Guide pillar and guide sleeve and mold adjusting method applying guide pillar and guide sleeve

Abstract

The invention discloses a guide pillar guide sleeve and a mold adjusting method applying the guide pillar guide sleeve, and relates to the technical field of molds. Comprising the following steps: a data acquisition stage, a stage of acquiring mold state data based on a pressure sensor, a displacement sensor, a temperature sensor and a vibration sensor which are distributed on a guide pillar and a guide sleeve, a stage of resolving a pose, a stage of calculating a current pose deviation of the mold through a space pose resolving model based on the acquired data, and an optimization adjustment stage. And calling a gap compensation optimization algorithm to calculate an optimal adjustment amount, and driving an actuator to perform adjustment. According to the invention, various sensors are arranged on the guide pillar and the guide sleeve, so that the state data of the mold can be comprehensively and accurately acquired, the pose deviation of the mold is accurately calculated by utilizing a spatial position calculation model, and the optimal adjustment amount is solved by virtue of a constrained least square method, so that the high-precision adjustment of the mold is realized; and the assembly precision and the production quality stability of the die are effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of molds, in particular to a guide pin and guide sleeve and a mold adjustment method using the guide pin and guide sleeve. Background Art

[0002] Molds are various molds and tools used in industrial production to obtain the desired products by injection molding, blow molding, extrusion, die casting or forging, smelting, stamping and other methods. It is mainly a tool that makes the blank into a part with a specific shape and size under the action of external force. It is widely used in blanking, die forging, cold heading, extrusion, powder metallurgy pressing, pressure casting, and compression or injection molding of engineering plastics, rubber, ceramics and other products. The mold has a specific contour or inner cavity shape. The use of a contour shape with a cutting edge can make the blank separate (punch out) according to the shape of the contour line. The use of the inner cavity shape can make the blank obtain a corresponding three-dimensional shape. The mold generally consists of a movable mold and a fixed mold (or a punch and a die), which can be separated or combined. When separated, the workpiece is taken out, and when closed, the blank is injected into the mold cavity for forming. The mold is a precision tool with a complex shape. It bears the expansion force of the blank. It has high requirements for structural strength, rigidity, surface hardness, surface roughness and processing accuracy. The development level of mold production is one of the important indicators of the level of mechanical manufacturing. In recent years, with the rapid development and universality of the plastics industry, the continuous improvement of engineering plastics in strength and precision has led to the continuous expansion of the application scope of plastic products, such as household appliances, instruments and meters, construction equipment, automobile industry, daily hardware and many other fields. The proportion of plastic products is increasing rapidly. With the growing demand for plastic products, higher requirements are placed on the output of plastic molds.

[0003] The traditional mold guide pin and guide sleeve structure has the following shortcomings: lack of adaptive self-aligning capability, requiring repeated manual debugging, difficulty in dynamic compensation of the matching clearance, resulting in shortened mold life, no real-time status monitoring function, inability to predict failure risks, and self-aligning accuracy relying on workers' experience and poor consistency.

[0004] Therefore, a guide pin and guide sleeve and a mold adjustment method using the guide pin and guide sleeve are proposed. Summary of the invention

[0005] The object of the present invention is to provide a guide pin and guide sleeve and a mold adjustment method using the guide pin and guide sleeve to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a guide pin and guide sleeve and a mold adjustment method using the guide pin and guide sleeve, comprising the following steps:

[0007] In the data collection stage, the mold status data is collected based on the pressure sensors, displacement sensors, temperature sensors and vibration sensors distributed on the guide pins and guide sleeves;

[0008] In the pose calculation stage, based on the collected data, the current pose deviation of the mold is calculated through the spatial pose calculation model;

[0009] In the optimization and adjustment stage, the gap compensation optimization algorithm is called to calculate the optimal adjustment amount, and the actuator is driven to make adjustments;

[0010] In the verification and feedback stage, data is collected again to verify the adjustment effect. If the standard is not met, the adjustment is repeated;

[0011] In the predictive maintenance stage, based on the dynamic prediction model, the wear trend is estimated and maintenance suggestions are generated.

[0012] As a specific solution of the technical solution of the present application, the spatial pose calculation model uses a homogeneous transformation matrix for pose calculation, and the specific calculation formula is:

[0013] , where R is the rotation matrix, t is the translation vector, w is the rotation vector, is the skew-symmetric matrix of w, defined as: .

[0014] As a specific solution of the technical solution of the present application, the gap compensation optimization algorithm uses the least squares method with constraints to solve, and the objective function is:

[0015] , and the constraint condition is: , where is the weight coefficient of the i-th measurement point, is the actual gap value of the i-th measurement point, is the target gap value, is the regularization parameter, is the adjustment angle.

[0016] As a specific solution of the technical solution of the present application, the dynamic prediction model combines a physical model and a data-driven model, and the wear depth prediction formula is:

[0017] , where K is the material wear coefficient, load is the contact load, speed is the relative sliding speed, time is the running time, H is the material hardness, and correction is a correction term based on the LSTM neural network, which is calculated through the following model:

[0018]

[0019] As a specific solution of the technical solution of the present application, in the data acquisition stage, the Kalman filter algorithm is used to fuse multi-source sensor data, and the state space model is expressed as: , where is the system state vector, is the observation vector, is the state transition matrix, is the control input matrix, is the observation matrix, is the process noise, is the observation noise.

[0020] As a specific solution of the technical solution of the present application, the lubrication cycle generated in the predictive maintenance stage is calculated according to the following formula:

[0021] ;

[0022] Wherein, is the recommended lubrication cycle, is the lubrication system, which is related to the lubrication method and environment, load is the average load, speed is the average sliding speed, viscosity is the lubricating oil viscosity, and temperature is the working temperature.

[0023] A system for a guide pillar and guide sleeve and a die adjustment method using the same, comprising:

[0024] A guide pillar and guide sleeve assembly, including a pressure sensor, a displacement sensor, a temperature sensor, a vibration sensor and a piezoelectric ceramic actuator;

[0025] A data acquisition module for collecting and preprocessing sensor data;

[0026] An algorithm processing model that implements a spatial pose solution model, a clearance compensation optimization algorithm and a dynamic prediction model;

[0027] A control execution model that drives the piezoelectric ceramic actuator and the servo motor to realize die adjustment;

[0028] A human-computer interaction model that displays the adjustment process data and results and receives user instructions.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] The guide pillar and guide sleeve and the die adjustment method using the same can comprehensively and accurately collect the state data of the die by arranging a variety of sensors on the guide pillar and guide sleeve, accurately calculate the pose deviation of the die by using the spatial position solution model, and solve the optimal adjustment amount by using the least squares method with constraints, realizing high-precision adjustment of the die, effectively improving the assembly accuracy of the die and the stability of production quality.

[0031] Meanwhile, the dynamic prediction model combines the physical Jupiter and the data-driven Jupiter, which can estimate the wear trends of components such as guide pillars and guide bushes based on real-time data, generate reasonable maintenance suggestions and lubrication cycles, realize intelligent predictive maintenance of the mold, reduce maintenance costs and downtime, and extend the service life of the mold. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the system module process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention.

[0035] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0036] In the present invention, unless otherwise clearly specified and defined, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may mean that the first feature is directly above or obliquely above the second feature, or merely indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature being "under", "beneath" and "underneath" the second feature may mean that the first feature is directly below or obliquely below the second feature, or merely indicates that the horizontal height of the first feature is less than that of the second feature.

[0038] In the description of this specification, the description with reference to terms such as "an embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0039] As Figure 1 shown, the present invention provides a technical solution: a guide pillar and guide sleeve and a die adjustment method using the guide pillar and guide sleeve, comprising the following steps:

[0040] In the data acquisition stage, die state data is collected based on pressure sensors, displacement sensors, temperature sensors, and vibration sensors distributed on the guide pillar and guide sleeve. Four pressure sensors are circumferentially distributed on the guide pillar, and a thin-film pressure sensor with signal XPT2046 is used, with a range of 0 - 50KN, a measurement accuracy of ±0.1%, and a measurement point spacing of 90°. Three laser displacement sensors of model KEYENCE LK-G30 are arranged at the end face of the guide pillar and the bottom of the guide sleeve respectively, forming a spatial triangular measurement structure with a resolution of 0.1um. Two PT100 temperature sensors are buried on the surfaces of the guide pillar and the guide sleeve respectively, with an accuracy of ±0.5°C. A three-axis vibration sensor of model ADXL345 is installed at the root of the guide pillar, with a range of ±20g and a frequency response range of 0.5 - 10kHz. And a PLC (Siemens S7-1500) is used as the main controller, and multi-sensor data acquisition is realized through a hardware trigger synchronization mechanism. The acquisition frequency is dynamically adjusted according to the adjustment stage, which is 50Hz in the static debugging stage and increased to 500Hz in the dynamic movement stage. The collected data is transmitted to the edge controller through Ethernet.

[0041] In the predictive maintenance stage, the wear trend is predicted based on a dynamic prediction model and maintenance suggestions are generated.

[0042] In the pose calculation stage, based on the collected data, the current pose deviation of the mold is calculated through a spatial pose calculation model. The spatial pose calculation model uses a homogeneous transformation matrix for pose calculation, and the specific calculation formula is:

[0043] , where R is the rotation matrix, t is the translation vector, w is the rotation vector, is the skew-symmetric matrix of w, defined as: , determine the actual contact point position of the guide pillar and the guide sleeve through the pressure distribution, construct an error equation in combination with the displacement sensor data, and use the Levenberg-Marquardt algorithm to iteratively solve the optimal pose parameters. The iterative convergence condition is that the parameter change between two adjacent iterations is less than 0.001 mm, and finally a pose deviation matrix including 6 degrees of freedom (3 translations + 3 rotations) is obtained.

[0044] In the optimization and adjustment stage, call the clearance compensation optimization algorithm to calculate the optimal adjustment amount and drive the actuator to make adjustments. The clearance compensation optimization algorithm uses the least squares method with constraints to solve, and the objective function is:

[0045] , and the constraint condition is: , where, is the weight coefficient of the i-th measurement point, is the actual clearance value of the i-th measurement point, is the target clearance value, is the regularization parameter, is the adjustment angle, Adjust according to the importance of the sensor position, the weight of the edge point , the weight of the center point , Calculate through the pressure-clearance mapping model: , is the regularization parameter, determined by the cross-validation method. The actuator control converts the calculated adjustment amount into the voltage of the piezoelectric ceramic actuator and the pulse number of the servo motor. The voltage-displacement characteristic curve of the piezoelectric ceramic actuator is , the pulse equivalent of the servo motor is 0.001 mm / pulse, and the S-curve acceleration and deceleration algorithm is adopted, with a maximum acceleration of 0.5g.

[0046] In the verification and feedback stage, collect data again to verify the adjustment effect. If it does not meet the standard, repeat the adjustment. The data collection stage uses the Kalman filter algorithm to fuse multi-source sensor data, and the state space model is expressed as: , where, is the system state vector, is the observation vector, is the state transition matrix, For the control input matrix, For the observation matrix, For the process noise, For the observation noise. After the adjustment is completed, delay for 500 ms to wait for the system to stabilize. During this period, collect sensor data, repeat the pose calculation process, and evaluate by calculating the Cpk value. The calculation formula is:

[0047] ;

[0048] Among them, , , and are the mean and standard deviation of the actual clearance value respectively. When Cpk < 1.67, trigger a new round of adjustment, with a maximum of 3 iterations. If it still does not meet the standard, an alarm will be prompted.

[0049] The dynamic prediction model combines a physical model and a data-driven model. The wear depth prediction formula is:

[0050] , where K is the material wear coefficient, load is the contact load, speed is the relative sliding speed, time is the running time, H is the material hardness, and correction is a correction term based on the LSTM neural network, which is calculated through the following model: , K = 2.5×10 -8 (the material wear coefficient of die steel, calibrated through a pin-on-disc experiment), load is the contact load, calculated from the pressure sensor data, speed is the relative sliding speed, calculated by differentiating the displacement sensor data, and correction is calculated by the LSTM neural network. The network structure has 3 hidden layers, with 50 neurons in each layer. The input features include temperature, vibration, and historical wear data.

[0051] The lubrication period generated in the predictive maintenance stage is calculated according to the following formula:

[0052] , where is the recommended lubrication period, is the lubrication system, related to the lubrication method and environment, load is the average load, speed is the average sliding speed, viscosity is the lubricating oil viscosity, and temperature is the working temperature.

[0053] A system of a guide pillar and guide sleeve and a die adjustment method using the guide pillar and guide sleeve, comprising:

[0054] A guide pillar and guide sleeve assembly, including a pressure sensor, a displacement sensor, a temperature sensor, a vibration sensor, and a piezoelectric ceramic actuator;

[0055] The pressure sensor adopts a thin-film structure, with the sensitive element being a strain gauge, flush with the surface of the guide post, protection level IP67, overload capacity 200%FS. The displacement sensor is of the laser triangulation reflection type, with a measurement range of 0 - 50mm, response time <1ms, using red visible laser, arm length 650nm, spot diameter 0.1mm. The piezoelectric ceramic actuator adopts a multi-layer stacked structure, with the material being PZT-5H, maximum thrust 2KN, resolution 0.01um, and operating temperature range from -20°C to 80°C.

[0056] The data acquisition module is used to acquire and preprocess sensor data, adopting NI9234 data acquisition, 16-bit ADC, sampling rate 100kHz, supporting IEPE sensor power supply, and channel interval voltage 1000Vrms.

[0057] The algorithm processing model realizes the spatial pose solution model, gap compensation optimization algorithm, and dynamic prediction model. The algorithm processing module adopts an industrial-grade edge controller (Advantech UNO-2272G), Intel Core i7-6700TE processor, main frequency 2.8GHz, 8GB RAM, 256GB SSD, supporting an operating temperature of -20°C to 60°C. The software architecture is developed based on the ROS (Robot Operating System) Kinetic version, adopting a multi-threaded parallel computing architecture. The main thread is responsible for task scheduling and status management. The data processing thread processes sensor data in real time. The pose solution thread runs the spatial pose solution model. The optimization calculation thread executes the gap compensation optimization algorithm. The control output thread generates the actuator control signal.

[0058] The control execution model drives the piezoelectric ceramic actuator and the servo motor to achieve die adjustment. The piezoelectric ceramic drive adopts a PIE-665 high-voltage amplifier, with an output voltage range of 0 - 150V, bandwidth 10kHz, rise time <1ms, supporting analog and digital quantity diagrams. The servo motor control adopts a Panasonic MINAS A6 series servo drive, supporting full-closed-loop control, position control accuracy of ±1 pulse, and speed fluctuation <±0.01%.

[0059] The human-machine interaction model displays the adjustment process data and results, and receives user instructions. The human-machine interaction module adopts a 10.4-inch industrial-grade touch screen, resolution 800*600, LED backlight, brightness 500nits, supporting resistive touch, protection level IP65. The visualization interface is developed using the QT framework to achieve the following functions: 3D dynamic display of die pose deviation and adjustment process, real-time display of the gap distribution cloud map, differentiating gap sizes with different colors, plotting historical change curves of parameters such as pressure, temperature, and vibration, supporting real-time calculation of cpk values and quality trend analysis, storing and querying historical adjustment measurements, and supporting Excel export.

[0060] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A guide pillar and guide sleeve and a die adjustment method using the guide pillar and guide sleeve, characterized in that It includes the following steps: In the data acquisition stage, die state data is collected based on pressure sensors, displacement sensors, temperature sensors, and vibration sensors distributed on the guide pillars and bushings; In the pose calculation stage, based on the collected data, the current pose deviation of the die is calculated through a spatial pose calculation model; In the optimization and adjustment stage, a clearance compensation optimization algorithm is called to calculate the optimal adjustment amount and drive the actuator for adjustment; In the verification and feedback stage, data is collected again to verify the adjustment effect, and if it does not meet the standard, the adjustment is repeated; In the predictive maintenance stage, the wear trend is predicted based on a dynamic prediction model and maintenance suggestions are generated.

2. The guide pillar and guide sleeve according to claim 1 and the mold adjustment method using the same are characterized in that: The spatial pose calculation model uses a homogeneous transformation matrix for pose calculation, and the specific calculation formula is: , where R is the rotation matrix, t is the translation vector, w is the rotation vector, is the skew-symmetric matrix of w, defined as: .

3. A guide pillar and guide sleeve and a die adjustment method using the same according to claim 1, characterized in that: The clearance compensation optimization algorithm uses the least squares method with constraints to solve, and the objective function is: , the constraint conditions are: , where is the weight coefficient of the i-th measurement point, is the actual clearance value of the i-th measurement point, is the target clearance value, is the regularization parameter, is the adjustment angle.

4. A guide pillar and guide sleeve and a die adjustment method using the same according to claim 1, characterized in that: The dynamic prediction model combines a physical model and a data-driven model, and the wear depth prediction formula is: , where K is the material wear coefficient, load is the contact load, speed is the relative sliding speed, time is the running time, H is the material hardness, and correction is a correction term based on the LSTM neural network, which is calculated through the following model:

5. A guide pillar and guide sleeve according to claim 1, and a die adjustment method using the guide pillar and guide sleeve, characterized in that: In the data acquisition stage, the Kalman filtering algorithm is used to fuse multi-source sensor data, and the state space model is expressed as: , where is the system state vector, is the observation vector, is the state transition matrix, is the control input matrix, is the observation matrix, is the process noise, is the observation noise.

6. A guide pillar and guide sleeve according to claim 1, and a die adjustment method using the guide pillar and guide sleeve, characterized in that: The lubrication period generated in the predictive maintenance stage is calculated according to the following formula: , where is the recommended lubrication period, is the lubrication system, which is related to the lubrication method and environment, load is the average load, speed is the average sliding speed, viscosity is the lubricating oil viscosity, and temperature is the operating temperature.

7. A system of a guide pillar and a guide bushing and a die adjustment method using the guide pillar and the guide bushing, characterized in that: It includes: A guide pillar and bushing assembly, including a pressure sensor, a displacement sensor, a temperature sensor, a vibration sensor, and a piezoelectric ceramic actuator; A data acquisition module for collecting and preprocessing sensor data; An algorithm processing model that implements a spatial pose calculation model, a clearance compensation optimization algorithm, and a dynamic prediction model; A control execution model that drives the piezoelectric ceramic actuator and the servo motor to achieve die adjustment; A human-machine interaction model that displays the adjustment process data and results and receives user instructions.

Images