TWI885665B - Strength prediction method for laser bonded composite materials - Google Patents

Abstract

A strength prediction method for laser bonded composite materials includes the following steps: establishing an initial geometric model that includes an initial solid geometric model and an initial surface geometric model in contact with each other; receiving metal material information, non-metal material information and a plurality of layer formation parameters; setting material property parameters of the initial solid geometric model according to the metal material information to generate a solid model; generating a layer model according to the non-metal material information and a layer thickness and a layer number that are included in the plurality of layer formation parameters; setting material property parameters of the initial surface geometric model according to the layer model to generate a surface model; setting connection between the solid model and the surface model as a laser combination to generate a composite structural model; and performing a tensile test simulation to the composite structural model to obtain a simulation result.

Description

Strength prediction method for laser bonding of composite materials

The present invention relates to a strength prediction method, and in particular to a strength prediction method for laser bonding of composite materials.

Most machine tools are made of metal. In order to reduce energy consumption during operation and thus lower carbon emissions, some developers have tried to manufacture machine tools by combining metal with non-metallic composite materials.

However, when designing machine tools, the strength of composite materials combining metal and non-metal is not clearly understood, which may lead to the design and manufacture of machine tools that are not strong enough and are easily damaged, or are too strong and difficult to reduce in weight, resulting in poor energy consumption reduction effect.

The present invention provides a strength prediction method which can simulate the bonding strength of metal and non-metal composite materials to facilitate the design of machine tools using the composite materials.

The strength prediction method for composite material laser bonding disclosed in one embodiment of the present invention is executed by a computing device. The strength prediction method for composite material laser bonding includes the following steps: establishing an initial geometric model, wherein the initial geometric model includes an initial solid geometric model and an initial surface geometric model that are in contact with each other; receiving a metal material information, a non-metal material information and a plurality of layer formation parameters; according to the metal material information, setting the material property parameters of the initial solid geometric model to generate a solid model; according to the non-metal material information, The invention relates to a method for preparing a composite structure model of a composite structure comprising: obtaining a composite structure model by using material information and layer formation parameters, wherein the layer formation parameters include at least one layer thickness and a layer number; setting material property parameters of an initial surface geometry model according to the layer model to generate a surface model; setting a connection between the solid model and the surface model as laser bonding to generate a composite structure model; and performing a tensile test simulation on the composite structure model to obtain a simulation result.

According to the strength prediction method of composite laser bonding disclosed in the above embodiment, by establishing an initial geometric model including an initial solid geometric model and an initial surface geometric model, a tensile test simulation can be performed on a composite structure model of metal and non-metal laser bonding. Compared with the prior art, the method of the present invention can be used to know the bonding strength of metal and non-metal composite materials in the design stage, which is conducive to manufacturing products with appropriate strength and avoiding insufficient or excessive strength design.

The above description of the content of the present invention and the following description of the implementation method are used to demonstrate and explain the principle of the present invention and provide a further explanation of the scope of the patent application of the present invention.

The detailed features and advantages of the present invention are described in detail in the following embodiments, and the contents are sufficient to enable anyone familiar with the relevant technology to understand the technical content of the present invention and implement it accordingly. According to the contents disclosed in this specification, the scope of the patent application and the drawings, anyone familiar with the relevant technology can easily understand the relevant purposes and effects of the present invention. The following embodiments further illustrate the viewpoints of the present invention in detail, but do not limit the scope of the present invention by any viewpoint.

Please refer to Figures 1 to 8, wherein Figures 1 to 4 are flow charts of a method for predicting the intensity of composite material laser bonding according to an embodiment of the present invention, and Figures 5 to 8 are schematic diagrams of a method for predicting the intensity of composite material laser bonding according to an embodiment of the present invention.

As shown in FIG. 1 , the strength prediction method for laser bonding of composite materials may include step S101: establishing an initial geometric model; step S102: dividing the initial solid geometric model and the initial surface geometric model into multiple elements; step S103: receiving metal material information, non-metal material information and layer formation parameters; step S104: setting the material property parameters of the initial solid geometric model according to the metal material information to generate a solid model ; Step S105: Establish a layer model based on non-metallic material information and layer formation parameters; Step S106: Set material property parameters of the initial surface geometry model based on the layer model to generate a surface model; Step S107: Set the connection between the solid model and the surface model to laser bonding to generate a composite structure model; and Step S108: Perform a tensile test simulation on the composite structure model based on the finite element method to obtain a simulation result.

As shown in FIG. 2 , step S105 may include step S1051: establishing an initial layer corresponding to the number of layers according to non-metallic material information, layer thickness and layer number; step S1052: receiving a coordinate system corresponding to the number of layers; and step S1053: stacking the initial layers along a stacking direction according to the coordinate system to establish a layer model.

As shown in FIG. 3 , step S107 may include step S1071: setting an interface node in the connection area between the solid model and the surface model; step S1072: setting an interface element connecting the solid model and the surface model; and step S1073: setting the bonding strength between the solid model and the surface model with laser power to generate a composite structure model.

As shown in FIG. 4 , the strength prediction method of laser bonding of composite materials may further include step S201: receiving a feedback signal in response to a simulation result; step S202: adjusting the laser power of the laser bonding between the solid model and the surface model according to the feedback signal to generate an updated composite structure model; and step S203: performing a tensile test simulation on the updated composite structure model to obtain an updated simulation result.

The intensity prediction method for composite material laser bonding shown in FIGS. 1 to 4 will be exemplarily described below using a computing device such as a central processing unit of a computer, and please refer to FIGS. 5 to 8 for the corresponding descriptions.

In step S101, as shown in FIG5, the computing device establishes an initial geometric model 10. The initial geometric model 10 includes an initial solid geometric model 11 and an initial surface geometric model 12 that are in contact with each other. The initial solid geometric model 11 is, for example, a component with a three-dimensional size and a certain degree of thickness. Please note that the initial solid geometric model 11 shown in a block shape in FIG5 is only an example; in some embodiments, the initial solid geometric model can also be any shape, and the present invention is not limited thereto. The initial surface geometric model 12 is, for example, a component with a three-dimensional size and a very small thickness. Please note that due to the extremely small thickness of the initial surface geometric model 12, it is only exemplarily shown as a flat sheet in FIG. 5, but the present invention is not limited thereto; in some embodiments, the initial surface geometric model can also present a curved shape in three-dimensional space. The above-mentioned contact between the initial solid geometric model 11 and the initial surface geometric model 12 refers to the initial solid geometric model 11 and the initial surface geometric model 12 lightly abutting against each other without any connection relationship involving a connecting force.

In step S102, as shown in FIG6 , the computing device divides the initial solid geometry model 11 and the initial surface geometry model 12 into a plurality of elements. In FIG6 , the initial solid geometry model 11 is divided into a plurality of hexahedral elements, and the initial surface geometry model 12 is divided into a plurality of quadrilateral elements, but the present invention is not limited thereto. In some embodiments, the initial solid geometry model may also be divided into a plurality of any polyhedral elements. In other embodiments, the initial surface geometry model may also be divided into a plurality of any polygonal elements. In addition, please note that after the initial surface geometry model 12 is divided into a plurality of elements, the initial surface geometry model 12 is divided into an upper surface layer and a lower surface layer to facilitate calculation using a plurality of nodes, so in order to make the element division more visual, the initial surface geometry model 12 is shown as having a thickness in FIG. 6 , but in fact, the initial surface geometry model 12 is still maintained as an element with a very small thickness. The above can also be understood as step S102 does not change the thickness of the initial surface geometry model 12.

In step S103, the computing device receives a metal material information, a non-metal material information and a plurality of layer formation parameters. The metal material information, the non-metal material information and the layer formation parameters can be inputted into the computer by a user through an input device such as a mouse or a keyboard of a computer and then received by the computing device. The layer formation parameters can include the thickness of the layer to be formed (corresponding to the initial layer described below) and the number of the layer to be formed (corresponding to the initial layer described below).

In step S104, the computing device can set the material property parameters of the initial solid geometric model 11 according to the received metal material information through the corresponding metal material property parameters pre-stored in a storage device such as a computer hard disk to generate a solid model 13, as shown in FIG7. For example, if the user inputs the metal material information as iron in step S103, the computing device can find the pre-stored iron-related property parameters in the hard disk, such as Young's modulus and Pusson's ratio, and apply these iron-related property parameters to the initial solid geometric model 11 to generate a solid model 13. However, the present invention is not limited thereto. In some embodiments, the metal material information input by the user may be, for example, the Young's modulus and the Pusson's ratio of the metal (eg, iron), and the computing device may directly set the material property parameters of the initial solid geometric model using the received Young's modulus and the Pusson's ratio of the metal.

In step S105, the computing device establishes a layer model according to the received non-metallic material information and layer formation parameters.

Specifically, step S105 may include step S1051 to step S1053. In step S1051, the computing device creates an initial layer corresponding to the number of layers according to the received non-metallic material information and the layer thickness and number of layers included in the layer formation parameters. Please note that the number of layers can be a positive integer such as 1, 2, 3, etc., and the number of initial layers can be 1 layer, 2 layers, 3 layers, etc., respectively. In addition, the single layer thickness of the initial layer can correspond to one of the layer thicknesses. For example, the layer thickness and the number of layers included in the layer formation parameters are, for example, 0.09 mm and 4, and the computing device can create 4 initial layers with a thickness of 0.09 mm. In another example, the layer thicknesses are, for example, 0.09 mm, 0.08 mm, 0.09 mm, and 0.08 mm, and the number of layers is, for example, 4, and the computing device can create 4 initial layers with thicknesses of 0.09 mm, 0.08 mm, 0.09 mm, and 0.08 mm, respectively. In addition, the material property parameters of the initial layer can correspond to non-metallic material information and can be obtained through the corresponding non-metallic material property parameters pre-stored in a storage device such as a hard disk of a computer. For example, if the user inputs the non-metallic material information as carbon fiber in step S103, the computing device can find the pre-stored carbon fiber-related characteristic parameters in the hard disk, such as the fiber angle and its corresponding Young's modulus and Pusson's ratio, and apply these carbon fiber-related characteristic parameters to the initial layer when establishing the initial layer. However, the present invention is not limited to this. In some embodiments, the non-metallic material information input by the user can be, for example, the fiber angle of the non-metal (such as carbon fiber) and its corresponding Young's modulus and Pusson's ratio, and the computing device can directly use the received fiber angle, Young's modulus and Pusson's ratio of the non-metal to establish the material characteristic parameters of the initial layer. Please note that when there are multiple initial layers, these initial layers can be built simultaneously or in batches, and the present invention is not limited thereto. In some embodiments, the non-metallic material can also be epoxy resin.

In step S1052, the computing device receives coordinate systems corresponding to the number of layers. For example, if the number of layers is 4, the computing device may receive four coordinate systems corresponding to the four initial layers respectively.

In step S1053, the computing device stacks the initial layers corresponding to the number of layers along the stacking direction according to the coordinate system corresponding to the number of layers to establish a layer model. For example, please refer to FIG. 9, which is a schematic diagram of stacking initial layers according to an embodiment of the present invention for predicting the strength of composite material laser bonding. In FIG. 9 , the stacking direction is, for example, the positive Z-axis direction of the global system shown in FIG. 9 , and the positive X-axis directions of the four coordinate systems are, for example, 45 degrees, 135 degrees, 135 degrees, and 45 degrees different from the positive X-axis direction of the global system, respectively. Then, the computing device will, for example, stack the corresponding four initial layers 161, 162, 163, and 164 in sequence along the positive Z-axis direction at 45 degrees, 135 degrees, 135 degrees, and 45 degrees relative to the global system, respectively, to form a layer model 16. In Figure 9, four initial layers 161, 162, 163, and 164 are, for example, carbon fiber materials, and the directions in which the fibers 161a, 162a, 163a, and 164a extend along their respective fiber angles are, for example, parallel to the X-axis of their respective coordinate systems. When stacking the initial layers 161, 162, 163, and 164, the initial layers 161, 162, 163, and 164 are stacked along the positive Z-axis direction with 45 degrees, 135 degrees, 135 degrees, and 45 degrees as the fiber angles of the fibers 161a, 162a, 163a, and 164a relative to the X-axis of the overall system. Please note that the present invention is not limited to stacking initial layers at an angle relative to an overall system; in some embodiments, initial layers may also be stacked at a relative angle relative to adjacent initial layers; in some embodiments, the coordinate systems corresponding to the initial layers may all be aligned with the coordinate system of the overall system (which may also be understood as the positive X-axis directions of each coordinate system are the same), and the fiber angles of each initial layer are displayed by the extension direction of each fiber. Please note that the angle between the extension direction of each fiber 161a, 162a, 163a, 164a and the X-axis of the corresponding coordinate system is not used to limit the present invention. In some embodiments, the fiber extension direction of a single initial layer can be at any angle to the X of the corresponding coordinate system. In addition, the fiber angles of the stacked initial layers can be different. Taking Figure 9 as an example, the fiber angle of the fiber 161a of the initial layer 161 is 90 degrees different from the fiber angle of the fiber 162a of the initial layer 162, while the fiber angle of the fiber 161a of the initial layer 161 is the same as the fiber angle of the fiber 164a of the initial layer 164.

In step S106, the computing device sets the material property parameters of the initial surface geometry model 12 according to the layer model established in step S105 to generate a surface model 14, as shown in FIG7. Please note that the layer model 16 in FIG9 can be one of the layer models used in step S106, and the present invention is not limited thereto. Please note that the layer model in step S105 will have different strength-related parameters depending on the number of stacked layers and the thickness of a single initial layer (for example, the more stacked layers there are or the thicker the stack is, the greater the strength-related parameters of the layer model), and step S106 sets the strength-related parameters displayed by the stacked layer model in step S105 as the material property parameters of the initial surface geometry model 12. The strength-related parameters do not include the thickness of the layer model, so the surface model 14 generated in step S106 still maintains an extremely small thickness; the thickness of the surface model 14 presented in Figure 7 is as described above, and is also shown in Figure 7 as having a thick appearance due to the visualization of the element division.

In step S107 , the computing device sets the connection between the solid model 13 and the surface model 14 to be laser-bonded to generate a composite structure model 20 .

Specifically, step S107 may include step S1071 to step S1073. In step S1071, as shown in FIG7 and FIG8, the computing device sets a plurality of interface nodes 18 in the connection area between the solid model 13 and the surface model 14, wherein two adjacent interface nodes 18 are arranged with a certain distance between them. As shown in the partially enlarged FIG8 , the number of interface nodes 18 is, for example, 72, and they are arranged at equal intervals in a connection area of 25 mm×25 mm with a fixed value of, for example, 2 to 2.5 mm as the spacing. The array formed by the arrangement of these interface nodes 18 has, for example, 12 and 6 interface nodes 18 distributed in the range of 25 mm×12.5 mm at the long side and short side, respectively, and the array formed by the arrangement of these interface nodes 18 is, for example, 6.25 mm from the boundary of the connection area in the short side direction. Please note that the spacing of the interface nodes is not intended to limit the present invention. In some embodiments, the interface nodes may also be arranged at spacings outside the above ranges or at unequal spacings.

In step S1072, the computing device sets an interface element connecting the solid model 13 and the surface model 14, wherein the interface element is, for example, a spring, so as to utilize the characteristics of the spring to perform connection simulation. Please note that the type of the interface element is not intended to limit the present invention, and other types of interface elements may also be set.

In step S1073, the computing device sets the bonding strength between the solid model 13 and the surface model 14 with a laser power to generate a composite structure model 20. Thus, the computing device completes the composite structure model 20 of metal and non-metal bonded by laser.

Since the computing device has divided the initial geometric model 10 into a plurality of elements in step S102, the computing device can perform a tensile test simulation on the composite structure model 20 based on the finite element method to obtain a simulation result in step S108. The simulation result can be, for example, referred to FIG. 10 , which is a graph of simulation results of a composite material laser bonding strength prediction method according to an embodiment of the present invention. As shown at point AA in FIG. 10 , the composite structure model 20 can withstand a maximum tensile stress of 3.6 Newtons/square millimeter (N/mm ² ), and the maximum elongation can be 8.4%. Please note that the simulation result shown in FIG. 10 may be an average simulation result of multiple tensile test simulations on the same composite structure model 20 , or may be a simulation result of a single tensile test simulation, but the present invention is not limited thereto.

Then, the computing device may display the simulation result through a display device such as a computer screen. The user may observe whether the simulation result meets the requirements through the display device. If the user determines that the simulation result does not meet the requirements, for example, if the user wants to reduce the slope of the line in Figure 10, a feedback signal in response to the above-mentioned simulation result may be input through the input device, and the above-mentioned feedback signal is received by the computing device in step S201.

In step S202, the computing device may adjust the laser power of the laser combination between the solid model 13 and the surface model 14 according to the aforementioned feedback signal to generate an updated composite structure model 22. For example, in order to reduce the slope of the line of the simulation result of FIG. 10, the computing device may adjust the laser power to 0.9 times the original value without changing other settings related to the interface nodes or interface elements. Please note that the computing device may also adjust the settings related to one or more of the laser power, interface nodes, and interface elements, and the present invention is not limited thereto.

In step S203, the computing device performs a tensile test simulation on the updated composite structure model 22 to obtain an updated simulation result. The updated simulation result can be, for example, referred to FIG. 11, which is a graph of the updated simulation result of the composite material laser bonding strength prediction method according to one embodiment of the present invention. As shown at point BB in FIG. 11, the updated composite structure model 22 can withstand a maximum tensile stress of 3.75 Newtons/square millimeter (N/ ^mm2 ), and the maximum elongation can be 9.0%. Please note that the updated simulation result shown in FIG. 11 can be the average updated simulation result of multiple tensile test simulations performed on the same updated composite structure model 22, or can be the updated simulation result of a single tensile test simulation, and the present invention is not limited thereto.

Similarly, the user can observe whether the updated simulation result meets the requirements through the display device, and can provide a feedback signal again to execute steps S201 to S203 again. Please note that the judgment of whether the simulation result or the updated simulation result meets the requirements can also be performed by a judgment device and the feedback signal can be selectively issued accordingly, and the present invention is not limited to this.

Please note that the order of the above steps is not intended to limit the present invention. In some embodiments, step S105 may be performed first and then step S104. In other embodiments, steps S103 to S105 may be performed first and then steps S101 to S102. In other embodiments, steps S101 to S102 and steps S103 to S105 may be performed simultaneously.

According to the strength prediction method of composite laser bonding of the above embodiment, by establishing an initial geometric model including an initial solid geometric model and an initial surface geometric model, a tensile test simulation can be performed on a composite structure model of metal and non-metal laser bonding. Compared with the prior art, the method of the present invention can be used to know the bonding strength of metal and non-metal composite materials in the design stage, which is conducive to manufacturing products with appropriate strength and avoiding insufficient or excessive strength design.

Although the present invention is disclosed as above with the aforementioned embodiments, they are not used to limit the present invention. Anyone skilled in similar techniques may make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of patent protection of the present invention shall be subject to the scope of the patent application attached to this specification.

10: Initial geometry model 11: Initial solid geometry model 12: Initial surface geometry model 13: Solid model 14: Surface model 16: Layer model 161, 162, 163, 164: Initial layer 161a, 162a, 163a, 164a: Fiber 18: Interconnection node 20: Composite structure model 22: Update composite structure model AA, BB: Points S101~S108, S1051~S1053, S1071~S1073, S201~S203: Steps X, Y, Z: Axes

Figures 1 to 4 are flow charts of a composite material laser bonding intensity prediction method according to an embodiment of the present invention. Figures 5 to 8 are schematic diagrams of a composite material laser bonding intensity prediction method according to an embodiment of the present invention. Figure 9 is a schematic diagram of stacking initial layers of a composite material laser bonding intensity prediction method according to an embodiment of the present invention. Figure 10 is a graph of simulation results of a composite material laser bonding intensity prediction method according to an embodiment of the present invention. Figure 11 is a graph of updated simulation results of a composite material laser bonding intensity prediction method according to an embodiment of the present invention.

S101~S108: Steps

Claims (10)

A strength prediction method for laser bonding of composite materials is executed by a computing device, and the strength prediction method for laser bonding of composite materials includes: establishing an initial geometric model, wherein the initial geometric model includes an initial solid geometric model and an initial surface geometric model that are in contact with each other; receiving metal material information, non-metal material information and a plurality of layer formation parameters; and setting the material characteristic parameters of the initial solid geometric model according to the metal material information to generate a A solid model is provided; a layer model is established according to the non-metallic material information and the layer formation parameters, wherein the layer formation parameters include at least a layer thickness and a layer number; according to the layer model, the material property parameters of the initial surface geometry model are set to generate a surface model; the connection between the solid model and the surface model is set to be laser combined to generate a composite structure model; and a tensile test simulation is performed on the composite structure model to obtain a simulation result. The strength prediction method for composite material laser bonding as described in claim 1 further includes: dividing the initial solid geometric model and the initial surface geometric model into multiple elements respectively. A method for predicting the strength of composite laser bonding as described in claim 1, wherein the material property parameters set to the initial solid geometric model include Young's modulus and Pusson's ratio. A method for predicting the strength of composite laser bonding as described in claim 1, wherein the non-metallic material information is carbon fiber, and the material property parameters set to the initial surface geometric model include fiber angle, Young's modulus and Poisson's ratio. A method for predicting the strength of composite material laser bonding as described in claim 4, wherein establishing the layer model based on the non-metallic material information and the layer formation parameters includes: establishing at least one initial layer corresponding to the number of layers based on the non-metallic material information, the thickness of the at least one layer and the number of layers, wherein the material property parameters of the at least one initial layer correspond to the non-metallic material information, and the single-layer thickness of the at least one initial layer corresponds to one of the thicknesses of the at least one layer; receiving at least one coordinate system corresponding to the number of layers; and stacking the at least one initial layer along a stacking direction according to the at least one coordinate system to establish the layer model. A method for predicting the strength of composite material laser bonding as described in claim 5, wherein the number of the at least one initial layer is two or more, and at least two of the initial layers have different fiber angles after stacking. A method for predicting the strength of laser bonding of composite materials as described in claim 1, wherein setting the connection between the solid model and the surface model as laser bonding to generate the composite structure model includes: setting a plurality of interface nodes in the connection area between the solid model and the surface model, wherein two adjacent interface nodes are arranged with a distance maintained; setting an interface element connecting the solid model and the surface model; and setting the bonding strength between the solid model and the surface model with a laser power to generate the composite structure model. A method for predicting the strength of composite material laser bonding as described in claim 7, wherein the interfacing element is a spring. The strength prediction method of composite material laser bonding as described in claim 1 further includes: receiving a feedback signal in response to the simulation result; adjusting a laser power of the laser bonding between the solid model and the surface model according to the feedback signal to generate an updated composite structure model; and executing the tensile test simulation on the updated composite structure model to obtain an updated simulation result; wherein the updated simulation result includes the tensile stress and elongation of the updated composite structure model. The strength prediction method for laser bonding of composite materials as described in claim 1, wherein performing the tensile test simulation on the composite structure model to obtain the simulation result includes: performing the tensile test simulation on the composite structure model based on the finite element method to obtain the simulation result.

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