3D printing substrate calibration method and system based on IPMC sensing array

Abstract

The invention relates to a 3D printing substrate calibration method and system based on an IPMC sensing array. The method comprises the following steps: S300, based on an offset value of each point of a substrate on a z axis, performing mesh bed reconstruction by using a bicubic interpolation method; and S400, predicting the flatness of the net bed by using a least square area method, and minimizing the total error. The system comprises: a sensor element comprising an ionic polymer metal composite (IPMC) sensing array embedded in a 3D printed substrate; the sensor conditioning circuit is used for amplifying a signal acquired by the sensor element; the MCU lower computer is used for carrying out binarization processing on the received signal; and the upper computer is used for generating a pressure cloud picture after executing the bicubic interpolation method and the least square area method. In each Ionic Polymer Metal Composite (IPMC) encapsulation structure in the Ionic Polymer Metal Composite (IPMC) sensing array, the Ionic Polymer Metal Composite (IPMC) is encapsulated in a polydimethylsiloxane (PDMS) film.

Description

Technical Field

[0001] The present disclosure relates to the field of substrate calibration in the 3D printing industry, and more specifically, to a 3D printing substrate calibration method and system based on an IPMC sensing array. Background Art

[0002] This Background generally presents the context of the present disclosure. To the extent described in this Background, the work of the presently named inventors and aspects of the specification that may not constitute prior art at the time of filing are neither expressly nor impliedly admitted to be prior art to the present disclosure.

[0003] The quality of 3D printing depends largely on the flatness of the substrate. In the field of 3D printing technology, traditional substrate calibration methods mainly include manual leveling and mechanical leveling. Manual leveling relies on the operator's experience, has low efficiency and limited accuracy; while mechanical leveling improves efficiency, it still has the problem of insufficient calibration accuracy.

[0004] In order to further improve the success rate of 3D printing and the accuracy of the first layer of printing, 3D printing equipment manufacturers and maker communities have introduced a variety of sensor technologies, such as proximity sensors, servo probes, BL-TOUCH (an open source-based intelligent 3D printer automatic leveling sensor that can accurately measure and adjust the distance between the substrate and the 3D printer nozzle in an easy-to-use way to achieve automatic leveling), Hall sensors and pressure sensors. These sensors monitor the status of the substrate in real time and apply advanced algorithms such as bicubic interpolation and / or Lagrangian algorithms to achieve dynamic compensation and calibration of the substrate. However, these sensors are susceptible to changes in ambient temperature during long-term use, resulting in internal temperature drift, which causes large errors in the collected data.

[0005] With the rapid development of Fused Deposition Modeling (FDM) high-speed 3D printing technology, the importance of the first layer printing quality has become increasingly prominent, because the quality of the first layer printing is directly related to the adhesion of the entire 3D printed part and the accuracy of the final product.

[0006] In order to meet the growing demand for 3D printing quality and efficiency, some leading 3D printing equipment manufacturers and maker communities have gradually conducted research on multi-head 3D printers to achieve the simultaneous execution of multiple 3D printing tasks and improve production efficiency.

[0007] However, in a multi-head 3D printing environment, the options for mainboard expansion are relatively few, and sensor expansion is also subject to certain restrictions, which may lead to lower sensor sensitivity, thereby reducing the efficiency of the 3D printer. Especially in a multi-head 3D printing environment, the sensitivity and response speed of the sensor become key factors restricting the efficiency of 3D printing.

[0008] Therefore, in view of the shortcomings of the prior art, the present disclosure urgently needs to develop a new 3D printing substrate calibration method and system. Improve calibration efficiency, simplify the operation process, control calibration costs, and improve the sensitivity and response speed of the sensor. Achieve more accurate and efficient substrate calibration, meet the needs of high-speed 3D printing and multi-head 3D printing, and promote the development of 3D printing technology to a higher level. Summary of the invention

[0009] The technical problem of the present disclosure is: to design a new 3D printing substrate calibration method and system in view of the defects of the existing substrate calibration method. The defects of the existing technology are fully overcome, the calibration efficiency is improved, the operation process is simplified, the calibration cost is controlled, and the sensitivity and response speed of the sensor are improved. A more accurate and efficient substrate calibration is achieved to meet the needs of high-speed printing and multi-head printing, and to promote the development of 3D printing technology to a higher level.

[0010] In order to solve at least one of the above technical problems, the first aspect of the present disclosure proposes a 3D printing substrate calibration method based on an ionic polymer metal composite (IPMC) sensing array, the method comprising the following steps:

[0011] S300: Based on the offset value of each point on the substrate on the z-axis, the mesh bed is reconstructed using a bicubic interpolation method;

[0012] S400: Use the least squares area method to predict the flatness of the mesh bed and minimize the total error.

[0013] In a preferred embodiment of the present disclosure, step S400 further includes the following steps:

[0014] S410: Calculate the vertical distance from the actual observed coordinates of each data point to the fitting plane:

[0015] S420: defining the sum of squares of all vertical distances as the total error, and calculating the parameter value in the fitting plane;

[0016] S430: Minimize the total error by adjusting the calibration parameter value.

[0017] In a preferred embodiment of the present disclosure, step S300 further includes the following steps:

[0018] S320: Record the offset value of each point on the substrate on the z-axis;

[0019] S330: Reconstruct the mesh bed using a bicubic interpolation method.

[0020] In a preferred embodiment of the present disclosure, in step S330, the following steps are also included:

[0021] S332: Determine the nearest point near the point to be interpolated:

[0022] S334: Calculate the horizontal interpolation weight and the vertical interpolation weight of the nearest point using the horizontal distance and the vertical distance from the nearest point to the point to be interpolated;

[0023] S336: using the horizontal interpolation weight and the vertical interpolation weight of the nearest point, based on the bicubic interpolation method, calculate the z-axis offset value of the nearest point for the point to be interpolated;

[0024] S338: Reconstruct the precise shape of the entire mesh bed based on the z-axis offset value of the nearest point to the point to be interpolated.

[0025] In a preferred embodiment of the present disclosure, before step S300, the following steps are also included:

[0026] S100: encapsulating the ionic polymer metal composite material (IPMC) in a polydimethylsiloxane (PDMS) film and leading out electrodes to form an ionic polymer metal composite material (IPMC) encapsulation structure;

[0027] S200: Embed a plurality of ionic polymer metal composite (IPMC) packaging structures in a 3D printed substrate to form an ionic polymer metal composite (IPMC) sensing array.

[0028] In a preferred embodiment of the present disclosure, in step S100, the following steps are also included:

[0029] S120: preparing a polydimethylsiloxane (PDMS) mixed liquid, and mixing it evenly using a mechanical stirrer;

[0030] S140: vacuuming the polydimethylsiloxane (PDMS) mixed liquid to eliminate bubbles generated during the mixing process;

[0031] S160: applying the vacuumed polydimethylsiloxane (PDMS) mixed liquid onto the surface of the ionic polymer metal composite material (IPMC);

[0032] S180: Connecting electrodes to both ends of the ionic polymer metal composite (IPMC) sensing array and leading out the electrodes;

[0033] S190: Curing the polydimethylsiloxane (PDMS) film to form a packaging structure.

[0034] In a preferred embodiment of the present disclosure, in step S120, the following steps are also included:

[0035] A polydimethylsiloxane (PDMS) A solution and a polydimethylsiloxane (PDMS) B solution are mixed in a mass ratio of 10:1, wherein the polydimethylsiloxane (PDMS) A solution includes a poly-dimethyl-methyl-vinyl-siloxane prepolymer and a trace amount of platinum catalyst, and the polydimethylsiloxane (PDMS) B solution includes a prepolymer with a vinyl side chain and a crosslinking agent.

[0036] In order to solve at least one of the above technical problems, the second aspect of the present disclosure proposes a 3D printing substrate calibration system based on an ionic polymer metal composite (IPMC) sensing array, comprising:

[0037] The sensor element includes an ionic polymer metal composite (IPMC) sensing array embedded in a 3D printed substrate;

[0038] A sensor conditioning circuit for amplifying the signal collected by the sensor element;

[0039] MCU lower computer, used to perform binary processing on the received signal;

[0040] The host computer is used to generate a pressure cloud map after executing the bicubic interpolation method and the least squares area method.

[0041] In a preferred embodiment of the present disclosure, in each ionic polymer metal composite (IPMC) packaging structure in the ionic polymer metal composite (IPMC) sensing array, the ionic polymer metal composite (IPMC) is encapsulated in a polydimethylsiloxane (PDMS) film.

[0042] In a preferred embodiment of the present disclosure, the sensor conditioning circuit is externally mounted on the 3D printed substrate.

[0043] In summary, compared with the prior art, the present disclosure provides a 3D printing substrate calibration method and system based on an IPMC sensing array, which has the following beneficial technical effects: by using a sensing array based on an ionic polymer metal composite material (IPMC) material, and utilizing the flexibility and piezoelectric properties of the polymer metal composite material (IPMC) material, accurate calibration is provided for the 3D printing substrate to improve printing quality and efficiency. A new array of flexible piezoelectric pressure sensors is used for mesh bed detection to calibrate the 3D printing substrate, to provide pressure detection with higher sensitivity and faster response speed, to effectively solve the problem of decreased accuracy of existing sensors due to temperature drift during long-term use.

[0044] Through the above technical scheme, the substrate calibration method disclosed in the present invention provides an efficient, low-cost, and easy-to-operate solution for the calibration of the printing substrate of a 3D printer, which has significant technological advancement and market application potential.

[0045] Further areas of applicability will become apparent from the description provided herein.It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.

[0046] The above features and advantages and other features and advantages of the presently disclosed systems and methods are apparent from the detailed description, including the claims and exemplary embodiments, when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The following is a brief introduction to the drawings required for use in the specific implementation or description of the prior art.

[0048] Obviously, some drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] In order to more clearly illustrate the specific implementation manner of the present disclosure or the technical solutions in the prior art, more details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0050] Figure 1 is a schematic diagram of a packaging structure of a flexible sensor in a specific embodiment of the present disclosure;

[0051] Figure 2 is a schematic diagram of a flexible sensor integrated with a printed substrate in a specific embodiment of the present disclosure;

[0052] Figure 3 is a physical picture of a flexible sensor integrated with a printed substrate in a specific embodiment of the present disclosure;

[0053] Figure 4 is a system structure block diagram in a specific embodiment of the present disclosure;

[0054] Figure 5 is a reconstructed substrate detection map in a specific embodiment of the present disclosure;

[0055] Figure 6 is a pressure cloud diagram in a specific embodiment of the present disclosure;

[0056] Figure 7 is a schematic diagram of an external sensor in a specific embodiment of the present disclosure;

[0057] Figure 8 It is a comparison diagram of printing effects in a specific embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the present disclosure is further described in detail below in conjunction with specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details will be set forth with reference to several examples of the present disclosure illustrated in the accompanying drawings to facilitate a full understanding of the present disclosure. Whenever possible, the same or similar reference numerals are used in the drawings and the description to represent the same or similar components or steps.

[0060] However, the present disclosure may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present disclosure is not limited by the specific embodiments disclosed below.

[0061] Figure 1 is a schematic diagram of a packaging structure of a flexible sensor in a specific embodiment of the present disclosure.

[0062] Flexible sensor design: The new flexible piezoelectric sensor disclosed in the present invention adopts an ionic polymer metal composite material (IPMC) as the sensitive element of the sensor, and is encapsulated by encapsulating the ionic polymer metal composite material (IPMC) 202 in a polydimethylsiloxane (PDMS) film 201 and leading out an electrode 203.

[0063] like Figure 1 As shown, the novel flexible piezoelectric sensor 102 is embedded in the aluminum substrate 101 by bonding, welding or mechanical fixing. Figure 1 As shown, a printing platform heating pad 103 is disposed on an aluminum substrate 101 to provide temperature control; a printing platform polyetherimide (PEI) board 104 is disposed on the aluminum substrate 101. The polyetherimide (PEI) board is suitable as a substrate or covering layer due to its high temperature resistance, chemical resistance and electrical insulation properties, and is used in conjunction with the flexible piezoelectric sensor 102 to improve the overall performance and stability of the sensor.

[0064] Figure 2 is a schematic diagram of a flexible sensor integrated with a printed substrate in a specific embodiment of the present disclosure.

[0065] A specific embodiment of the present disclosure designs a flexible piezoelectric sensor based on an ionic polymer metal composite material (IPMC). Figure 2The flexible piezoelectric sensor shown is of ionic polymer metal composite material (IPMC): the ionic polymer metal composite material (IPMC) is constructed as a flexible piezoelectric sensor integrally with the printed substrate.

[0066] like Figure 2 As shown, polydimethylsiloxane (PDMS) 201 is printed, an ionic polymer metal composite material (IPMC) 202 is encapsulated in the polydimethylsiloxane (PDMS) 201, and an electrode 203 is led out, and a sensor conditioning circuit is integrated in an external expansion module to realize algorithm update iteration.

[0067] In order to overcome the shortcomings of the prior art, the present disclosure introduces an innovative solution, using a flexible piezoelectric sensor ionic polymer metal composite (IPMC). The present disclosure proposes a 3D printing substrate calibration method and system based on an ionic polymer metal composite (IPMC) sensing array, which can effectively solve the impact of temperature changes on sensor sensitivity and improve the use efficiency and accuracy of multiple print heads.

[0068] The preparation process of the flexible sensor integrated with the printed substrate is as follows:

[0069] Prepare a polydimethylsiloxane (PDMS) mixed liquid and mix it evenly using a mechanical stirrer;

[0070] and evacuating the polydimethylsiloxane (PDMS) mixed liquid to extract bubbles from the polydimethylsiloxane (PDMS) mixed liquid to eliminate bubbles generated during the mixing process;

[0071] Then, the vacuumized polydimethylsiloxane (PDMS) mixed liquid is applied on the surface of the ionic polymer metal composite material (IPMC);

[0072] Electrodes are connected to both ends of an ionic polymer metal composite material (IPMC) coated with a polydimethylsiloxane (PDMS) mixed liquid on the surface, and the electrodes are led out;

[0073] Then put it in a vacuum and let it stand for 2 hours to accelerate the curing process. The polydimethylsiloxane (PDMS) will solidify and adhere to the ionic polymer metal composite material (IPMC).

[0074] In the step of preparing the polydimethylsiloxane (PDMS) mixed liquid, polydimethylsiloxane (PDMS) A solution and polydimethylsiloxane (PDMS) B solution are mixed to form a polydimethylsiloxane (PDMS) mixed liquid. Polydimethylsiloxane (PDMS) A solution and polydimethylsiloxane (PDMS) B solution are mixed in a ratio of 10:1 by volume or mass. By accurately controlling and optimizing the component ratio of solution A and solution B, it can be ensured that PDMS has good mechanical properties and precision, and a uniform PDMS three-dimensional network structure is formed.

[0075] Among them, the components of polydimethylsiloxane (PDMS) A solution are mainly poly-dimethyl-methyl-vinyl-siloxane prepolymer and a trace amount of platinum catalyst to accelerate the curing reaction; the components of polydimethylsiloxane (PDMS) B solution are prepolymer with vinyl side chains and cross-linking agent: poly-dimethyl-methyl-hydrogeno-siloxane, which are used to form the three-dimensional network structure of PDMS.

[0076] In a preferred embodiment of the present disclosure, a 3D printing substrate calibration method based on an ionic polymer metal composite (IPMC) sensing array further includes the following steps before step S300 mentioned below:

[0077] S100: encapsulating the ionic polymer metal composite material (IPMC) in a polydimethylsiloxane (PDMS) film and leading out electrodes to form an ionic polymer metal composite material (IPMC) encapsulation structure;

[0078] S200: Embed a plurality of ionic polymer metal composite (IPMC) packaging structures in a 3D printed substrate to form an ionic polymer metal composite (IPMC) sensing array.

[0079] In a preferred embodiment of the present disclosure, in step S100, the following steps are also included:

[0080] S120: preparing a polydimethylsiloxane (PDMS) mixed liquid, and mixing it evenly using a mechanical stirrer;

[0081] S140: vacuuming the polydimethylsiloxane (PDMS) mixed liquid to eliminate bubbles generated during the mixing process;

[0082] S160: applying the vacuumed polydimethylsiloxane (PDMS) mixed liquid onto the surface of the ionic polymer metal composite material (IPMC);

[0083] S180: Connecting electrodes to both ends of the ionic polymer metal composite (IPMC) sensing array and leading out the electrodes;

[0084] S190: Curing the polydimethylsiloxane (PDMS) film to form a packaging structure.

[0085] In a preferred embodiment of the present disclosure, in step S120, the following steps are also included:

[0086] A polydimethylsiloxane (PDMS) A solution and a polydimethylsiloxane (PDMS) B solution are mixed in a mass ratio of 10:1, wherein the polydimethylsiloxane (PDMS) A solution includes a poly-dimethyl-methyl-vinyl-siloxane prepolymer and a trace amount of platinum catalyst, and the polydimethylsiloxane (PDMS) B solution includes a prepolymer with a vinyl side chain and a crosslinking agent.

[0087] This solution mainly provides a simple, low-cost, high-replication accuracy PDMS mold-turning process to address the deficiencies in the prior art. The prepolymer with vinyl side chains in the PDMS B solution provides active points that can be cross-linked, while the cross-linking agent provides additional connection points, allowing PDMS to be transformed from a liquid state to an elastic solid state. After mixing, the prepolymer with vinyl side chains and the cross-linking agent form a stable three-dimensional network structure through a hydrosilylation reaction (also known as a hydrosilylation reaction). This hydrosilylation reaction is carried out under the action of a platinum catalyst in the PDMS A solution and is a key step in the PDMS curing process. By changing the ratio of the vinyl side chain prepolymer and the cross-linking agent in the PDMS B solution, the mechanical properties of the final PDMS material, such as hardness, elasticity, and stretchability, can be adjusted.

[0088] In the field of electroactive material packaging technology, the present disclosure adopts a sandwich structure of "polydimethylsiloxane (PDMS)"-"ionic polymer metal composite material (IPMC)"-"polydimethylsiloxane (PDMS)". Because the sensitivity and other properties of the ionic polymer metal composite material (IPMC) mainly depend on the water molecule content inside the IPMC material. The present disclosure wraps the ionic polymer metal composite material (IPMC) with polydimethylsiloxane (PDMS), which greatly reduces the water loss of the ionic polymer metal composite material (IPMC). The life and sensitivity of the sensor are improved.

[0089] Figure 3 is a physical picture of a flexible sensor integrated with a printed substrate in a specific embodiment of the present disclosure.

[0090] Figure 3 is based on Figure 2In a specific embodiment of the present disclosure, in the structure of the schematic diagram, Figure 3 In the sensor shown: a 1 cm2 piece of ionic polymer metal composite (IPMC) is used as a flexible piezoelectric sensor, which is integrated with the printed substrate - two 2 cm2 pieces of polydimethylsiloxane (PDMS) and an integrated circuit.

[0091] Why is the polydimethylsiloxane (PDMS) 2 square centimeters in this specific embodiment of the present disclosure? This size is mainly to completely wrap the 1 square centimeter ion polymer metal composite material (IPMC) to prevent the IPMC from losing water and losing its sensing performance.

[0092] IPMC and PDMS can take other sizes and shapes: the sizes of 1 square centimeter and 2 square centimeters mentioned above can be changed, and the shape can also be changed according to actual conditions. Whether to change and how to change depends on how the slicing is performed.

[0093] Among them, the integrated circuit (IC) uses a specific processing technology and follows certain circuit interconnection to integrate the active and passive devices such as transistors, capacitors, resistors, etc. required in a circuit on a small semiconductor chip and install it in a tube shell, becoming a microstructure that can perform specific circuit or system functions.

[0094] Figure 4 It is a system structure block diagram in a specific embodiment of the present disclosure.

[0095] like Figure 4 The 3D printing substrate calibration system based on the ionic polymer metal composite material (IPMC) sensing array described in the present disclosure is shown, which is mainly composed of four parts: sensor element, sensor conditioning circuit, lower computer and upper computer. Figure 4 The substrate has sensor elements made of IPMC materials locally at various coordinate points. Figure 4 There is a sensor conditioning circuit in it, which amplifies the signal collected from the sensor element and sends it to the MCU lower computer. Figure 4 There is an MCU (microcontroller) lower computer, which binarizes the received signal (when the appropriate pressure is reached, it is fed back as "1", otherwise it is "0") and sends it to the Kipper upper computer. Figure 4 There is a klipper host computer in the binary signal, which processes the binary signal by bicubic interpolation and least squares area method to generate the following Figure 6 Pressure contour diagram shown.

[0096] The present disclosure provides a 3D printing substrate calibration system based on an ionic polymer metal composite (IPMC) sensing array, comprising:

[0097] The sensor element includes an ionic polymer metal composite (IPMC) sensing array embedded in a 3D printed substrate;

[0098] A sensor conditioning circuit for amplifying the signal collected by the sensor element;

[0099] MCU lower computer, used to perform binary processing on the received signal;

[0100] The host computer is used to generate a pressure cloud map after executing the bicubic interpolation method and the least squares area method.

[0101] In a preferred embodiment of the present disclosure, in each ionic polymer metal composite (IPMC) packaging structure in the ionic polymer metal composite (IPMC) sensing array, the ionic polymer metal composite (IPMC) is encapsulated in a polydimethylsiloxane (PDMS) film.

[0102] In a preferred embodiment of the present disclosure, the sensor conditioning circuit is externally mounted on the 3D printed substrate.

[0103] The 3D printed substrate calibration system based on the ionic polymer metal composite (IPMC) sensing array performs the following calibration steps:

[0104] First, step S240: roughly leveling;

[0105] Then step S280: adjust the Z limit:

[0106] Then, step S300: based on the offset value of each point on the substrate on the z-axis, the mesh bed is reconstructed using a bicubic interpolation method;

[0107] Then step S400: using the least squares area method to predict the flatness of the mesh bed to minimize the total error.

[0108] Step S240: Rough leveling:

[0109] The core calibration mechanism for print bed (substrate) leveling is the "paper jam test". It involves placing a piece of regular printing paper between the print bed (substrate) and the nozzle, then controlling the nozzle to different Z heights until you feel the right amount of resistance when moving the paper back and forth.

[0110] In order to ensure the effectiveness of the print bed (substrate), calibration is often required. The calibration mechanism of the print bed (substrate) also relies on the "paper jam test". Before the subsequent steps performed by the machine begin, the "paper jam test" is required to manually calibrate once.

[0111] To perform the paper jam test, start with a random piece of A4 paper. The first step of the "paper jam test" is to check the printer's nozzle and print bed (i.e., substrate). Make sure that the printer's nozzle and print bed (i.e., substrate) are basically free of plastic (or other debris). If there is plastic remaining on the nozzle, you need to heat the extruder head first and remove the remaining plastic with metal tweezers. Wait until the extruder head has completely cooled to room temperature before continuing with the "paper jam test". While the nozzle is cooling, use metal tweezers to remove any plastic that may have leaked out. Only perform the "paper jam test" when both the nozzle and print bed (i.e., substrate) are at room temperature, pull the A4 paper back and forth on the print bed (i.e., substrate) and adjust the nut; until you feel resistance during the pull process.

[0112] The "paper jam test" is performed at the four corners of the print bed (i.e., substrate) respectively. Its main purpose is to pave the way for the Z-axis limit calibration of the S280.

[0113] Step S280: Adjust the Z limit: call the Z limit calibration command to calibrate the Z limit.

[0114] The S280 continues to use the "paper jam test" method; however, when the command is called, the print head returns to the default Z-axis "zero point" at the basic midpoint, and the 3D printer's control platform calibrates the Z-axis when the operator feels resistance when pulling with A4 paper.

[0115] In a specific embodiment of the present disclosure, when a command is called, the command may be a G code command or a macro command. The klipper system used can set and define its commands by itself. The G code naming can be

[0116] G0 XnnnYnnn Znnn Ennn Fnnn Snnn

[0117] Among them, Xnnn, Ynnn, Znnn, Ennn represent the moving positions of X, Y, Z, and E respectively. Fnnn represents the moving speed. Snnn represents the limit switch.

[0118] Figure 5 It is a reconstructed substrate detection map in a specific embodiment of the present disclosure.

[0119] Step S300: Based on the offset values ​​of each point on the substrate on the z-axis, the mesh bed is reconstructed using the bicubic interpolation method:

[0120] After the previous two steps S240 and S280 are performed in sequence, S300 is performed: mesh bed reconstruction: based on the offset value of each point on the substrate on the z-axis, the bicubic interpolation method is used to reconstruct the composition of the mesh bed.

[0121] Step S310: Setting the basic zero point:

[0122] The calibration system is mainly achieved by setting a basic "zero point", for example, on a 120mm×120mm rectangular substrate (print bed), (60mm, 60mm) can be set as the starting point of the 3D printer.

[0123] When the print head touches the point, the signal obtained by the sensor is amplified by the sensor conditioning circuit and fed back to the lower computer MCU. The lower computer MCU performs binary processing on the collected signal. When the appropriate pressure is reached, it is fed back as "1", otherwise it is "0".

[0124] At the same time, the relative zero position of the Z axis is also set at (X, Y, 0). The values ​​of the X coordinate and the Y coordinate are within the range of 0-120mm.

[0125] Step S320: The piezoelectric pressure sensor detects the substrate through the nozzle of the printer and records the offset value of each point on the substrate on the z-axis.

[0126] In a specific embodiment, when all three axes (X axis, Y axis, Z axis) of the 3D printer are reset to zero, the printer can perform detection, and the detection sequence starts from (0,0) and ends at (120,120).

[0127] In a specific embodiment, in order to reconstruct the substrate, the detection sequence of each point on the substrate is zigzag.

[0128] In a specific embodiment, in order to reconstruct the substrate, the detection sequence of each point on the substrate is S-shaped.

[0129] In a specific embodiment, in order to reconstruct the substrate, the detection sequence of each point on the substrate is spiral.

[0130] In a specific embodiment, in order to prevent the print head from failing to detect the point to be detected, the position of the point to be detected is first expanded inward by 10 mm. The starting point becomes (10,10) and the end point becomes (110,110). Scan point by point as follows Figure 5 As shown in the reconstructed substrate detection image, the relative z-axis offset of each point on the substrate is recorded.

[0131] In a specific embodiment, in order to reconstruct the substrate, the detection order of each point on the substrate is S-shaped. Such a path is shorter than other paths, which can detect the entire image and save time. Figure 5This path has been presented, and the detection order of each point on the substrate is first from coordinate (10,10) to coordinate (10,35) to coordinate (10,60) to coordinate (10,85) to coordinate (10,110), then to coordinate (60,110) to coordinate (60,85) to coordinate (60,60) to coordinate (60,35) to coordinate (60,10); then to coordinate (110,10) to coordinate (110,35) to coordinate (110,60) to coordinate (110,85) to coordinate (110,110). This path will be shorter than other paths, which can detect the entire map and save time.

[0132] According to the detection sequence, the heights of the above coordinate points are detected and recorded, that is, the relative z-axis offset of the above coordinate points on the substrate, and then the flatness of the substrate can be understood as a whole.

[0133] Step S330: For the offset values ​​of each point on the substrate on the z-axis stored in step S320, the mesh bed (platform) of the 3D printing substrate is first reconstructed (composition reconstruction) using the bicubic interpolation method. The entire mesh bed is accurately reconstructed using the known z-axis offset values ​​of several points on the substrate.

[0134] In step S330, the following steps are also included:

[0135] Step S332: Determine the nearest known point near the point to be interpolated (the point to be determined for the z-axis offset value):

[0136] The z-axis offset values ​​of several points on the substrate are recorded in step S320. These points are selected as the basis for interpolation, usually the corners and center points of the substrate, as well as other key positions.

[0137] In a specific embodiment, for any point to be interpolated (the point for which the z-axis offset value is to be determined) (x, y) on the mesh bed, find the four known nearest points (x0, y0, z0), (x0, y1, z1), (x1, y0, z2) and (x1, y1, z3) around it. Given the four nearest points (x0, y0, z0), (x0, y1, z1), (x1, y0, z2) and (x1, y1, z3), a local area is formed for interpolating the point (x, y), where (x0, y0) is the point closest to the point to be interpolated (x, y).

[0138] Step S334: Weight calculation:

[0139] For each nearest point (x0,y0,z0), (x0,y1,z1), (x1,y0,z2), and (x1,y1,z3), calculate its horizontal distance and vertical distance to the point to be interpolated (x,y).

[0140] Using these horizontal and vertical distances, calculate the horizontal interpolation weight a and vertical interpolation weight b of the nearest point;

[0141] a=(x-x0) / (x1-x0)

[0142] b=(y-y0) / (y1-y0)

[0143] Wherein, x0 and x1 represent the coordinate values ​​on the X axis of the substrate, y0 and y1 represent the coordinate values ​​on the Y axis of the substrate, a represents the interpolation weight in the horizontal direction, and b represents the interpolation weight in the vertical direction.

[0144] Step S336: Bicubic interpolation calculation:

[0145] Using the horizontal interpolation weight a and vertical interpolation weight b of the nearest point, based on the bicubic interpolation method, calculate the z-axis offset Z of the nearest points (x0,y0,z0), (x0,y1,z1), (x1,y0,z2) and (x1,y1,z3) for the point to be interpolated (x,y).

[0146] Z=(1-a)(1-b)*z0+a)1-b)*z1+(1-a)b / z2+ab*z3

[0147] Among them, Z is the z-axis offset value of the interpolation point (nearest point) (x0, y0, z0), (x0, y1, z1), (x1, y0, z2) and (x1, y1, z3) for the point to be interpolated (x, y).

[0148] z0, z1, z2, and z3 represent the calculated interpolation results, that is, the pixel values ​​at the given coordinates (x, y).

[0149] Step S338: Reconstruct the precise shape of the entire net bed:

[0150] By repeating the above steps, we can get the z-axis offset value Z of each point (x, y) on the entire mesh bed, thereby reconstructing the precise shape of the entire mesh bed. Effectively utilizing the data of the known points on the substrate, the entire mesh bed is accurately reconstructed through bicubic interpolation, thereby improving the quality and efficiency of 3D printing. This helps calibrate the 3D printing substrate to ensure flatness and accuracy during printing.

[0151] Step S400: Use the least squares area method to predict the flatness of the net bed and minimize the total error: Reconstruct the net bed obtained by the bicubic interpolation method, and then use the least squares area method to predict the generated data.

[0152] In 3D printing mesh bed calibration, the least squares method is used to determine the best plane fit to ensure the flatness and position accuracy of the printed substrate. The least squares method can provide a best fit plane that is closest to all measurement points, thereby ensuring the flatness and position accuracy of the substrate during the 3D printing process. The advantage of the least squares method is that it provides an objective, mathematically optimal solution that can process a large number of data points and find the best fit model.

[0153] The specific steps are as follows:

[0154] Measure the actual observed coordinates (x i ,y i ,z i );

[0155] Assume that the model of the best-fit plane Z of the substrate is the function:

[0156]

[0157] This function can describe the independent variable x i and i With dependent variable , where α and β are model parameters.

[0158] Step S410: Calculate the actual observed coordinates (x i ,y i ,z i ) to the vertical distance d from the fitting plane Z i :

[0159]

[0160] For each data point, the actual observed coordinates (x i ,y i ,z i ), calculate the predicted value of the fitted plane model and the actual observed value z i The difference (i.e., error) between them is specifically manifested in calculating the actual observed coordinates (x i ,y i ,z i ) to the predicted value on the fitting plane Z The vertical distance d i .

[0161] Step S420: All vertical distances d i The sum of squares is defined as the total error E, and the values ​​of parameters α, β, and γ in the fitting plane Z = αx + βy + γ are calculated;

[0162] Based on the actual observed coordinates (xi ,y i ,z i ) to the vertical distance d from the fitting plane i , all data points (x i ,y i ,z i ) to the vertical distance d from the fitting plane i The sum of squares is defined as the total error E:

[0163]

[0164] Where N represents the total number of all data points.

[0165] Based on the total error E, the values ​​of the parameters α, β, and γ in the fitting plane Z=αx+βy+γ are calculated.

[0166]

[0167]

[0168] Step S430: Adjust the calibration parameter values ​​α, β, γ by solving the optimal parameters α, β, γ using mathematical methods (such as matrix operations) to minimize the total error E (i.e., the sum of the squares of the vertical distances from all data points to the fitting plane). Adjust the values ​​of the parameters α, β, γ in the fitting plane Z=αx+βy+γ so that the fitting plane best matches the actual observed coordinates of each data point, thereby minimizing the total error E to ensure the flatness and position accuracy of the 3D printed substrate.

[0169] Among them, N represents the total number of points; α is the slope of the fitting plane, which represents the influence of the independent variable x on the dependent variable z; β is the slope of the fitting plane, which represents the influence of the independent variable x on the dependent variable z; γ is the intercept of the fitting plane, which represents the value of the dependent variable z when all independent variables are zero.

[0170] In a specific embodiment, the minimum value of the total error E is found by calculating the partial derivative of the total error E with respect to each parameter α, β, γ and setting it to zero. Solving the above derivative equation, find the parameter values ​​α, β, γ that minimize the total error E. These parameter values ​​α, β, γ are the best fitting parameters.

[0171] The least squares area method is used to fit the distribution of data points. By adjusting the parameters α, β, and γ, the fitting plane can be made to best fit the data points, thereby minimizing the total error E, and then obtaining Figure 6 The pressure contour diagram in a specific embodiment of the present disclosure is shown. The goal of the least squares area method is to find a plane to minimize the sum of the squares of the vertical distances of the data points to the plane, thereby fitting the distribution of the data points, which is used in the calibration of the 3D printing mesh bed to ensure the flatness and position accuracy of the printed substrate. Figure 6 It is a pressure cloud diagram in a specific embodiment of the present disclosure.

[0172] Figure 7 Schematic diagram of external placement of sensors in a specific embodiment of the present disclosure. Ionic polymer metal composite (IPMC) sensing array: Figure 7 As shown in the figure, an ionic polymer metal composite (IPMC) sensing array is used to provide real-time feedback of pressure distribution, and five packaged piezoelectric pressure sensors are placed at the four corners and the center of the substrate to fully capture pressure information. This helps to achieve more accurate reconstruction Figure 6 Pressure cloud map.

[0173] The conditioning circuit is externally placed to prevent the temperature rise of the print bed (substrate) from interfering with the collected data, and provides power supply and signal amplification functions, thereby enhancing the functionality and flexibility of the sensor system.

[0174] Figure 8 It is a comparison diagram of printing effects in a specific embodiment of the present disclosure.

[0175] Updated Iterative Algorithm: An updated iterative algorithm has been introduced, based on the bicubic interpolation and Lagrangian methods that come with the mainstream Marlin and Klipper firmware. This algorithm combines bicubic interpolation and the least squares area method to create probe pressure distribution maps and evaluate the impact of long-term continuous heating of the substrate during use.

[0176] We can get Figure 8 The printed effect comparison chart. Figure 8 The left picture is obtained by using Figure 7 The printing effect after the pressure cloud map is calibrated. The bottom surface of the left picture is flat and uniform. Figure 8 The right picture is not obtained through Figure 7 The printing effect of the pressure cloud map calibration. The bottom surface of the right picture is uneven and the printing fails.

[0177] The technical solution of this disclosure:

[0178] The print bed (substrate) is leveled.

[0179] The IPMC (ion polymer metal composite) sensing array is used to provide real-time feedback of pressure distribution.

[0180] Five packaged piezoelectric pressure sensors are placed at the four corners and the center of the reference plate to fully capture pressure information.

[0181] This helps to achieve a more accurate reconstruction of the pressure contour.

[0182] The temperature rise of the print bed (substrate) interferes with the collected data. During the printing process, different materials will use different printing temperatures, the nozzle has different temperatures, and the print bed (substrate), also known as the hot bed, also has a corresponding temperature. Otherwise, it will cause the edge to warp during printing, resulting in printing failure, and in severe cases, fire.

[0183] The conditioning circuit is external to prevent the temperature rise of the print bed (substrate) from interfering with the collected data, providing power supply and signal amplification functions, and enhancing the function and flexibility of the sensor system. The conditioning circuit is a small signal amplification circuit, because the ipmc touch generates a micro voltage signal, which needs to be amplified. In order to prevent the amplification circuit from being affected by temperature drift, the conditioning circuit is external to reduce errors. Improve efficiency and reduce printing failure rate.

[0184] The algorithm is updated and the net bed is reconstructed point by point into the net bed surface through the double triple interpolation method.

[0185] The reconstructed mesh bed is a mathematical model reconstructed in geometric space after probing the substrate.

[0186] According to the deduction of time, the printing platform is deformed by temperature.

[0187] The predictions for the net bed have been modified by building the model through the least squares area method.

[0188] The least squares area method is used to predict the flatness of the substrate, which successfully improves the printing accuracy and reduces the frequent leveling operations, thus saving time. Manual calibration is no longer necessary.

[0189] Long-term heating impact assessment: Continuously heat the print bed (substrate) and record pressure data.

[0190] By comparing the pressure distribution diagrams at different time points, the changes in the substrate during long-term continuous heating use can be evaluated. Based on the evaluation results, the printing parameters can be adjusted or other measures can be taken to improve printing accuracy and efficiency, such as adjusting the print head height, optimizing heating control, etc.

[0191] In summary, the present disclosure provides an innovative system and method based on flexible piezoelectric sensor ionic polymer metal composite (IPMC) and ionic polymer metal composite (IPMC) sensing array to address the challenges of 3D printing substrate calibration.

[0192] This system and method combines new sensor design, efficient data acquisition and processing, and optimized calibration algorithms to improve the performance, accuracy, and efficiency of the printer. Through the above technical solutions, the substrate calibration method disclosed in the present invention provides an efficient, low-cost, and easy-to-operate solution for 3D printing substrate calibration of 3D printers, which has significant technical advancement and market application potential.

[0193] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present disclosure. Various modifications to the above embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the above embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0195] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

[0196] The above description is merely illustrative and is in no way intended to limit the present disclosure, its application or use. The broad teachings of the present disclosure can be implemented in many forms. Therefore, although the present disclosure includes specific examples, the scope of the present disclosure should not be limited thereto, as other modifications will become apparent after studying the drawings, the specification and the appended claims.

Claims

1. A 3D printing substrate calibration method based on an ionic polymer metal composite (IPMC) sensing array, characterized in that: The method comprises the following steps: S300: Based on the offset value of each point on the substrate on the z-axis, the mesh bed is reconstructed using a bicubic interpolation method; S400: Use the least squares area method to predict the flatness of the mesh bed and minimize the total error.

2. The method according to claim 1, characterized in that: Step S400 also includes the following steps: S410: Calculate the vertical distance from the actual observed coordinates of each data point to the fitting plane: S420: defining the sum of squares of all vertical distances as the total error, and calculating the parameter value in the fitting plane; S430: Minimize the total error by adjusting the calibration parameter value.

3. The method according to claim 1, characterized in that Step S300 also includes the following steps: S320: Record the offset value of each point on the substrate on the z-axis; S330: Reconstruct the mesh bed using a bicubic interpolation method.

4. The method according to claim 3, characterized in that Step S330 also includes the following steps: S332: Determine the nearest point near the point to be interpolated: S334: Calculate the horizontal interpolation weight and the vertical interpolation weight of the nearest point using the horizontal distance and the vertical distance from the nearest point to the point to be interpolated; S336: using the horizontal interpolation weight and the vertical interpolation weight of the nearest point, based on the bicubic interpolation method, calculate the z-axis offset value of the nearest point for the point to be interpolated; S338: Reconstruct the precise shape of the entire mesh bed based on the z-axis offset value of the nearest point to the point to be interpolated.

5. The method according to claim 1, characterized in that Before step S300, the method further includes the following steps: S100: encapsulating the ionic polymer metal composite material (IPMC) in a polydimethylsiloxane (PDMS) film and leading out electrodes to form an ionic polymer metal composite material (IPMC) encapsulation structure; S200: Embed a plurality of ionic polymer metal composite (IPMC) packaging structures in a 3D printed substrate to form an ionic polymer metal composite (IPMC) sensing array.

6. The method according to claim 5, characterized in that In step S100, the following steps are also included: S120: preparing a polydimethylsiloxane (PDMS) mixed liquid, and mixing it evenly using a mechanical stirrer; S140: vacuuming the polydimethylsiloxane (PDMS) mixed liquid to eliminate bubbles generated during the mixing process; S160: applying the vacuumed polydimethylsiloxane (PDMS) mixed liquid onto the surface of the ionic polymer metal composite material (IPMC); S180: Connecting electrodes to both ends of the ionic polymer metal composite (IPMC) sensing array and leading out the electrodes; S190: Curing the polydimethylsiloxane (PDMS) film to form a packaging structure.

7. The method according to claim 6, characterized in that In step S120, the following steps are also included: A polydimethylsiloxane (PDMS) A solution and a polydimethylsiloxane (PDMS) B solution are mixed in a mass ratio of 10:1, wherein the polydimethylsiloxane (PDMS) A solution includes a poly-dimethyl-methyl-vinyl-siloxane prepolymer and a trace amount of platinum catalyst, and the polydimethylsiloxane (PDMS) B solution includes a prepolymer with a vinyl side chain and a crosslinking agent.

8. A 3D printing substrate calibration system based on an ionic polymer metal composite (IPMC) sensing array, characterized in that: include: The sensor element includes an ionic polymer metal composite (IPMC) sensing array embedded in a 3D printed substrate; A sensor conditioning circuit for amplifying the signal collected by the sensor element; MCU lower computer, used to perform binary processing on the received signal; The host computer is used to generate a pressure cloud map after executing the bicubic interpolation method and the least squares area method.

9. The 3D printing substrate calibration system according to claim 8, characterized in that: In each ionic polymer metal composite (IPMC) encapsulation structure in the ionic polymer metal composite (IPMC) sensing array, the ionic polymer metal composite (IPMC) is encapsulated in a polydimethylsiloxane (PDMS) film.

10. The 3D printing substrate calibration system according to claim 8, characterized in that: The sensor conditioning circuit is externally mounted on the 3D printed substrate.

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