining process through a processing unit, executing a linear regression model through the processing unit, and inputting the working voltage and the working current into the linear regression model through the processing unit so that the linear regression model can estimate machining quality parameters of the workpiece.
According to an embodiment of the invention, the process accuracy monitoring system comprises electrochemical machining equipment, electric discharge machining equipment, a storage unit and a processing unit. The electrochemical machining equipment is used for carrying out an electrochemical machining process on a workpiece. The electric discharge machining equipment is used for continuously carrying out an electric discharge machining process on the workpiece. The storage unit is used for storing the linear regression model and the electric discharge machining precision prediction model. The processing unit is coupled to the electrochemical machining device, the electrical discharge machining device and the storage unit. The processing unit is used for detecting the working voltage and the working current of the electrochemical machining equipment in the electrochemical machining process, and executing a linear regression model to estimate the first removal area of the workpiece. The processing unit is used for detecting the discharge voltage and the discharge current of the electric discharge machining equipment in the electric discharge machining process and executing an electric discharge machining precision prediction model to estimate the second removal area of the machined part. The processing unit adjusts at least one of the discharge voltage and the discharge current according to the first removed area and the second removed area.
According to an embodiment of the invention, the process accuracy monitoring method comprises the steps of firstly performing an electrochemical machining process on a workpiece through electrochemical machining equipment, detecting working voltage and working current of the electrochemical machining equipment in the electrochemical machining process through a processing unit, and executing a linear regression model to estimate a first removal area of the workpiece, sequentially performing an electric discharge machining process on the workpiece through electric discharge machining equipment, detecting electric discharge voltage and electric discharge current of the electric discharge machining equipment in the electric discharge machining process through the processing unit, and executing an electric discharge machining accuracy prediction model to estimate a second removal area of the workpiece, and adjusting at least one of the electric discharge voltage and the electric discharge current according to the first removal area and the second removal area through the processing unit.
Based on the above, the process accuracy monitoring system and the process accuracy monitoring method of the present invention can monitor the working voltage and the working current of the electrochemical machining device in the electrochemical machining process in real time, so as to estimate the machining quality parameters of the workpiece in real time, and dynamically adjust the feed amount setting of the electrochemical machining device in the electrochemical machining process of the workpiece. In addition, the process accuracy monitoring system and the process accuracy monitoring method can monitor the discharge voltage and the discharge current of the electric discharge machining equipment in the electric discharge machining process in real time so as to estimate the electric discharge machining result of the workpiece in real time, and can dynamically adjust the process setting of the electric discharge machining equipment.
In order to make the above features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a schematic circuit diagram of an electrochemical machining process accuracy monitoring system according to an embodiment of the present invention;
FIG. 2A is a schematic view of an electrochemical processing apparatus according to an embodiment of the present invention;
FIG. 2B is a schematic illustration of a workpiece according to an embodiment of the invention;
FIG. 3 is a flow chart of establishing a linear regression model according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method for monitoring accuracy of an electrochemical machining process according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of process quality parameters according to an embodiment of the present invention;
FIG. 6 is a schematic circuit diagram of a cross-process accuracy monitoring system according to an embodiment of the present invention;
fig. 7 is a schematic view of an electric discharge machine according to an embodiment of the present invention;
FIG. 8 is a flow chart of establishing an electrical discharge machining accuracy prediction model in accordance with an embodiment of the present invention;
FIG. 9 is a flow chart of a cross-process accuracy monitoring method according to an embodiment of the present invention;
FIG. 10 is a flow chart of a cross-process accuracy monitoring method according to another embodiment of the present invention.
Description of the reference numerals
100. 600, A process precision monitoring system;
110. 610 a processing unit;
120. 620 a storage unit;
121. 621, linear regression model;
130. 630 electrochemical machining equipment;
131, cathode;
132, anode;
133, electrode cutters;
134 an insulating layer;
140. 650, machining a workpiece;
141. 651 workpiece material removal area;
141-1 to 141-6, removing the layer;
142, electrolyte;
143, electrolyte flow direction;
501. 502, 503, curves;
622, an electric discharge machining precision prediction model;
640, an electric discharge machining device;
641, a main shaft;
642 machining the electrode;
643, a platform;
D1, D2, D3, D4;
S310-S330, S410-S440, S810-S830, S910-S950, S1010-S1080.
Detailed Description
Reference will now be made in detail to the exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings and the description to refer to the same or like parts.
FIG. 1 is a schematic circuit diagram of an electrochemical machining process accuracy monitoring system according to an embodiment of the invention. Referring to fig. 1, a process accuracy monitoring system 100 includes a processing unit 110, a storage unit 120, and an electrochemical machining (Electro-CHEMICAL MACHINING, ECM) device 130. The processing unit 110 is coupled to the storage unit 120 and the electrochemical processing apparatus 130. The storage unit 120 is configured to store a linear regression (linear regression) model 121. In the present embodiment, the electrochemical machining apparatus 130 may be used for performing an electrochemical machining process on a workpiece, and the processing unit 110 may obtain the working voltage and the working current of the electrochemical machining apparatus 130 in the electrochemical machining process in real time. The processing unit 110 may execute the linear regression model 121 to effectively estimate a current process quality parameter according to the current operating voltage and the current operating current, and may dynamically adjust the current process quality parameter according to the current process quality parameter to effectively monitor and maintain the process accuracy of the electrochemical processing performed on the workpiece.
In the present embodiment, the processing unit 110 may include a processing circuit such as a central processing unit (Central Processing Unit, CPU), a microprocessor (Microprocessor Control Unit, MCU) or a field programmable gate array (Field Programmable GATE ARRAY, FPGA) or a control chip with data operation function, but the invention is not limited thereto. In this embodiment, the storage unit 120 may be a Memory (Memory), such as a non-volatile Memory including a Read Only Memory (ROM), an erasable programmable ROM (Erasable Programmable Read Only Memory, EPROM), a volatile Memory including a random access Memory (Random Access Memory, RAM), a hard disk drive (HARD DISC DRIVE), a semiconductor Memory, and the like. The memory unit 120 may be used to store algorithms of the linear regression model 121 and the parameters, analysis software, control instructions and related algorithms and programs mentioned in the various embodiments of the present invention, and may be read and executed by the processing unit 110.
Fig. 2A is a schematic view of an electrochemical processing apparatus according to an embodiment of the present invention. Fig. 2B is a schematic view of a workpiece according to an embodiment of the invention. Referring to fig. 1 to 2B, the electrochemical machining apparatus 130 may include a cathode 131, an anode 132, an electrode cutter 133, and an insulating layer 134 formed on an outer layer of the electrode cutter 133. In this embodiment, the electrochemical machining apparatus 130 may couple, mount, or provide the cathode 131 to the electrode cutter 133 and the anode 132 to the workpiece 140. The electrochemical machining apparatus 130 can apply a working voltage and a working current to the cathode 131 and the anode 132, so that the electrode cutter 133 can perform an electrochemical machining process on the workpiece 140. As electrolyte 142 adjacent to electrode tool 133 reacts with work piece 140, work piece 140 may form a work piece material removal zone 141, and the removed material in work piece material removal zone 141 may be removed as electrolyte 142 along electrolyte flow direction 143.
As shown in fig. 2B, the electrode tool 133 of the electrochemical machining apparatus 130 may extend into the workpiece 140, for example, in the direction D4, at the workpiece material removal area 141 to remove material from the workpiece 140 in the workpiece material removal area 141. The workpiece 140 may be placed horizontally so as to be parallel to a plane formed by extending along the direction D1 and the direction D2, wherein the direction D1 and the direction D2 may be horizontal directions, respectively, and the direction D3 may be vertical directions (the direction D4 is opposite to the direction D3). It should be noted that the processing quality parameters according to the embodiments of the present invention may include a plurality of removal areas of the plurality of removal layers 141-1 to 141-6 of the workpiece 140 as shown in fig. 2B, and the number of removal layers is not limited to that shown in fig. 2B.
FIG. 3 is a flow chart of establishing a linear regression model according to an embodiment of the present invention. Referring to fig. 1to 3, the process accuracy monitoring system 100 may perform the following steps S310 to S330 to pre-establish the linear regression model 121. In step S310, the process accuracy monitoring system 100 may perform an electrochemical machining process on the same reference workpieces according to the original operating voltages and the original operating currents in advance by the electrochemical machining apparatus 130. In this regard, the plurality of original operating voltages are the same voltage value, and the plurality of original operating currents are different current values, but the present invention is not limited thereto. As shown in table 1 below, the electrochemical machining apparatus 130 may perform process tests 1to 5 (refer to workpieces 1to 5) according to different Feed rates (Feed rates). In contrast, in the process tests 1-5, the electrochemical machining apparatus 130 can correspondingly adjust the feeding amount by changing the working current (fixed working voltage).
TABLE 1
In step S320, the processing unit 110 may obtain a plurality of original processing parameters of a plurality of reference workpieces. In this embodiment, as shown in table 2 below, the plurality of original processing parameters may correspond to a plurality of removal areas of a plurality of removal layers (such as the plurality of removal layers 141-1-141-6 shown in fig. 2B) of a plurality of reference workpieces, respectively.
TABLE 2
In step S330, the processing unit 110 may establish the linear regression model 121 according to the plurality of raw operating voltages, the plurality of raw operating currents, and the plurality of raw process quality parameters. In this embodiment, the processing unit 110 may perform at least one of Heat map analysis and scatter map matrix analysis (Pair plot) according to the plurality of raw operating voltages, the plurality of raw operating currents and the plurality of raw process quality parameters (removal areas) as shown in table 1 above to evaluate the analysis characteristics of the plurality of raw operating voltages, the plurality of raw operating currents and the plurality of raw process quality parameters. When the analysis characteristic is a linear analysis characteristic, the processing unit 110 may choose to build a linear regression model 121. In other embodiments, if the analysis characteristic is a type of analysis characteristic, the processing unit 110 may choose to build a model of its corresponding type, and is not limited to the linear regression model 121. In the present embodiment, the processing unit 110 may establish a linear regression model 121 according to the following formula (1), and is adapted to find the relation between the reaction variable (Y) and the interpretation variable (X 1,X2,......,Xn). In the following formula (1), the aforementioned plurality of original operating voltages and the plurality of original operating currents are represented by a parameter X 1,X2,......,Xn, and the estimated removal area output by the linear regression model 121 may be represented by a parameter Y. The parameter i is the total number of samples (n is a positive integer), the parameter p is the number of features, and β 0,...,βp is the number of parameters to be estimated.
Y i＝β0+β1Xi1+…+βpXip + epsilon, i=1, 2, n. the formula (1)
FIG. 4 is a flow chart of a method for monitoring accuracy of an electrochemical machining process according to an embodiment of the invention. FIG. 5 is a schematic diagram of process quality parameters according to an embodiment of the invention. Referring to fig. 1, 2A, 2B, 4 and 5, the process accuracy monitoring system 100 may perform the following steps S410 to S440 to monitor the process accuracy. In step S410, the process accuracy monitoring system 100 may perform an electrochemical machining process on the workpiece 140 by the electrochemical machining apparatus 130. In step S420, the processing unit 110 of the process accuracy monitoring system 100 may detect the operating voltage and the operating current of the electrochemical machining apparatus 130 during the electrochemical machining process. In step S430, the processing unit 110 of the process accuracy monitoring system 100 may execute the linear regression model 121. In step S440, the processing unit 110 of the process accuracy monitoring system 100 may input the operating voltage and the operating current to the linear regression model 121, so that the linear regression model 121 estimates the processing quality parameters of the workpiece 140.
In this embodiment, the linear regression model 121 may change with time (e.g., time t0 to time t 6), and output a curve 501 as shown in fig. 5, wherein the unit of the horizontal axis corresponding to the curve 501 may be time (seconds) and the unit of the vertical axis may be square millimeter (mm 2). Curve 501 is a graph showing the results of the linear regression model 121 estimating the electrochemical machining process performed by the electrochemical machining apparatus 130 on the workpiece 140, and the resulting removal area in the workpiece material removal area 141 over time (e.g., time t0 to time t 6). Alternatively, the linear regression model 121 may estimate a plurality of removed areas corresponding to different removed layers (e.g., the removed layers 141-1-141-6 shown in fig. 2B) and output a curve 502 shown in fig. 5, wherein the horizontal axis of the curves 502 and 530 may be in layers and the vertical axis may be in square millimeters (mm 2). Curve 502 shows the results of the electrochemical machining process performed on the workpiece 140 by the electrochemical machining apparatus 130 estimated by the linear regression model 121, and the removal areas (e.g., layer 1 to layer 6) corresponding to the removal layers 141-1 to 141-6 in the workpiece material removal area 141, respectively. It is noted that the curve 503 is a result of the electrochemical machining apparatus 130 performing an electrochemical machining process on the workpiece 140, and the actual (experimental) removal areas (e.g., layer 1 to layer 6) corresponding to the plurality of removal layers 141-1 to 141-6 in the workpiece material removal area 141, respectively. In contrast, the average absolute error (Mean Absolute Error, MAE) between the estimated removal areas and the actual (experimental) removal areas of the removal layers 141-1-141-6, respectively, is less than three percent (3%). Therefore, the process accuracy monitoring system 100 can provide the processing quality parameters of the workpiece 140 with high accuracy and real time, and can effectively monitor the process accuracy of the electrochemical processing process. The processing unit 110 may dynamically adjust the feed rate setting during the electrochemical machining process of the workpiece 140 according to the machining quality parameter at the current time during the electrochemical machining process of the workpiece 140. Or the processing unit 110 may further operate the electric discharge machining (ELECTRICAL DISCHARGE MACHINING, EDM) apparatus according to the machining quality parameters to sequentially perform the electric discharge machining process on the workpiece. In one embodiment, the electrochemical machining apparatus 130 may perform, for example, a rapid reaming process on the workpiece 140, while the electrical discharge machining apparatus may perform further fine machining on the workpiece 140.
FIG. 6 is a schematic circuit diagram of a cross-process accuracy monitoring system according to an embodiment of the present invention. The process accuracy monitoring system 600 includes a processing unit 610, a storage unit 620, an electrochemical machining apparatus 630, and an electrical discharge machining apparatus 640. The processing unit 610 is coupled to the storage unit 620 and the electrochemical processing apparatus 630. The storage unit 620 is configured to store a linear regression (linear regression) model 621 and an electric discharge machining accuracy prediction model 622. In this embodiment, the electrochemical machining apparatus 630 may be used to perform an electrochemical machining process on a workpiece, and the processing unit 610 may obtain the working voltage and the working current of the electrochemical machining apparatus 630 in the electrochemical machining process in real time. The processing unit 610 may execute the linear regression model 621 to effectively estimate the first removal area according to the operating voltage and the operating current. Then, the electric discharge machining apparatus 640 may be used to perform an electric discharge machining process on the workpiece, and the processing unit 610 may obtain the electric discharge voltage and the electric discharge current of the electrochemical machining apparatus 630 in the electrochemical machining process in real time. The processing unit 610 may execute the electric discharge machining precision prediction model 622 to effectively estimate the second removal area according to the electric discharge voltage and the electric discharge current. It should be noted that the first removing area refers to an area of a removing layer of the workpiece after the electrochemical machining process, and the second removing area may be based on an increased removing area of the same removing layer, but the invention is not limited thereto. Therefore, the process accuracy monitoring system 600 of the present invention can effectively grasp the process accuracy across processes, and can dynamically adjust the process time, the number of times, etc. of the electric discharge machining process of the electric discharge machining apparatus 640.
It should be noted that, the specific embodiments and technical features of the processing unit 610, the storage unit 620 and the electrochemical processing apparatus 630 of the present embodiment may refer to the descriptions of the embodiments of fig. 1 to 5, so that sufficient teachings, suggestions and implementation descriptions may be obtained, and thus the descriptions are not repeated herein. Moreover, the process accuracy monitoring system 600 may perform steps S310-S330 described above with respect to the embodiment of FIG. 3 to pre-establish the linear regression model 621. At least a portion of the process accuracy monitoring system 600 of this embodiment may be implemented as described in the related art of the process accuracy monitoring system 100.
Fig. 7 is a schematic view of an electric discharge machine according to an embodiment of the present invention. Referring to fig. 6 and 7, the electric discharge machining apparatus 640 may include a main shaft 641 and a machining electrode 642 provided to the main shaft 641 to perform electric discharge machining on a workpiece 650' placed on a stage 643 by the machining electrode 642. The workpiece 650 may be, for example, the workpiece 140 after electrochemical machining shown in fig. 2A and 2B shown in fig. 2B. In the present embodiment, the processing unit 610 may include a sensing unit, and the sensing unit may sense a discharge voltage and a discharge current at the time of the in-line processing of the electric discharge machining apparatus 640. The processing unit 610 may analyze the discharge voltage and the discharge current to extract a plurality of reference characteristic parameters, and the processing unit 610 may input the plurality of reference characteristic parameters to the electric discharge machining precision prediction model 622, so that the electric discharge machining precision prediction model 622 may estimate a removal area generated (increased) after the electric discharge machining of the workpiece material removal area 651 of the workpiece 650.
Fig. 8 is a flowchart of establishing an electric discharge machining precision prediction model according to an embodiment of the present invention. Referring to fig. 6 and 8, the process accuracy monitoring system 600 may perform the following steps S810 to S830 to pre-establish the electric discharge machining accuracy prediction model 622. In step S810, the process accuracy monitoring system 600 may perform an electric discharge machining process on a plurality of reference workpieces in advance by the electric discharge machining apparatus 640, and detect a plurality of reference electric discharge voltages and a plurality of reference electric discharge currents of the electric discharge machining apparatus 640 in the electric discharge machining process on the plurality of reference workpieces in advance by the processing unit 610. Each of the plurality of reference workpieces may be like workpiece 650 of fig. 7. In step S820, the processing unit 610 may analyze the plurality of reference discharge voltages and the plurality of reference discharge currents to extract a plurality of sets of reference characteristic parameters. In step S830, the processing unit 610 may select at least a portion of each of the plurality of sets of reference characteristic parameters for establishing the electric discharge machining precision prediction model 622 according to the workpiece type of the plurality of sets of reference workpieces.
In this embodiment, each of the plurality of sets of reference characteristic parameters may include a discharge frequency (spark frequency), an open ratio, a short ratio (Short circuit ratio), an average short time, a short time standard deviation, an average short current, a short current standard deviation, an average delay time, a delay time standard deviation, an average discharge peak current (AVERAGE PEAK DISCHARGE current), a peak current standard deviation, an average discharge time, a discharge time standard deviation, an average discharge energy, and a discharge energy standard deviation.
In the above-mentioned reference characteristic parameters, the average delay time to short-circuit ratio is established from the discharge voltage signal, wherein the average delay time is defined as a time difference from a point of time when a sufficient open-circuit voltage has been established until the voltage pulse passes through the gap between the electrode and the workpiece, and a discharge current starts. The short ratio is defined as the number of short pulses (short circuit pulse, SCP) divided by the number of discharge pulses, wherein the short pulses are one short pulse recorded during a discharge pulse period when the open circuit voltage is continuously less than a specified voltage threshold.
In the above-mentioned reference characteristic parameters, the discharge frequency, the average discharge peak current and the average discharge time are established from the discharge current signal, wherein the discharge frequency is defined as the occurrence of current sparks if the current peak value exceeds the minimum threshold peak value within a pulse time, and the discharge frequency is defined as the total number of sparks occurring within the sampling period. The average discharge peak current is defined as the average of all discharge peak currents (peak currents) during the sampling period, where peak current is the large current value through the electrode to the workpiece during the pulse period.
In the above processing feature, the average short-circuit time, the open ratio, the average discharge energy and the average short-circuit current are commonly established according to the discharge current signal and the discharge voltage signal, wherein the average short-circuit time is related to the short-circuit duration, and the short-circuit duration (Short circuit duration) is defined as the time difference from the first short-circuit peak (short circuit peak) to the last short-circuit pulse peak in a plurality of continuous short-circuit periods when a plurality of continuous short-circuits occur in a period of one discharge pulse (more than two continuous pulses are needed). The Open ratio is defined as the number of Open circuits divided by the total number of discharge pulses during a sampling period, wherein, during a certain pulse time, when the voltage peak ends and there is no rise followed by the current peak, i.e. it is called Open circuit (Open circuit). If an open circuit occurs, it indicates that a Voltage peak (Ignition Voltage) cannot induce a subsequent current peak (DISCHARGE CURRENT), which is an inactive pulse. The average discharge energy is mainly used to maintain the stability of the electric discharge machining process to ensure the machining quality, and the discharge energy of the ith discharge is represented by the following formula (1), wherein tei is the discharge duration, ui is the discharge voltage, ipi is the discharge peak current, and the formula assumes that the discharge voltage remains unchanged during the discharge process.
In addition, according to the foregoing disclosure, the calculation methods of the standard deviation of the short-circuit time, the standard deviation of the short-circuit current, the standard deviation of the delay time, the standard deviation of the peak current, the standard deviation of the discharge time and the standard deviation of the discharge energy, and other parameters are well known to those skilled in the art to which the present invention pertains, and therefore will not be described in detail herein.
In this embodiment, the processing unit 610 may train a neural network (Neural Network, NN) model (or other type of known machine learning model) by at least a portion of the above-mentioned multiple sets of reference feature parameters, and may also build an electric discharge machining precision prediction model 622 in combination with a regression analysis (Regression Analysis) method (e.g., a partial least Squares method (PARTIAL LEAST square, PLS): to this effect, the processing unit 610 may select at least a portion of each of the above-mentioned multiple sets of reference feature parameters for use in building the electric discharge machining precision prediction model 622 according to the workpiece types of the reference workpieces, in other words, the processing unit 610 may select certain specific reference feature parameters according to the workpiece types of the reference workpieces to build corresponding prediction models to provide an efficient and accurate process precision prediction function of the electric discharge machining process.
FIG. 9 is a flow chart of a cross-process accuracy monitoring method according to an embodiment of the invention. Referring to fig. 6 and 9, the process accuracy monitoring system 600 may perform the following steps S910 to S950 to perform process accuracy monitoring. In step S910, the process accuracy monitoring system 600 may first perform an electrochemical machining process on a workpiece (e.g., the workpiece 140, 650 of fig. 2B or 7) by the electrochemical machining apparatus 630. In step S920, the processing unit 610 may detect the operating voltage and the operating current of the electrochemical machining apparatus 630 in the electrochemical machining process, and execute the linear regression model 621 to estimate the first removal area of the workpiece. It should be noted that, the manner of estimating the first removal area of the workpiece may refer to the description of the embodiment of fig. 4, and thus a detailed description is omitted. In step S930, the process accuracy monitoring system 600 may perform the electric discharge machining process on the workpiece process accuracy monitoring system 600 by the electric discharge machining apparatus 640. In step S940, the processing unit 610 may detect the electric discharge voltage and the electric discharge current of the electric discharge machining apparatus 640 in the electric discharge machining process, and execute the electric discharge machining precision prediction model 622 to estimate the second removal area of the workpiece. In step S950, the processing unit 610 may adjust at least one of the discharge voltage and the discharge current according to the first removed area and the second removed area. Therefore, the process accuracy monitoring system 600 of the present embodiment can effectively estimate the overall removal area of the workpiece generated after the electrochemical machining process and the electric discharge machining process, and dynamically adjust the electric discharge voltage and the electric discharge current in the electric discharge machining process according to the predicted removal area, so that the workpiece as the final machined product can meet the expected specification.
FIG. 10 is a flow chart of a cross-process accuracy monitoring method according to another embodiment of the present invention. Referring to fig. 6 and 10, the process accuracy monitoring system 600 may perform the following steps S1010-S1080 to monitor the process accuracy.
In step S1010, the processing unit 610 may obtain the operating voltage and the operating current of the electrochemical machining device 630. In step S1020, the processing unit 610 may estimate a first removal area formed on a workpiece (e.g., the workpiece 140, 650 of fig. 2B or 7) by executing the linear regression model 621. In step S1030, the processing unit 610 may adjust a process setting of the electric discharge machine 640, such as a feed rate setting of the electric discharge machine 640. In other words, the process accuracy monitoring system 600 can dynamically adjust the process parameters of the electric discharge machining process according to the result of the electrochemical machining process of the workpiece, so as to effectively compensate the process errors of the electrochemical machining process. In step S1040, the processing unit 610 may obtain the discharge voltage and the discharge current of the electric discharge machining apparatus 640. In step S1050, the processing unit 610 may estimate a second removal area formed on the workpiece. In other words, the process accuracy monitoring system 600 can effectively monitor the process accuracy of the electrical discharge machining process. In step S1060, the processing unit 610 may calculate a post-processing volume of the workpiece. In this regard, the processing unit 610 may calculate the processed volume of the workpiece according to the first removed area, the second removed area, and the predetermined unit thickness (the thickness of the predetermined or known removed layer), for example. In step S1070, the processing unit 610 may determine whether the processed volume is within a preset volume threshold. If not, the processing unit 610 re-executes step S1030 to re-adjust the process setting of the electric discharge machining apparatus 640 and re-perform the electric discharge machining process on the workpiece. If so, in step S1080, the processing unit 610 ends the estimation and may output information such as the estimated process specification or accuracy of the workpiece of the final processed product.
In summary, the process accuracy monitoring system and the process accuracy monitoring method of the present invention can provide effective cross-process accuracy monitoring for the electrochemical machining process performed by the electrochemical machining apparatus and the electric discharge machining process performed by the electric discharge machining apparatus, respectively. In addition, the processing precision monitoring system and the processing precision monitoring method can set the feed rate of the electrochemical processing equipment for controlling the processing quality parameter feedback of the electrochemical processing process so as to effectively maintain the processing precision. In addition, the process accuracy monitoring system and the process accuracy monitoring method can dynamically adjust the process setting of the electric discharge machining equipment according to the result of the electrochemical machining process so as to effectively integrate the process effect of the cross-process and compensate the process error of the electrochemical machining process through the electric discharge machining process.
It should be noted that the above embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present invention.
Claims (10)
1. A process accuracy monitoring system, comprising:
Electrochemical machining equipment is used for carrying out an electrochemical machining process on a workpiece;
the electric discharge machining equipment is used for continuously carrying out an electric discharge machining process on the machined part;
A storage unit for storing the linear regression model and the electric discharge machining precision prediction model, and
A processing unit coupled to the electrochemical machining apparatus, the electrical discharge machining apparatus, and the storage unit,
Wherein the processing unit is configured to detect an operating voltage and an operating current of the electrochemical machining apparatus during the electrochemical machining process, and execute the linear regression model to estimate a first removal area of the workpiece,
Wherein the processing unit is configured to detect a discharge voltage and a discharge current of the electric discharge machining apparatus in the electric discharge machining process, and execute the electric discharge machining precision prediction model to estimate a second removal area of the workpiece,
The processing unit adjusts at least one of the discharge voltage and the discharge current according to the first removal area and the second removal area.
2. The process accuracy monitoring system of claim 1, wherein the processing unit calculates a post-processing volume of the workpiece from the first removed area and the second removed area, and evaluates whether to operate the electric discharge machining apparatus to perform the electric discharge machining process again on the workpiece based on the post-processing volume.
3. The system of claim 1, wherein the electrochemical machining device performs the electrochemical machining process on the same first reference workpieces according to the original operating voltages and the original operating currents, respectively, so that the processing unit obtains the original machining quality parameters of the first reference workpieces,
The processing unit establishes the linear regression model according to the plurality of original working voltages, the plurality of original working currents and the plurality of original processing quality parameters.
4. The process accuracy monitoring system according to claim 1, wherein the electric discharge machining apparatus performs the electric discharge machining process on a plurality of second reference workpieces in advance, and the processing unit detects in advance a plurality of reference discharge voltages and a plurality of reference discharge currents in the electric discharge machining process performed by the electric discharge machining apparatus on the plurality of second reference workpieces,
Wherein the processing unit analyzes the plurality of reference discharge voltages and the plurality of reference discharge currents to extract a plurality of sets of reference feature parameters, and the processing unit selects at least a portion of each of the plurality of sets of reference feature parameters for use in establishing the electrical discharge machining precision prediction model in accordance with a workpiece type of the plurality of second reference workpieces.
5. The process accuracy monitoring system of claim 4, wherein each of the plurality of sets of reference characteristic parameters comprises a discharge frequency, an open ratio, a short ratio, an average short time, a short time standard deviation, an average short current, a short current standard deviation, an average delay time, a delay time standard deviation, an average discharge peak current, a peak current standard deviation, an average discharge time, a discharge time standard deviation, an average discharge energy, and a discharge energy standard deviation.
6. A process accuracy monitoring method is characterized by comprising the following steps:
the electrochemical machining process is carried out on the machined part through electrochemical machining equipment;
Detecting, by a processing unit, an operating voltage and an operating current of the electrochemical machining device during the electrochemical machining process, and performing a linear regression model to estimate a first removal area of the workpiece;
continuously performing an electric discharge machining process on the workpiece through electric discharge machining equipment;
Detecting, by the processing unit, a discharge voltage and a discharge current of the electric discharge machining apparatus in the electric discharge machining process, and executing an electric discharge machining accuracy prediction model to estimate a second removal area of the workpiece, and
And adjusting at least one of the discharge voltage and the discharge current according to the first removal area and the second removal area by the processing unit.
7. According to claim 6a method for monitoring the precision of a manufacturing process, characterized by further comprising:
Calculating, by the processing unit, a post-processing volume of the workpiece from the first removed area and the second removed area, and
And evaluating whether to operate the electric discharge machining equipment to carry out the electric discharge machining process on the workpiece according to the machined volume by the processing unit.
8. According to claim 6a method for monitoring the precision of a manufacturing process, characterized by further comprising:
Respectively performing the electrochemical machining process on the same first reference workpieces according to a plurality of original working voltages and a plurality of original working currents in advance by the electrochemical machining equipment so that the processing unit obtains a plurality of original machining quality parameters of the first reference workpieces, and
And establishing the linear regression model by the processing unit according to the plurality of original working voltages, the plurality of original working currents and the plurality of original processing quality parameters.
9. According to claim 6a method for monitoring the precision of a manufacturing process, characterized by further comprising:
Performing the electric discharge machining process on a plurality of second reference workpieces in advance by the electric discharge machining apparatus, and detecting a plurality of reference electric discharge voltages and a plurality of reference electric discharge currents in the electric discharge machining process by the electric discharge machining apparatus in the plurality of second reference workpieces in advance by the processing unit;
Analyzing, by the processing unit, the plurality of reference discharge voltages and the plurality of reference discharge currents to extract a plurality of sets of reference characteristic parameters, and
Selecting, by the processing unit, at least a portion of each of the plurality of sets of reference feature parameters for establishing the electric discharge machining precision prediction model according to a workpiece type of the plurality of second reference workpieces.
10. The method of claim 9, wherein each of the plurality of sets of reference characteristic parameters includes a discharge frequency, an open ratio, a short ratio, an average short time, a short time standard deviation, an average short current, a short current standard deviation, an average delay time, a delay time standard deviation, an average discharge peak current, a peak current standard deviation, an average discharge time, a discharge time standard deviation, an average discharge energy, and a discharge energy standard deviation.
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