Stamping die detection adjustment method and system based on use working condition

Abstract

The application discloses a stamping die detection adjustment method and system based on working conditions, comprising the following steps: S1, constructing a multi-physical field coupling reference model; S2, anti-interference signal acquisition and processing; S3, multi-level fault diagnosis: based on the acoustic emission high-frequency energy mutation rate to trigger the preliminary warning, and combined with the vibration-temperature coupling factor to cross-verify the fault; S4, online dynamic compensation and verification. The application multi-source signal fusion: acoustic emission (crack sensitive), vibration (structure loosening sensitive), and thermal imaging (cooling abnormal sensitive) three modal cross-verification, reduces the missed detection rate from 15% to ≤2.1%; the vibration-temperature coupling factor β: reveals the nonlinear relationship between thermal deformation and mechanical vibration, makes the guide pillar eccentric wear and other hidden faults detected 800 stamping periods in advance; the dynamic arbitration mechanism: when the acoustic emission and the β factor conflict, the strain sensor is automatically activated for rechecking, the false alarm rate is reduced by 67%, and the fault diagnosis accuracy is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of precision molds, in particular to a stamping die detection and adjustment method and system based on working conditions. BACKGROUND

[0002] With the surge in demand for precision molds in high-end manufacturing, micro-defect control in the metal stamping / injection molding process has become a pain point in the industry. The existing technology has three bottlenecks:

[0003] Single-mode monitoring blind area: traditional single-sensor systems based on vibration or temperature cannot capture the thermal-force-acoustic coupling effect, resulting in a failure detection rate of more than 15% for early cracks (<0.1mm) and local sticking;

[0004] Closed-loop verification missing: compensation devices lack real-time effectiveness verification, and 34% of invalid compensation accelerates mold wear in actual applications.

[0005] A particularly prominent case is the production of automotive coverings: warping deformation caused by asymmetric cooling requires repeated mold adjustment, with a single mold adjustment cost of more than $50,000. In the medical micro-part field, poor texture replication caused by micro-vibration leads to a product scrap rate of up to 22%. SUMMARY

[0006] The purpose of the present application is to provide a stamping die detection and adjustment method and system based on working conditions to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides the following technical solution: a stamping die detection and adjustment method based on working conditions, comprising the following steps:

[0008] S1, constructing a multi-physical field coupling reference model: establishing an acoustic-thermal-force-flow coupling simulation model according to the mold geometry and material properties, dynamically updating the three-modal reference dataset, including acoustic emission spectrum baseline, thermal image spatiotemporal feature map, and vibration response envelope;

[0009] S2, anti-interference signal acquisition and processing: collecting acoustic emission signals through a MEMS microphone array and adding adaptive band-stop filtering, and using a partitioned thermal transfer function to perform inverse convolution operation on infrared thermography data to restore the real temperature field;

[0010] S3, multi-level fault diagnosis: based on the acoustic emission high-frequency energy mutation rate to trigger preliminary warning, combined with the vibration-temperature coupling factor for fault cross-validation, when the acoustic emission warning and β are normal, the strain sensor is started for review, when β is abnormal and there is no acoustic emission warning, the cooling system is checked;

[0011] S4, online dynamic compensation and verification: the magneto-rheological elastomer actuator generates a deformation compensation force, adjusts the hydraulic support stiffness coefficient, and verifies the effectiveness of the compensation through secondary acquisition of acoustic emission signals and decides the subsequent action.

[0012] The acoustic-thermal-force-flow coupling simulation model in S1 satisfies:

[0013]

[0014] Where h is the convective heat transfer coefficient, T coolant is the real-time temperature of the cooling liquid.

[0015] The inverse convolution operation of the partition heat transfer function in S2 is defined as:

[0016] T real = T meas ⊙H -1 (s)

[0017] Where H(s) is the partition heat transfer function matrix, and ⊙ represents the element-wise convolution operation.

[0018] The calculation method of the vibration-temperature coupling factor in S3 is:

[0019]

[0020] Where a env is the vibration acceleration envelope value, T i is the temperature of the i-th wear-sensitive zone, and when β exceeds the preset interval [βmin, βmax], the fault is determined.

[0021] The preset interval is dynamically set according to the fault type:

[0022] Guide pillar abrasion fault: β ∈ [0.65, 0.75];

[0023] Top pin sticking fault: β ∈ [1.30, 1.45];

[0024] Template micro-crack fault: β ∈ [0.95, 1.05].

[0025] The compensation force of the magneto-rheological elastomer actuator in S4 is generated by the following formula:

[0026]

[0027] Where Δd is the measured deformation of the laser displacement sensor, K p and K d are proportional and differential coefficients.

[0028] The adjustment of the hydraulic support stiffness coefficient in S4 satisfies:

[0029]

[0030] wherein γ is a stiffness gain coefficient, β0 is an ideal coupling factor value, sat(·) is a saturation function.

[0031] The determination condition of the compensation effectiveness is that the sudden change rate of the acoustic emission energy δE' collected twice k satisfies:

[0032]

[0033] wherein δE k is the sudden change rate of the acoustic emission energy before compensation

[0034] The stamping die detection and adjustment system based on the use working condition comprises:

[0035] A multi-modal sensing module: a MEMS microphone array (frequency response 20-180 kHz) with an adaptive bandpass filter, an infrared thermal imager (accuracy ±0.5℃), and a three-axis vibration sensor (range ±50g);

[0036] A signal processing module: an adaptive bandpass filter and a partition heat transfer inversion algorithm are built-in;

[0037] A compensation execution module: a magneto-rheological elastomer actuator (output 0.5-2kN, resolution 1μm) and a digital proportional valve controlled hydraulic support;

[0038] A control module: multi-level diagnostic decision and compensation effect verification are realized based on FPGA.

[0039] Compared with the prior art, the beneficial effects of the present application are:

[0040] Multi-source signal fusion of the present application: cross verification of three modes of acoustic emission (crack sensitive), vibration (structure loosening sensitive) and thermal image (cooling abnormal sensitive), reduces the missed detection rate from 15% to ≤2.1%; vibration-temperature coupling factor β: reveals the nonlinear relationship between thermal deformation and mechanical vibration, and makes the detection of hidden faults such as guide pillar wear advance by 800 stamping periods; dynamic arbitration mechanism: when acoustic emission and β factor conflict, automatically activate strain sensor for review, false positive rate is reduced by 67%, and fault diagnosis accuracy is increased.

[0041] Anti-interference signal processing of the present application: adaptive bandpass filter suppresses 23.4kHz hydraulic pump noise, and signal-to-noise ratio is improved by 40dB; dynamic threshold adjustment: β threshold value is automatically compensated with die life, avoids late misjudgment caused by fixed threshold value, and air floating vibration isolation platform makes the vibration transmission rate <5%; double-index closed-loop verification (acoustic emission sudden change rate + vibration drop rate) ensures the compensation effectiveness, and the comprehensive judgment accuracy reaches 98.7%. BRIEF DESCRIPTION OF DRAWINGS

[0042] Fig. 1 Flowchart of the present application;

[0043] Fig. 2 System block diagram of the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0045] Please refer to Figs. 1-2 The present application provides a technical solution: a stamping die detection and adjustment method based on working conditions, comprising the following steps:

[0046] S1, constructing a multi-physics field coupling reference model: establishing an acoustic-thermal-force-flow coupling simulation model according to the die geometry and material properties, dynamically updating the three-mode reference data set, including acoustic emission spectrum baseline, thermal image space-time feature map and vibration response envelope;

[0047] S2, anti-interference signal acquisition and processing: collecting acoustic emission signals through a MEMS microphone array and superimposing adaptive band-stop filtering, using a partitioned heat transfer function to perform inverse convolution operation on infrared thermal imager data to restore the real temperature field;

[0048] S3, multi-level fault diagnosis: triggering preliminary warning based on acoustic emission high-frequency energy mutation rate, combining vibration-temperature coupling factor for fault cross-validation, starting strain sensor review when acoustic emission warning and normal beta, checking the cooling system when beta is abnormal and there is no acoustic emission warning;

[0049] S4, online dynamic compensation and verification: driving the magneto-rheological elastomer actuator to generate deformation compensation force, adjusting the hydraulic support stiffness coefficient, verifying the compensation effectiveness by secondary acoustic emission signal acquisition and deciding the subsequent action.

[0050] The acoustic-thermal-force-flow coupling simulation model in S1 satisfies:

[0051]

[0052] Where h is the convective heat transfer coefficient, T coolant is the real-time temperature of the cooling liquid.

[0053] The inverse convolution operation of the partitioned heat transfer function in S2 is defined as:

[0054] T real = T meas ⊙H-1 (s)

[0055] wherein H(s) is a partitioned heat transfer function matrix, and represents an element-wise convolution operation.

[0056] The calculation method of the vibration-temperature coupling factor in S3 is as follows:

[0057]

[0058] wherein a env is a vibration acceleration envelope value, T i is the temperature of the i-th wear-sensitive zone, and when β exceeds the preset interval [βmin, βmax], the fault is determined.

[0059] The preset interval is dynamically set according to the fault type:

[0060] Guide pillar abrasion fault: β ∈ [0.65, 0.75];

[0061] Needle jamming fault: β ∈ [1.30, 1.45];

[0062] Template micro-crack fault: β ∈ [0.95, 1.05].

[0063] The compensation force of the magneto-rheological elastomer actuator in S4 is generated by the following formula:

[0064]

[0065] wherein Δd is the measured deformation of the laser displacement sensor, K p and K d are proportional and differential coefficients.

[0066] The adjustment of the hydraulic supporting stiffness coefficient in S4 satisfies:

[0067]

[0068] wherein γ is a stiffness gain coefficient, β0 is an ideal coupling factor value, and sat(·) is a saturation function.

[0069] The compensation effectiveness determination condition is that the acoustic emission energy mutation rate δE′ k satisfies:

[0070]

[0071] wherein δE k is the acoustic emission energy mutation rate before compensation.

[0072] The stamping die detection and adjustment system based on the use working condition comprises:

[0073] Multi-modal sensing module: MEMS microphone array with adaptive band-stop filter (frequency response 20-180 kHz), infrared thermal imager (accuracy ±0.5℃), tri-axial vibration sensor (range ±50g);

[0074] Signal processing module: built-in adaptive band-stop filter and zonal heat transfer inversion algorithm;

[0075] Compensation execution module: magneto-rheological elastomer actuator (output 0.5-2kN, resolution 1μm), digitally proportional valve-controlled hydraulic support;

[0076] Control module: multi-level diagnostic decision and compensation effect verification based on FPGA.

[0077] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Claims

1. A method for inspecting and adjusting stamping dies based on operating conditions, characterized in that, Includes the following steps: S1. Construct a multi-physics coupled benchmark model: Based on the mold geometry and material properties, establish an acoustic-thermal-mechanical-fluid coupled simulation model, dynamically update the three-modal benchmark dataset, including acoustic emission spectrum baseline, thermal image spatiotemporal feature map and vibration response envelope; S2. Anti-interference signal acquisition and processing: Acoustic emission signals are acquired through MEMS microphone array and superimposed with adaptive band-stop filter. The infrared thermal imager data is deconvolved using the partitioned heat transfer function to restore the real temperature field. S3, Multi-level fault diagnosis: Based on the high-frequency energy mutation rate of acoustic emission, a primary early warning is triggered, and the fault cross-verification is performed in combination with the vibration-temperature coupling factor. When the acoustic emission warning is triggered but β is normal, the strain sensor is activated for verification. When β is abnormal but there is no acoustic emission warning, the cooling system is checked. S4. Online dynamic compensation and verification: The drive magnetorheological elastomer actuator generates deformation compensation force, adjusts the stiffness coefficient of the hydraulic support, verifies the effectiveness of compensation by secondary acquisition of acoustic emission signals, and decides on subsequent actions.

2. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The acoustic-thermal-mechanical-fluid coupling simulation model in S1 satisfies: Where h is the convective heat transfer coefficient, T coolant This is the real-time temperature of the coolant.

3. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The inverse convolution operation of the partitioned heat transfer function in S2 is defined as follows: T real =T meas ⊙H -1 (s) Where H(s) is the partitioned heat transfer function matrix, and ⊙ represents element-wise convolution operation.

4. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The vibration-temperature coupling factor in S3 is calculated as follows: Where a env T is the envelope value of vibration acceleration. i Let β be the temperature of the i-th wear-sensitive zone. A fault is determined when β exceeds the preset range [βmin, βmax].

5. The stamping die inspection and adjustment method based on operating conditions according to claim 4, characterized in that: The preset interval is dynamically set according to the fault type: Guide post wear fault: β∈[0.65,0.75]; Ejector pin jamming fault: β∈[1.30,1.45]; Template microcrack fault: β∈[0.95,1.05].

6. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The compensation force of the magnetorheological elastomer actuator in S4 is generated by the following formula: Where Δd is the measured deformation of the laser displacement sensor, and K p and K d is the proportional differential coefficient.

7. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The adjustment of the hydraulic support stiffness coefficient in S4 satisfies: Where γ is the stiffness gain coefficient, β0 is the ideal coupling factor value, and sat(·) is the saturation function.

8. The stamping die inspection and adjustment method based on operating conditions according to claim 1, characterized in that: The criterion for determining the effectiveness of the compensation is: the change rate δE′ of the acoustic emission energy in the secondary acquisition. k satisfy: Where δE k To compensate for the abrupt change rate of the foreground emission energy.

9. A stamping die inspection and adjustment system based on operating conditions, used to implement the inspection and adjustment method according to any one of claims 1-8, characterized in that, include: Multimodal sensing module: including MEMS microphone array with adaptive band-stop filter, infrared thermal imager and triaxial vibration sensor; Signal processing module: includes a built-in adaptive band-stop filter and a partitioned heat transfer inversion algorithm; Compensation execution module: includes magnetorheological elastomer actuator and digital proportional valve-controlled hydraulic support; Control module: Based on FPGA, it realizes multi-level diagnostic decision-making and compensation effect verification.

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