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CN-121820804-B - Electric spark machining efficiency analysis system based on historical working conditions

CN121820804BCN 121820804 BCN121820804 BCN 121820804BCN-121820804-B

Abstract

The invention discloses an electric spark machining efficiency analysis system based on historical working conditions, which relates to the technical field of machining analysis, and the invention generates an event ledger according to pulse events in the electric spark machining process, extracts event spectrum characteristics, decomposes energy according to event types into effective corrosion, short-circuit loss and arc loss, calculates energy supply effective efficiency, further generating indexes of chip removal retention, medium pollution and servo matching, outputting the issued clearance clearing pulse, rebound rhythm, flushing duty ratio and servo gain switching parameters by combining the historical prescription threshold, and giving out a rechecking difference value of efficiency and cluster length and recovery and separation after execution, thereby realizing attribution of efficiency reduction of deep cavity section and correction closed loop.

Inventors

  • XU GUIJUN
  • Zhang Menxing

Assignees

  • 小脉冲(南通)智能装备有限公司

Dates

Publication Date
20260512
Application Date
20260311

Claims (10)

  1. 1. The electric spark machining efficiency analysis system based on the historical working conditions is characterized by comprising an event account book generation module, an event spectrum feature extraction module, an energy decomposition calculation module, a cryptogenic index generation module, a historical prescription matching module and an action rechecking module: The event ledger generation module is used for collecting gap voltage sampling data, gap current sampling data, pulse switch state data, servo feeding instruction data, flushing state data, rebound state data and process step section data, generating event window data based on the pulse switch state data, generating discharge event type marking data and event integral energy data in an event window, packaging to form discharge event record data and collecting the discharge event record data into event ledger data; The event spectrum feature extraction module is used for generating short circuit cluster data and recovery statistical data based on event account data and summarizing the short circuit cluster data and the recovery statistical data to generate event spectrum feature data; the energy decomposition calculation module is used for generating input energy accumulation data, effective erosion energy data, short circuit loss energy data and arc loss energy data based on event account data, and calculating energy supply effective efficiency data according to the input energy accumulation data and the effective erosion energy data; the hidden factor index generation module is used for generating chip removal retention index data and medium pollution index data based on the event spectrum characteristic data and generating servo matching rate data based on servo feeding instruction data and recovery statistical data; the history prescription matching module is used for outputting candidate prescription data from the history prescription data based on energy supply effective rate data, chip removal retention index data, medium pollution index data, servo matching rate data and process step number data; the action rechecking module is used for generating and issuing deviation rectifying action sequence data based on candidate prescription data, regenerating energy supply effective rate data and event spectrum characteristic data based on updated event account data after issuing, and outputting rechecking result data.
  2. 2. The system for analyzing the electric discharge machining efficiency based on the historical operating conditions according to claim 1, wherein the event ledger wall generation module comprises an acquisition unit, an event window generation unit, an event segmentation unit and a ledger wall writing unit; The acquisition unit is used for acquiring gap voltage sampling data, gap current sampling data, pulse switch state data, servo feeding instruction data, flushing state data, rebound state data and work step number data; the event window generating unit is used for generating event window data by the on-off edges of the pulse switch state data; The event segmentation unit is used for generating discharge event type marking data, event integral energy data and event context marking data in an event window based on the event window data; the account book writing unit is used for packaging the step number data, the discharge event type marking data, the event integral energy data and the event context marking data into discharge event record data and writing the discharge event account book data.
  3. 3. The system for analyzing electric discharge machining efficiency based on historical operating conditions as set forth in claim 2, wherein the event segmentation unit generates discharge event record data satisfying: The event integration energy data is obtained by multiplying and accumulating gap voltage sampling data and gap current sampling data in an event window according to sampling intervals; The discharging event type marking data comprise a normal discharging mark, a short circuit mark, an arc mark and an open circuit mark, and window statistics of gap voltage sampling data and gap current sampling data in an event window are obtained according to a preset judging rule; The event context marking data is obtained by combining servo feeding instruction data, flushing state data and rebound state data corresponding to the starting moment of an event window.
  4. 4. The system for analyzing the electrical discharge machining efficiency based on the historical operating conditions according to claim 3 wherein the event spectrum feature extraction module comprises a cluster construction unit and a recovery statistical unit; The cluster construction unit is used for searching discharge event record data corresponding to the short circuit mark in event account book data, merging the two adjacent short circuit mark discharge event record data into the same short circuit cluster and generating short circuit cluster data when the event starting interval of the two adjacent short circuit mark discharge event record data is smaller than the cluster interval threshold value data, wherein the short circuit cluster data comprises cluster length data and cluster energy accumulation data; The recovery statistical unit is used for generating post-event recovery time length data, wherein the post-event recovery time length data is a time interval from the end of discharge event record data of a short circuit mark or an arc mark to the beginning of the next normal discharge mark discharge event record data, and calculating recovery quantile data for the post-event recovery time length data; and writing the short circuit cluster data and the recovery fractional data into event spectrum characteristic data.
  5. 5. The system for analyzing efficiency of electric discharge machining based on historical operating conditions as claimed in claim 4, wherein said energy decomposition calculation module comprises an energy accumulation unit and an energy distribution unit; the energy accumulation unit is used for accumulating event integral energy data in event ledger data to obtain input energy accumulation data; The energy distribution unit is used for classifying and accumulating event integral energy data according to the discharge event type mark data to obtain short circuit loss energy data and arc loss energy data, and multiplying the event integral energy data corresponding to the normal discharge mark by the erosion correction coefficient data to obtain effective erosion energy data; The energy supply effective rate data is obtained by calculating effective energy erosion data and input energy accumulation data.
  6. 6. The system for analyzing the efficiency of electric discharge machining based on the historical operating condition according to claim 5, wherein the hidden factor index generating module comprises a retention index unit, a pollution index unit and a servo matching unit; the retention index unit is used for generating chip removal retention index data by taking cluster length data, cluster energy accumulation data and recovery quantile data as inputs; The pollution index unit is used for generating medium pollution index data by taking arc marking event counting duty ratio data, arc marking event integral energy duty ratio data and recovery quantile data as inputs; The servo matching unit is used for generating servo matching rate data by taking window fluctuation quantity data and recovery quantile data of servo feeding instruction data as inputs.
  7. 7. The system of claim 6, wherein the historical recipe matching module comprises a recipe inventory unit and a matching scoring unit; The prescription stock unit stores historical prescription data, wherein the historical prescription data at least comprises applicable boundary data, event threshold data, deviation correcting action template data and erosion correction coefficient data; the applicable boundary data at least comprises material type data, electrode type data, processing polarity data and a process step number data range; The event threshold data at least comprises cluster interval threshold data, maximum cluster length threshold data and recovery bit threshold data.
  8. 8. The system of claim 7, wherein the matching and scoring unit is configured to screen historical prescription data based on step number data and applicable boundary data to obtain a boundary candidate set, compare cluster length data, recovery quantile data and arc mark event count duty ratio data in the event spectrum feature data with event threshold data to obtain threshold deviation data, generate prescription score data based on the threshold deviation data and energy supply efficiency data, and output historical prescription data satisfying a preset condition as candidate prescription data from the boundary candidate set.
  9. 9. The system for analyzing the electric discharge machining efficiency based on the historical operating conditions according to claim 8, wherein the action rechecking module comprises an action parameterization unit and a rechecking calculation unit; the action parameterization unit is used for parameterizing deviation rectifying action template data in candidate prescription data to generate clear gap pulse sequence parameter data, rebound rhythm parameter data, flushing liquid duty ratio parameter data and servo gain switching parameter data, and combining the parameter data to generate deviation rectifying action sequence data.
  10. 10. The system for analyzing the electrical discharge machining efficiency based on the historical operating conditions according to claim 9, wherein the rechecking calculation unit is used for reading updated event ledger data after the deviation correcting action sequence data is issued, regenerating energy supply effective rate data and event spectrum characteristic data, and outputting rechecking result data; The rechecking result data at least comprises energy supply effective rate difference data, maximum cluster length difference data and recovery quantile difference data.

Description

Electric spark machining efficiency analysis system based on historical working conditions Technical Field The invention relates to the technical field of machining analysis, in particular to an electric spark machining efficiency analysis system based on historical working conditions. Background In deep cavity forming electric spark machining, the machining efficiency is highly coupled with the clearance discharge state, the chip removal smoothness, the medium pollution, the temperature rise and the servo feeding stability. The phenomenon that the same parameter and the front section are normal and the rear section drag slowly in the same procedure frequently occurs on site is shown as short circuit and arc increase, gap recovery becomes slow and electrode abrasion is fast, but machine tool alarming and conventional statistics are difficult to give clear attribution and executable deviation correction. The existing method is based on multi-source sensor alignment, off-line trial cutting or experience parameter adjustment, so that efficiency loss is difficult to disassemble into distinguishable mechanisms of short circuit clustering, arc tailing and servo mismatch under the condition of no shutdown, and historical experience is precipitated into a movable working condition prescription, so that the deep cavity section processing period is unstable, reworking and electrode consumption are increased. At present, the Chinese patent application No. CN202111096841.3 discloses an automatic trimming method, an automatic trimming device and a storage medium for a discharge electrode, which comprise the steps of acquiring first site information before electric spark machining operation is carried out, acquiring second site information after electric spark machining operation is carried out, acquiring electrode loss parameters according to the first site information and the second site information, comparing the electrode loss parameters with a preset parameter threshold value to obtain a comparison result, and controlling a trimming mechanism to trim the discharge electrode. Through the implementation of the invention, after the electric spark numerical control machine tool performs electric spark machining operation, electrode loss parameters of the discharge electrode in the electric spark machining operation process are automatically analyzed through the first position information and the second position information, a comparison result is correspondingly obtained, the discharge electrode is automatically trimmed according to the comparison result, the human involvement degree is low in the whole automatic trimming process, and the trimming efficiency is improved by automatically executing the trimming process by using a trimming mechanism integrated in the machine tool. The technology is difficult to finish verifiable loss due to the reduction of deep cavity electric spark machining efficiency from a discharge event structure under the condition of no shutdown, and output deviation correcting action parameters and rechecking results which can be directly issued. Disclosure of Invention The invention solves the technical problems that the prior art is difficult to finish verifiable loss attribution of deep cavity electric spark machining efficiency reduction from a discharge event structure under the condition of no shutdown, and outputs deviation correcting action parameters and rechecking results which can be directly issued. In order to solve the technical problems, the invention provides the following technical scheme: the electric spark machining efficiency analysis system based on the historical working conditions comprises an event ledger generation module, an event spectrum feature extraction module, an energy decomposition calculation module, a hidden factor index generation module, a historical prescription matching module and an action rechecking module: The event ledger generation module is used for collecting gap voltage sampling data, gap current sampling data, pulse switch state data, servo feeding instruction data, flushing state data, rebound state data and process step section data, generating event window data based on the pulse switch state data, generating discharge event type marking data and event integral energy data in an event window, packaging to form discharge event record data and collecting the discharge event record data into event ledger data; The event spectrum feature extraction module is used for generating short circuit cluster data and recovery statistical data based on event account data and summarizing the short circuit cluster data and the recovery statistical data to generate event spectrum feature data; the energy decomposition calculation module is used for generating input energy accumulation data, effective erosion energy data, short circuit loss energy data and arc loss energy data based on event account data, and calculating energy supply effective efficiency data ac