Method and system for estimating the surface properties of adhesive surfaces of resin-metal composites

Abstract

This method facilitates the estimation of the surface properties of the adhesive surface of metal components in resin-metal composites. [Solution] The method for estimating the surface properties of an adhesive surface of a resin-metal composite according to the embodiment is a method for estimating the surface properties of an adhesive surface of a resin-metal composite having a metal member with an adhesive surface and a resin member to which the adhesive surface is bonded, and includes measuring at least one surface property from line roughness parameters, surface roughness parameters, or a contour diagram showing height information of the entire adhesive surface for a metal member sample with a roughened adhesive surface, measuring the adhesive strength between the metal member sample and the resin member sample, constructing a database of surface properties and adhesive strength under specific conditions, and estimating the surface properties of the adhesive surface corresponding to a predetermined adhesive strength based on the database.

Description

[Technical Field]

[0001] Embodiments of the present invention relate to a method and system for estimating the surface properties of an adhesive surface of a resin-metal composite. [Background technology]

[0002] The technology to integrate metal and resin materials to form resin-metal composites is in demand from all parts and component manufacturers, including those for aircraft, automobiles, and industrial equipment, and many bonding technologies have been developed. The bonding mechanism between metal and resin materials is a complex interplay of three types of interactions: mechanical, chemical, and physical. Mechanical interactions occur when the resin penetrates and solidifies into the irregularities of the adherend surface (anchoring effect). Chemical interactions occur through interatomic and intermolecular interactions (covalent bonds and hydrogen bonds) between the adhesive and adherend. Physical interactions occur through attractive forces (van der Waals forces) between molecules constituting the adhesive and adherend surfaces. To improve the bonding strength between metal and resin materials, it is necessary to effectively induce these interactions.

[0003] Generally, chemical and physical interactions are considered the primary cause of adhesive strength. Plasma treatment of the surface of solid components, such as metals or inorganic materials, is one method that effectively induces chemical bonding. Plasma treatment activates the solid surface, allowing the adhesive component containing acrylic polymer compounds to form chemical bonds with the solid surface, thereby improving adhesive strength. Furthermore, if the adhesive strength due to chemical and physical interactions is weak, interfacial fracture can easily occur. In such cases, it is possible to increase adhesive strength by roughening the bonding surface of the adherends to induce mechanical interactions, such as increasing the bonding area or changing surface property parameters such as arithmetic mean roughness (Ra). However, since adhesive strength is determined by a complex interplay of mechanical, chemical, and physical interactions, it was difficult to understand how the composition and surface properties of the bonding surface relate to it and which parameters contribute to improving adhesive strength. Therefore, adhesive strength testing was necessary, and each time, it was time-consuming to delaminate the resin-metal composite at the interface and prepare a sample. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] International Publication No. 2008 / 149745 brochure [Patent Document 2] Japanese Patent Publication No. 2018-111788 [Patent Document 3] Japanese Patent Application Publication No. 10-294024 [Patent Document 4] Japanese Patent Publication No. 2014-18995 [Patent Document 5] Patent No. 6784756 [Patent Document 6] Japanese Patent Publication No. 2021-156618 [Patent Document 7] International Publication No. 2023 / 219082 brochure [Non-patent literature]

[0005] [Non-Patent Document 1] Fujishima et al., "The effect of alumina sandblasting on the adhesion between titanium and hard resin," JSDMD, 1997, Vol. 16, No. 2. [Non-Patent Document 2] Murata et al., "Shear Strength of Adhesives to Blasted Titanium," Japanese Journal of Oral Implantology, 2003, vol. 16, no. 3. [Non-Patent Document 3] Yoshida et al., "Strength Evaluation of Adhesive Joints Using Three-Dimensional Surface Property Parameters," Journal of the Adhesion Society of Japan, 2013, vol. 49, no. 6. [Overview of the project] [Problems that the invention aims to solve]

[0006] Embodiments of the present invention aim to facilitate the estimation of the surface properties of the adhesive surface of a metal component in a resin-metal composite. [Means for solving the problem]

[0007] According to one embodiment, a method for estimating the surface properties of an adhesive surface of a resin-metal composite having a metal member with an adhesive surface and a resin member to which the adhesive surface is bonded, For a metal component sample having the same composition as the above-mentioned metal component and having the above-mentioned bonding surface roughened, at least one surface characteristic from among the line roughness parameter, surface roughness parameter, or contour diagram showing the height information of the entire bonding surface is measured. Using a resin-metal composite sample prepared by bonding a resin sample having the same composition as the resin sample to the above-mentioned metal sample, the adhesive strength between the metal sample and the resin sample was measured. A database of the above surface properties and adhesive strength was constructed under specific conditions. Based on the above database, a method for estimating the surface properties of an adhesive surface of a resin-metal composite can be provided, which includes estimating the surface properties of the adhesive surface corresponding to a predetermined adhesive strength. [Brief explanation of the drawing]

[0008] [Figure 1] This is a block diagram representing a system for estimating the surface properties of the adhesive surface of a resin-metal composite according to the second embodiment. [Figure 2] This is a flowchart illustrating a method for estimating the surface properties of the adhesive surface of a resin-metal composite according to the first embodiment. [Figure 3] This is a cross-sectional view showing an example of the structure of a resin-metal composite used in the embodiment. [Figure 4] This figure shows examples of cross-sectional shapes and surface property parameters for metal components. [Figure 5] This is a schematic cross-sectional view showing another example of the structure of the resin-metal composite used in the embodiment. [Figure 6] It is a graph showing an example of the relative ratio of the adhesive strength of a resin-metal composite depending on the presence or absence of a filler. [Figure 7] It is a cross-sectional view schematically showing an example of the fracture state of the resin-metal composite of FIG. 5. [Figure 8] It is a diagram showing the state of a tensile adhesion strength test. [Figure 9] It is a graph showing the relationship between the arithmetic mean roughness (Ra) and the adhesive strength as surface property parameters. [Figure 10] It is a graph showing the relationship between the root mean square height (Rdq) and the adhesive strength. [Figure 11] It is a graph showing the relationship between the surface area (S / A) and the adhesive strength. [Figure 12] It is a graph showing the relationship between the result of adding the arithmetic mean roughness (Ra) and the root mean square height (Rdq) and the adhesive strength. [Figure 13] It is a graph showing the relationship between the result of subtracting the root mean square height (Rdq) from the arithmetic mean roughness (Ra) and the adhesive strength. [Figure 14] It is a graph showing the relationship between the result of multiplying the arithmetic mean roughness (Ra) and the root mean square height (Rdq) and the adhesive strength. [Figure 15] It is a graph showing the relationship between the result of dividing the arithmetic mean roughness (Ra) by the root mean square height (Rdq) and the adhesive strength. [Figure 16] It is a contour diagram of the adhesive surface of a test piece obtained non-contact. [Figure 17] It is a contour diagram of the adhesive surface of a test piece obtained non-contact. [Figure 18] It is a contour diagram of the adhesive surface of a test piece obtained non-contact. [Figure 19] It is a diagram showing the persistent homology obtained by image analysis of the contour diagram of FIG. 18.

Embodiments for Carrying Out the Invention

[0009] The method for estimating the surface properties of an adhesive surface of a resin-metal composite according to the first embodiment estimates the surface properties of an adhesive surface in a resin-metal composite having a metal member with an adhesive surface and a resin member bonded to the adhesive surface. First, for a metal member sample having the same composition as the metal member and with a roughened adhesive surface, at least one surface property is measured from line roughness parameters, surface roughness parameters, or contour diagrams showing the height information of the entire adhesive surface. Next, the adhesive strength between the metal member sample and the resin member sample is measured using a resin-metal composite sample created by bonding a resin member sample having the same composition as the resin member to the adhesive surface of the metal member sample. Subsequently, a database of surface properties and adhesive strength is constructed under specific conditions, and then, based on the information from the database, the surface properties of the adhesive surface corresponding to the adhesive strength are estimated.

[0010] The system for estimating the surface properties of the adhesive surface of a resin-metal composite according to the second embodiment is an example of a system for implementing the method for estimating the surface properties of the adhesive surface of a resin-metal composite according to the first embodiment, and is a system for estimating the surface properties of the adhesive surface in a resin-metal composite having a metal member with an adhesive surface and a resin member to which the adhesive surface is bonded. This resin-metal composite surface surface texture estimation system includes a surface texture measurement unit that measures at least one surface texture from among linear roughness parameters, surface roughness parameters, or contour diagrams showing overall surface height information for multiple metal member samples having the same composition as the metal members and with roughened surface textures on the adhesive surface, An adhesive strength measuring unit measures the adhesive strength between a metal component sample and a resin component sample, using a resin-metal composite sample prepared by bonding a metal component sample and a resin component sample having a similar composition to the resin component. A storage unit that stores a database constructed under specific conditions based on surface properties and adhesive strength, It includes an estimation unit that estimates the surface properties corresponding to a predetermined adhesive strength of a resin-metal composite based on a database.

[0011] To confirm the effectiveness of roughening the bonding surface, the inventors conducted adhesive strength tests using roughened and untreated samples. They found that the adhesive strength improved for the roughened samples, while the adhesive strength was lower for the untreated samples, indicating a tendency towards weaker adhesion. It is thought that roughening the bonding surface imparts mechanical interactions to the adhesion process, in addition to chemical and physical interactions. In addition to arithmetic mean roughness (Ra), there are many other surface property parameters used to evaluate roughened adhesive surfaces, such as linear roughness parameters like the average length of the irregularities (Rsm) and the slope of the irregularities (Rdq), and surface roughness parameters like arithmetic mean surface roughness (Sa) and peak density (Sds). It was unclear which of these parameters contributed to improving adhesive strength.

[0012] According to the first and second embodiments, by measuring surface properties and adhesive strength and constructing a database, it is possible to estimate the surface properties of the adhesive surface corresponding to the adhesive strength based on the database. It is also possible to estimate the adhesive strength corresponding to the surface properties based on the database. Furthermore, according to the first and second embodiments, since the surface properties of the adhesive surface can be estimated without conducting an adhesive strength test, there is no need to prepare a sample for the adhesive strength test, and the estimation of surface properties can be easily performed. Moreover, according to the first and second embodiments, by estimating the surface properties of the adhesive surface of the resin-metal composite, it is possible to obtain a resin-metal composite with good adhesive strength between the metal member and the resin member.

[0013] The embodiments will be described below with reference to the drawings. Furthermore, the disclosure is merely an example, and any modifications that can be easily conceived by a person skilled in the art while maintaining the spirit of the invention are naturally included within the scope of the present invention. In addition, the drawings may schematically represent the width, thickness, shape, etc. of each part in order to clarify the explanation, but these are merely examples and do not limit the interpretation of the present invention. In addition, in this specification and each drawing, elements similar to those described above in previously shown drawings are denoted by the same reference numerals, and detailed explanations may be omitted as appropriate.

[0014] Figure 1 shows a block diagram representing a system for estimating the surface properties of the adhesive surface of a resin-metal composite according to the second embodiment. This system 41 is a system for estimating the surface properties of an adhesive surface 12 in a resin-metal composite 1 having a metal member 2 with an adhesive surface 12 and a resin member 3 bonded to the adhesive surface 12, and has a main control unit 42. The main control unit 42 is connected to a surface properties measurement unit 47, an adhesive strength measurement unit 48, a storage unit 44, and an estimation unit 46. The surface texture measurement unit 47 measures at least one surface texture from line roughness parameters, surface roughness parameters, or contour diagrams showing height information of the entire adhesive surface for multiple metal member samples having the same composition as the metal member 2 and having a roughened adhesive surface 12. The adhesive strength measurement unit 48 measures the adhesive strength between a metal member sample and a resin member sample using a resin-metal composite sample created by bonding a metal member sample to a resin member sample having the same composition as the resin member. The storage unit 44 stores a database 45 constructed under specific conditions based on surface texture and adhesive strength. The estimation unit 46 estimates the surface texture corresponding to a predetermined adhesive strength of the resin-metal composite based on the database 45. If necessary, the estimation unit 46 can also estimate the adhesive strength corresponding to a predetermined surface texture of the resin-metal composite based on the database 45. The database 45 can be constructed using at least one method from computer science, machine learning, or image analysis.

[0015] Figure 2 shows a flowchart illustrating a method for estimating the surface properties of the adhesive surface of a resin-metal composite according to the first embodiment. Figure 2 is an example of a method for implementing the system shown in Figure 1. First, a metal component sample having the same composition as the metal component and with a roughened bonding surface is prepared, and the surface properties measurement unit 47 measures at least one surface property of this metal component sample, which is either a line roughness parameter, a surface roughness parameter, or a contour diagram showing the height information of the entire bonding surface (ST1). Next, a resin-metal composite sample is prepared by bonding a resin component sample having the same composition as the resin component to the bonding surface of the metal component sample, and the bonding strength of this resin-metal composite sample between the metal component sample and the resin component sample is measured in the bonding strength measurement unit 48 (ST2). Subsequently, a database of surface properties and bonding strength is constructed under specific conditions (ST3) and stored in the storage unit 44. After that, the estimation unit 46 estimates the surface properties of the bonding surface corresponding to the bonding strength based on the database 45 (ST4). In this way, the surface properties of the bonding surface of the resin-metal composite can be estimated.

[0016] Figure 3 shows a cross-sectional view illustrating an example of the structure of a resin-metal composite used in the embodiment. The resin-metal composite 1 used in this embodiment is a composite formed by joining or bonding two different materials, and comprises a metal member 2 and a resin member 3 joined to the metal member 2. In this embodiment, the metal member 2 and the resin member 3 are in contact. The surface 12 of the metal member 2 to which the resin member 3 is joined is provided with a plurality of irregularities by roughening the surface. Figure 4 shows an example of the cross-sectional shape and surface property parameters of the metal member 2. As shown in the figure, there are multiple surface property parameters that quantify the irregularities formed on the roughened surface 12. In the figure, Sdq represents the root mean square slope, Sa represents the arithmetic mean height, Sz represents the maximum height, and Sku represents the curtsis (sharpness), all of which are surface roughness parameters. Also, Rsm represents the average length of the contour curve elements, Ra represents the arithmetic surface height, Rz represents the maximum height, and Rku represents the curtsis (sharpness), all of which are line roughness parameters.

[0017] Linear roughness parameters of surface texture usable in the embodiment include: arithmetic mean height Ra(Pa,Wa), maximum height Rz(Pz,Wz), ten-point mean roughness RzJIS, maximum peak height Rp(Pp,Wp), maximum valley depth Rv(Pv,Wv), average height of contour curve elements Rc(Pc,Wc), maximum cross-sectional height Rt(Pt,Wt), root mean square height Rq(Pq,Wq), skewness Rsk(Psk,Wsk), kurtosis Rku(Pku,Wku), and average length of contour curve elements RSm( PSm,WSm), root mean square slope RΔq(PΔq,WΔq), load length ratio Rmr(c)(Pmr(c),Wmr(c)), cutting level difference Rδc(Pδc,Wδc), relative load length ratio Rmr(Pmr,Wmr), load length ratio Mr1 separating the protruding peak and core, load length ratio Mr2 separating the protruding valley and core, core level difference Rk, protruding peak height Rpk, protruding valley depth Rvk, cross-sectional area A1 of the protruding peak, cross-sectional area A2 of the protruding valley, high spot count HSC, peak count / cm The parameters can be Pc / cm, peak count RPc(PPc,WPc), or expanded length RLo(PLo,WLo), expanded length ratio Rlr(Plr,Wlr), arithmetic mean slope angle RΔa(PΔa,WΔa), arithmetic mean wavelength Rλa(Pλa,Wλa), root mean square slope angle RΔq(PΔq,WΔq), root mean square wavelength Rλq(Pλq,Wλq), evaluation length, reference length, or number of reference lengths.

[0018] Surface roughness parameters that can be used in the embodiment include arithmetic mean height Sa, maximum height Sz, surface texture aspect ratio Str, arithmetic mean curvature of peaks Spc, interface development area ratio Sdr, root mean square height Sq, skewness Ssk, kurtosis Sku, maximum peak height Sp, maximum valley depth Sv, minimum autocorrelation length Sal, surface texture direction Std, root mean square slope Sdq, peak density Spd, core level difference Sk, protruding peak height Spk, protruding valley depth Svk, load area ratio Smr1 separating protruding peaks and core, load area ratio Smr2 separating protruding valleys and core, pole height Sxp, valley void volume Vvv, core void volume Vvc, peak volume Vmp, core volume Vmc, or measurement area (region area).

[0019] Examples of resin components 3 include thermosetting resins such as epoxy resin, phenolic resin, cyanate ester resin, or polyurethane resin. Such thermosetting resins can be used individually or in combination of two or more. For example, epoxy resin can be used. As the epoxy resin, it is possible to use an epoxy resin having two or more epoxy groups per molecule. Examples of such epoxy resins include bisphenol A type epoxy resin, bisphenol F type epoxy resin, novolac type epoxy resin, or alicyclic epoxy resin. These epoxy resins can be used individually or as a mixture of two or more types.

[0020] Furthermore, the resin member 3 can be formed by applying a solution of a resin material such as epoxy resin onto a metal member, for example, using a mold or a coating method such as dip coating or spin coating, and then curing it. The epoxy resin can be cured with an epoxy resin curing agent. The type of epoxy resin curing agent is not particularly limited as long as it can chemically react with the epoxy resin and cure it, and examples include amine-based curing agents, acid anhydride-based curing agents, and imidazole-based curing agents. Any metal can be used as metal component 2. For example, at least one of iron and copper can be used. Specifically, examples include copper material C1100 and stainless steel material SUS303.

[0021] The surface properties of the metal component 2 can be adjusted by roughening the adhesive surface. For roughening the adhesive surface 12, for example, sandblasting, shot blasting, chemical etching, laser processing, or additive manufacturing can be used. To measure the surface properties of the adhesive surface 12 of the metal member 2, either a contact-type or non-contact-type measuring device can be used. Since the surface properties obtained will differ depending on the method and the device used, the same method and device can be used when measuring surface properties. Contact-type measuring devices can primarily acquire only line roughness parameters. Non-contact measuring devices can acquire surface roughness parameters and contour maps. Since adhesion occurs on the surfaces of the components, a non-contact method can be used to acquire surface roughness parameters. For example, a non-contact optical measuring device such as the Keyence VR-6000 one-shot 3D shape measuring machine can be used.

[0022] Furthermore, fillers can be dispersed in the resin component 3. Figure 5 shows a schematic cross-sectional view illustrating another example of the structure of the resin-metal composite used in the embodiment. As shown in the figure, this resin-metal composite 15 has the same configuration as the resin-metal composite 1 in Figure 3, except that it has a resin member 23 having dispersed filler 4 instead of resin member 3. Because the filler 4 is dispersed within the resin member 23, its mechanical strength is increased, resulting in good adhesive strength between the metal member 2 and the resin member 23.

[0023] Figure 6 shows a graph illustrating an example of the relative ratio of adhesive strength of resin-metal composites with and without fillers. As shown in the figure, the relative strength ratio 102 of the adhesive strength of the resin-metal composite using a resin component with a filler is approximately 1.2 compared to the adhesive strength 101 of the resin-metal composite using a resin component without a filler, indicating that the adhesive strength is improved when a filler is present. For example, spherical bodies having an average particle size of 1 μm to 30 μm can be used as filler 4. Here, the average particle size refers to the median value. Such filler 4 is easily dispersed inside the resin member 23, making it possible to further increase the mechanical strength.

[0024] As filler 4, inorganic fillers such as silica or alumina can be used, and by dispersing the inorganic filler, it is possible to reduce the coefficient of thermal expansion of the resin member 23. As a result, the difference between the coefficient of thermal expansion of the resin member 23 and the coefficient of thermal expansion of the metal member 2 in Figure 5 can be reduced more than the difference (mismatch) between the coefficient of thermal expansion of the resin member 3 and the metal member 2 in Figure 3. Furthermore, one or more types of filler 4 can be used, and fillers with multiple particle size distributions can be combined. By filling with filler 4 with multiple particle size distributions, close packing is possible, and the adhesive strength between the metal member 2 and the resin member 23 can be improved. In addition, since filler 4 is filled into the recesses 5 on the surface 12 of the metal member 2, if a crack occurs on the surface 12 of the metal member 2, crack propagation can be suppressed.

[0025] Figure 7 shows a schematic cross-sectional view illustrating an example of the fracture of the resin-metal composite shown in Figure 5. Figure 7 shows the resin-metal composite 15 after a tensile load is applied to the resin member 23 in Figure 5, causing it to fracture, and the fractured portion of the resin member 23 is removed. As shown in the figure, when the resin-metal composite 15 is fractured, the multiple fillers 4 suppress crack propagation, so the base material of the resin member 23 can fracture via the fracture surface 11 that is formed between the dispersed multiple fillers 4. The fracture surface 11 has irregularities corresponding to the surface shape of the multiple fillers 4, and near the recess 5, it occurs near the entrance portion 5a, allowing the protrusion 25 of the resin member provided in the recess 5 to separate from the resin member 23. In areas other than the recess 5, the fracture surface 11 can also be formed near the surface 12 of the metal member 2. Thus, according to another example of the resin-metal composite according to the second embodiment, by dispersing a plurality of fillers 4 throughout the resin member 23, including the protrusions 13 provided in the recesses 5 of the metal member 2, it is possible to suppress the propagation of cracks on the surface of the metal member 2 and improve the adhesive strength.

[0026] Examples The embodiments will be described in more detail below, with reference to examples. (Preparation of metal component samples) A copper material C1100 with dimensions of 12.7 mm in length, 12.7 mm in width, and 38 mm in height was prepared as the metal substrate. The bonding surface of the copper material was roughened by sandblasting, and multiple metal component samples with diverse surface properties were prepared by setting several processing conditions. The surface properties of each adhesive surface were acquired non-contact using a one-shot 3D shape measuring machine (VR-6000) manufactured by Keyence Corporation. (Preparation of resin component samples) The resin material was prepared by mixing 100 parts by weight of bisphenol A type epoxy resin, which is the main component, with 90 parts by weight of an acid anhydride-based curing agent, which is the curing agent.

[0027] (Preparation of resin-metal composite samples) A resin-metal composite sample was fabricated as follows by applying the prepared resin sample onto a roughened copper metal sample and curing it to bond and form a resin component. First, one of the prepared metal component samples (first copper body) was fixed in a mold, and an appropriate amount of resin component sample was applied to the upper surface of the first copper body. Then, the other metal component sample (second copper body) was placed on top of the first copper body via the resin component sample, and the resin component sample was permeated into the recesses by the weight of the first copper body. Next, the mold was heated to 100°C and held at 100°C for 3 hours. After that, the temperature was raised to 150°C in 1 hour and held at 150°C for 15 hours to cure the epoxy resin, which is the resin component sample, thereby bonding the resin component between the first and second copper bodies, and resin-metal composite samples were obtained as adhesion test pieces.

[0028] (Measurement of adhesive strength) Figure 8 shows a diagram illustrating a tensile adhesive strength test used to measure adhesive strength. For the tensile adhesive strength test, an Autograph 30 (manufactured by Shimadzu Corporation) was used. As shown in the figure, the resin-metal composite sample 35 has a structure in which a resin member 33 is bonded between a first copper body 34 and a second copper body 32. The first copper body 34 was fixed to the fixing part 36 of the autograph 30, and the second copper body 32 was fixed to the sample mounting pin 31. A tensile adhesive strength test was performed by pulling them in opposite directions, as shown by arrows 103 and 104. At that time, the load at which the resin member was completely peeled off from the copper body was defined as the adhesive strength.

[0029] (Relationship between surface properties and adhesive strength) Figures 9 to 11 show examples of graphs illustrating the relationship between the obtained adhesive strength and surface property parameters. The coefficient of determination R, which indicates the correlation with adhesive strength, is also shown for each parameter. 2 The value was calculated. Figure 9 shows a graph illustrating the relationship between arithmetic mean roughness (Ra) and adhesive strength as surface property parameters. The coefficient of determination R is shown. 2 The value is 0.2832. Figure 10 shows a graph illustrating the relationship between the root mean square height (Rdq) and adhesive strength as surface property parameters. The coefficient of determination is R. 2 It is 0.2803. Figure 11 shows a graph representing the relationship between the surface area (S / A) and the adhesive strength as surface property parameters. The coefficient of determination R 2 is 0.3321. As shown in FIGS. 9 to 11, it can be seen that the relationship with the adhesive strength changes by changing the surface property parameters. Also, even with the same surface property parameters, the relationship with the adhesive strength changes as the numerical values of the parameters change.

[0030] In addition, for one result of the adhesive strength, the surface property parameters may be compared with one or two or more. Examples of graphs showing the relationship between the obtained adhesive strength and two surface property parameters are shown in FIGS. 12 to 15. Here, as the surface property parameters, the arithmetic mean roughness (Ra) and the root mean square height (Rdq) were combined and subjected to arithmetic operations. Also, the coefficient of determination R 2 values indicating the correlation with the adhesive strength were obtained. Figure 12 shows a graph representing the relationship between the result of adding the arithmetic mean roughness (Ra) and the root mean square height (Rdq) as surface property parameters and the adhesive strength. The coefficient of determination R 2 is 0.3301. Figure 13 shows a graph representing the relationship between the result of subtracting the root mean square height (Rdq) from the arithmetic mean roughness (Ra) as surface property parameters and the adhesive strength. The coefficient of determination R 2 is 0.02181.

[0031] Figure 14 shows a graph representing the relationship between the result of multiplying the arithmetic mean roughness (Ra) and the root mean square height (Rdq) as surface property parameters and the adhesive strength. The coefficient of determination R 2 is 0.0352l. Figure 15 shows a graph representing the relationship between the result of dividing the arithmetic mean roughness (Ra) by the root mean square height (Rdq) as surface property parameters and the adhesive strength. The coefficient of determination R 2 is 0.00727. In the results of FIGS. 9 to 11, R 2The value is around 0.3, indicating a low correlation with adhesive strength. However, as shown in Figures 12-15, by combining two or more surface property parameters, R 2 It can be seen that the values ​​change. By understanding and creating a database of many such relationships, it becomes possible to estimate the adhesive strength from the surface properties, provided that the manufacturing methods for metal components, resin components, and resin-metal composite samples, as well as the methods for obtaining surface property parameters, are specified and limited.

[0032] Furthermore, Figures 16-18 show contour maps of adhesive surfaces with three different arithmetic mean roughnesses: Ra=0.8μm, Ra=2.0μm, and Ra=2.8μm, obtained non-contact using a one-shot 3D shape measuring machine (VR-6000) manufactured by Keyence Corporation. The contour plots in Figures 16-18 have dimensions of X = 556.859 μm and Y = 557.394 μm, respectively, and as shown in the illustration, the gradient can represent heights from -10.1 μm to 10.1 μm. As shown in Figures 16-18, by using image data that unifies information about the entire adhesive surface to obtain feature quantities, it is possible to understand the correlation with adhesive strength from a different perspective than the correlation of surface property parameters in Figures 9-15. Figure 19 shows a diagram representing the persistent homology obtained by image analysis of the contour plot in Figure 17. As shown in Figure 19, combining contour maps with persistent homology, an image analysis technique, can provide new insights.

[0033] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0034] 1...Resin-metal composite, 2...Metal component, 3...Resin component, 12...Adhesive surface, 45...Database, 41...Surface properties estimation system for resin-metal composite adhesive surface, 42...Main control unit, 44...Storage unit, 46...Estimation unit, 47...Surface properties measurement unit, 48...Adhesion strength measurement unit

Claims

1. A method for estimating the surface properties of an adhesive surface of a resin-metal composite having a metal member with an adhesive surface and a resin member bonded to the adhesive surface, For a metal member sample having the same composition as the aforementioned metal member and having a roughened adhesive surface, at least one surface characteristic from among the line roughness parameter, surface roughness parameter, or contour diagram showing the height information of the entire adhesive surface is measured. Using a resin-metal composite sample prepared by bonding a resin sample having the same composition as the resin sample to the metal sample, the adhesive strength between the metal sample and the resin sample is measured. A database of the surface properties and adhesive strength is constructed under specific conditions. A method for estimating the surface properties of an adhesive surface of a resin-metal composite, comprising estimating the surface properties of the adhesive surface corresponding to a predetermined adhesive strength based on the aforementioned database.

2. The line roughness parameters of the surface texture are: arithmetic mean height Ra(Pa, Wa), maximum height Rz(Pz, Wz), ten-point mean roughness RzJIS, maximum peak height Rp(Pp, Wp), maximum valley depth Rv(Pv, Wv), average height of contour curve elements Rc(Pc, Wc), maximum cross-sectional height Rt(Pt, Wt), root mean square height Rq(Pq, Wq), skewness Rsk(Psk, Wsk), kurtosis Rku(Pku, Wku), and average length of contour curve elements RSm(PSm, W Sm), root mean square slope RΔq(PΔq, WΔq), load length ratio Rmr(c)(Pmr(c), Wmr(c)), cutting level difference Rδc(Pδc, Wδc), relative load length ratio Rmr(Pmr, Wmr), load length ratio Mr1 separating the protruding peak and core, load length ratio Mr2 separating the protruding valley and core, core level difference Rk, protruding peak height Rpk, protruding valley depth Rvk, cross-sectional area A1 of the protruding peak, cross-sectional area A2 of the protruding valley, high spot count HSC, peak count / cm Pc / cm, peak count RPc(PPc, WPc), or unfolded length RLo(PLo, WLo), unfolded length ratio Rlr(PLr, Wlr), arithmetic mean slope angle RΔa(PΔa, WΔa), arithmetic mean wavelength Rλa(PLa, Wλa), At least one of the following is a root mean square slope angle RΔq (PΔq, WΔq), a root mean square wavelength Rλq (Pλq, Wλq), an evaluation length, a reference length, or a number of reference lengths. The surface roughness parameters are: arithmetic mean height Sa, maximum height Sz, surface texture aspect ratio Str, arithmetic mean curvature of peaks Spc, interface development area ratio Sdr, root mean square height Sq, skewness Ssk, kurtosis Sku, maximum peak height Sp, maximum valley depth Sv, minimum autocorrelation length Sal, surface texture direction Std, root mean square slope Sdq, and peak density Spd. The method according to claim 1, wherein at least one of the following is selected: level difference Sk of the core portion, height Spk of the protruding peak portion, depth Svk of the protruding valley portion, load area ratio Smr1 separating the protruding peak portion and the core portion, load area ratio Smr2 separating the protruding valley portion and the core portion, pole height SXp, void volume Vvv of the valley portion, void volume Vvc of the core portion, actual volume Vmp of the peak portion, actual volume Vmc of the core portion, or area of ​​the measurement region (regional area).

3. The method according to claim 1, wherein the measurement of the surface properties is performed using a contact or non-contact measurement method.

4. The method according to claim 3, wherein the measurement of the surface properties is performed using a non-contact optical measuring device.

5. The method according to claim 1, characterized in that the roughening of the adhesive surface is performed by at least one of the following methods: chemical etching, blasting, laser processing, or additive manufacturing.

6. The method according to claim 1, characterized in that the metal member includes copper or iron.

7. The method according to claim 1, characterized in that the resin member is a thermosetting resin.

8. The method according to claim 1, wherein the database is constructed using at least one method from among computer science, machine learning, or image analysis.

9. A system for estimating the surface properties of a metal member having an adhesive surface and a resin member bonded to the adhesive surface in a resin-metal composite, A surface texture measuring unit measures at least one surface texture from among line roughness parameters, surface roughness parameters, or contour diagrams showing height information of the entire adhesive surface for a plurality of metal member samples having the same composition as the aforementioned metal member and having the adhesive surface roughened, An adhesive strength measuring unit measures the adhesive strength between a metal member sample and a resin member sample having the same composition as the resin member, using a resin-metal composite sample prepared by bonding the metal member sample and a resin member sample having the same composition as the resin member. A storage unit that stores a database constructed under specific conditions based on the surface properties and adhesive strength, A system for estimating the surface properties of an adhesive surface of a resin-metal composite, comprising an estimation unit that estimates the surface properties corresponding to a predetermined adhesive strength of the resin-metal composite based on the database.

10. The line roughness parameters of the surface texture are: arithmetic mean height Ra(Pa, Wa), maximum height Rz(Pz, Wz), ten-point mean roughness RzJIS, maximum peak height Rp(Pp, Wp), maximum valley depth Rv(Pv, Wv), average height of contour curve elements Rc(Pc, Wc), maximum cross-sectional height Rt(Pt, Wt), root mean square height Rq(Pq, Wq), skewness Rsk(Psk, Wsk), kurtosis Rku(Pku, Wku), and average length of contour curve elements RSm(PSm, W Sm), root mean square slope RΔq(PΔq, WΔq), load length ratio Rmr(c)(Pmr(c), Wmr(c)), cutting level difference Rδc(Pδc, Wδc), relative load length ratio Rmr(Pmr, Wmr), load length ratio Mr1 separating the protruding peak and core, load length ratio Mr2 separating the protruding valley and core, core level difference Rk, protruding peak height Rpk, protruding valley depth Rvk, cross-sectional area A1 of the protruding peak, cross-sectional area A2 of the protruding valley, high spot count HSC, peak count / cm Pc / cm, peak count RPc(PPc, WPc), or unfolded length RLo(PLo, WLo), unfolded length ratio Rlr(PLr, Wlr), arithmetic mean slope angle RΔa(PΔa, WΔa), arithmetic mean wavelength Rλa(PLa, Wλa), At least one of the following is a root mean square slope angle RΔq (PΔq, WΔq), a root mean square wavelength Rλq (Pλq, Wλq), an evaluation length, and a reference length. The surface roughness parameters are: arithmetic mean height Sa, maximum height Sz, surface texture aspect ratio Str, arithmetic mean curvature of peaks Spc, interface development area ratio Sdr, root mean square height Sq, skewness Ssk, kurtosis Sku, maximum peak height Sp, maximum valley depth Sv, minimum autocorrelation length Sal, surface texture direction Std, root mean square slope Sdq, peak density Spd, and core level difference Sk. A system for estimating the surface properties of a resin-metal composite adhesive surface according to claim 9, wherein the system comprises at least one of the following: height Spk of the protruding peak, depth Svk of the protruding valley, load area ratio Smr1 separating the protruding peak and the core, load area ratio Smr2 separating the protruding valley and the core, pole height SXp, void volume Vvv of the valley, void volume Vvc of the core, actual volume Vmp of the peak, actual volume Vmc of the core, or area of ​​the measurement region (regional area).

11. The surface properties estimation system for an adhesive surface of a resin-metal composite according to claim 9, wherein the measurement of the surface properties is performed using a contact-type or non-contact measurement method.

12. The surface properties estimation system for the adhesive surface of a resin-metal composite according to claim 11, wherein the measurement of the surface properties is performed using a non-contact optical measuring device.

13. The surface roughening of the adhesive surface is performed by at least one of the following methods: chemical etching, blasting, laser processing, or additive manufacturing, as described in claim 9, for estimating the surface properties of an adhesive surface of a resin-metal composite.

14. The system for estimating the surface properties of the adhesive surface of a resin-metal composite according to claim 9, characterized in that the metal member includes copper or iron.

15. The system for estimating the surface properties of the adhesive surface of a resin-metal composite according to claim 9, characterized in that the resin member is a thermosetting resin.

16. The system for estimating the surface properties of an adhesive surface of a resin-metal composite according to claim 9, comprising constructing the database using at least one method from among computer science, machine learning, or image analysis.

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