Theoretical calculation method and device for thickness of heat affected layer on metal electromachining surface

Abstract

The invention discloses a theoretical calculation method and device for the thickness of a heat-affected layer on a metal electromachining surface, and relates to the technical field of material analys.The method comprises the following steps that pretreatment is conducted on the heat-affected layer on the surface of a metal workpiece to be treated, and corresponding crystallographic characteristic information is obtained; respectively constructing a plurality of fitting calculation models, and substituting the crystallographic characteristic information into the plurality of fitting calculation models to obtain corresponding characteristic parameters; constructing a microhardness calculation model, and substituting the characteristic parameters into the microhardness calculation model for processing to obtain microhardness; obtaining the thickness of the heat affected layer according to the microhardness; the layer depth of the heat affected layer is predicted according to the change rule of the microscopic size in the layer depth direction.

Description

Technical Field

[0001] The present invention relates to the technical field of material analysis, and more particularly to a theoretical calculation method and device for the thickness of a heat-affected layer on a metal electro-processing surface. Background Art

[0002] Discharge machining (EDM) is a key branch of specialized metalworking, enabling the machining of ultra-hard materials and complex workpieces that are difficult to machine using traditional cutting methods. However, the material removal method used in EDM significantly impacts the machined surface. During machining, the dielectric between the positive and negative electrodes breaks down, creating a discharge channel and rapidly increasing temperatures. This high temperature causes the material to melt, condensing into metal particles that are then carried away by the dielectric. Any metal material that is unable to escape solidifies and adheres to the workpiece surface, forming a recast layer. This recast layer possesses distinct properties distinct from those of the parent material and can be removed through machining or chemical etching. However, beneath this recast layer lies a heat-affected layer (HAZ). Underlying this layer lies a heat-affected layer (HAZ). The high temperatures induced by EDM cause changes in the microstructure of the subsurface layer, resulting in grain growth and corresponding changes in the mechanical properties of the HAZ. Determining the depth of this HAZ to determine the appropriate machining methods and parameters is a key area of research.

[0003] However, in electrical machining, the heat source occurs in the area where the metal contacts the tool electrode, and heat is transferred and attenuated along the depth direction, which mainly affects the grain size, grain orientation deviation, dislocation density change, and twin band conditions. These crystallographic parameters show a certain change pattern under the influence of the heat source. The traditional method of determining the depth of the heat-affected layer is mostly cutting cross-section + microhardness testing, that is, processing the sample by wire cutting, and using hardness testing or nanoindentation experiments along the layer depth direction to determine the distribution of microhardness, but this method requires a lot of time and experimental operations. In addition, due to hardness fluctuations caused by other reasons at the boundary of the heat-affected layer, the depth of the heat-affected layer is difficult to express accurately.

[0004] Therefore, how to provide a theoretical calculation method for the thickness of the heat-affected layer on the surface of metal electromachining that can solve the above-mentioned technical problems is an issue that those skilled in the art urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a theoretical calculation method and device for the thickness of the heat-affected layer on the surface of metal electro-machined surfaces, which predicts the depth of the heat-affected layer by the change law of microscopic size along the depth direction.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A theoretical calculation method for the thickness of the heat-affected layer on a metal electro-machined surface comprises the following steps:

[0008] S1: Pre-treat the heat-affected layer on the surface of the metal workpiece to obtain the corresponding crystallographic feature information;

[0009] S2: constructing a plurality of fitting calculation models respectively, and substituting the crystallographic characteristic information into the plurality of fitting calculation models to obtain corresponding characteristic parameters;

[0010] S3: constructing a microhardness calculation model, and substituting the characteristic parameters obtained in S2 into the microhardness calculation model for processing to obtain the microhardness;

[0011] S4: Calculate the thickness of the heat-affected layer based on the microhardness obtained in S3.

[0012] Preferably, the S1 specifically includes:

[0013] S11: Select the area below the recast layer for wire cutting, clean the cut surface and mount the sample;

[0014] S12: grinding, polishing and etching the sample surface in sequence;

[0015] S13: collecting electronic patterns of the samples obtained after processing in S12;

[0016] S14: Processing the electronic image to obtain corresponding crystallographic feature information.

[0017] Preferably, the S2 includes:

[0018] S21: constructing a plurality of fitting calculation models respectively, wherein the plurality of fitting calculation models include a grain layer depth fitting calculation model, a misorientation layer depth fitting calculation model, and a twin layer depth fitting calculation model;

[0019] S22: Substitute the crystallographic characteristic information into the grain layer depth fitting calculation model, the orientation difference layer depth fitting calculation model and the twin layer depth fitting calculation model respectively to obtain the corresponding grain layer depth, orientation difference layer depth and twin layer depth, where the grain layer depth, orientation difference layer depth and twin layer depth are the characteristic parameters.

[0020] Preferably, the S3 includes:

[0021] S31: Determine multiple weight coefficients;

[0022] S32: A microhardness calculation model is constructed based on multiple weight coefficients, grain layer depth, misorientation layer depth, and twin layer depth. The specific expression is:

[0023]

[0024] Wherein, w1 and w2 are HV(h) and HV0, respectively, which represent the microhardness at a depth of h, the microhardness of the parent material, k, the Hall-Petch strengthening coefficient, M, the Taylor factor, α, the material constant, G, the shear modulus, μ, the step size selected in the EBSD experiment, b, the Burgers vector length, γ, the spacing between twin bands, D(h) and K(h) respectively, which represent the average grain diameter at a depth of h, the average nucleus misorientation at a depth of h, and T(h) respectively, which represent the average twin thickness at a depth of h.

[0025] S33: Calculate the microhardness according to the microhardness calculation model obtained in S32.

[0026] Preferably, the S4 includes:

[0027] S41: Calculate the depth of the microstructure-affected layer of the recast layer;

[0028] S42: The thickness of the heat-affected layer is obtained by combining the depth of the microstructure-affected layer and the microhardness. The specific expression is:

[0029] h HAZ =max(h HV ,h M ) (2)

[0030] Where h M Indicates the depth of the microstructure influence layer, h HV Indicates microhardness.

[0031] The present invention also provides a theoretical calculation device for the thickness of the heat-affected layer on the surface of metal electro-processing, comprising:

[0032] A processing module is used to pre-process the heat-affected layer on the surface of the metal workpiece to obtain corresponding crystallographic feature information;

[0033] A first calculation module is used to construct a plurality of fitting calculation models respectively, and substitute the crystallographic characteristic information into the plurality of fitting calculation models to obtain corresponding characteristic parameters;

[0034] The second calculation module is used to construct a microhardness calculation model and substitute the characteristic parameters obtained in S2 into the microhardness calculation model for processing to obtain the microhardness;

[0035] The third calculation module is used to obtain the thickness of the heat-affected layer according to the microhardness.

[0036] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a theoretical calculation method and device for the thickness of the heat-affected layer on the surface of metal electro-processing, which comprehensively considers the influence of the electro-processing heat source on the macro level of the metal, namely the microhardness, and the influence on the micro level, namely the grain size, grain orientation difference, twin thickness, etc. By integrating multi-scale microstructural parameters such as grain refinement (Hall-Petch effect), dislocation strengthening (core average orientation difference) and twin strengthening, a composite model is established to quantify the synergistic effect of different strengthening mechanisms, so that the prediction accuracy of the heat-affected layer is higher, the sudden change boundary of the depth of the heat-affected layer can be accurately captured, and a large number of experiments using microhardness testing are reduced.

[0037] Traditional wire cutting combined with microhardness testing requires workpiece destruction, and single sample preparation is time-consuming. This method, through local sampling combined with EBSD analysis, requires only pre-treatment of a tiny area (approximately 2 mm x 2 mm) beneath the recast layer, reducing sample preparation time by over 50%. Full cross-section cutting is also unnecessary, preserving the integrity of the workpiece. This highly efficient non-destructive testing reduces material waste and overall costs.

[0038] By introducing weight coefficients, the adaptability of the model to different processing conditions is improved. The dynamic weight coefficient enhances the generalization ability of the model and can adjust the contribution weight of each micro parameter according to the actual working conditions. Through a small amount of microhardness calibration tests, the new process can be quickly adapted, and the adaptability of the model is improved by more than 50%, which is especially suitable for multi-variety and small batch production modes. In addition, the heat affected layer depth prediction result h output by the method provided by the present invention is HAZ A mapping relationship can be directly established with processing parameters (such as current and pulse width) to provide data support for subsequent process optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 This is an overall flow chart of a theoretical calculation method for the thickness of the heat-affected layer of a metal electro-machined surface provided by the present invention;

[0041] Figure 2 A schematic diagram of a specific process flow of the pre-processing provided by an embodiment of the present invention;

[0042] Figure 3 A microscopic structural morphology diagram of the layered process provided by an embodiment of the present invention;

[0043] Figure 4A schematic diagram of a microhardness test according to an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of the microhardness distribution along the layer depth direction provided by an embodiment of the present invention;

[0045] Figure 6 The average distribution of grain diameters along the depth direction provided by the embodiment of the present invention;

[0046] Figure 7 The average distribution of the average orientation difference of the core along the layer depth direction provided by the embodiment of the present invention;

[0047] Figure 8 The average distribution of twin thickness along the layer depth direction provided by the embodiment of the present invention;

[0048] Figure 9 This is a structural principle block diagram of a theoretical calculation device for the thickness of the heat-affected layer on a metal electro-machined surface provided by the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1 As shown, the embodiment of the present invention discloses a theoretical calculation method for the thickness of the heat-affected layer of the metal electro-machined surface, comprising the following steps:

[0051] S1: Pre-treat the heat-affected layer on the surface of the metal workpiece to obtain the corresponding crystallographic feature information;

[0052] S2: Construct multiple fitting calculation models respectively, and substitute the crystallographic feature information into the multiple fitting calculation models to obtain the corresponding feature parameters;

[0053] S3: Construct a microhardness calculation model and substitute the characteristic parameters obtained in S2 into the microhardness calculation model to obtain the microhardness;

[0054] S4: The thickness of the heat-affected layer is obtained based on the microhardness obtained in S3.

[0055] See also Figure 2 As shown, in a specific embodiment, S1 specifically includes:

[0056] S11: Select the area below the recast layer for wire cutting, clean the cut surface and mount the sample. After cutting, use alcohol or acetone to clean and remove debris.

[0057] S12: grinding, polishing and etching the sample surface in sequence;

[0058] The sample surface was polished using sandpaper of different mesh sizes to ensure the removal of the wire-cut damage layer and scratches, thereby improving the surface flatness. Mechanical polishing was then performed to achieve the required smoothness for testing. Finally, chemical etching was performed using nitric acid to ensure the visibility of grain boundaries and subgrain boundaries.

[0059] S13: collecting electronic patterns of the samples obtained after processing in S12;

[0060] Mount the prepared sample on the SEM sample stage, set up the electron backscatter diffraction (EBSD) analysis program in the SEM, select appropriate voltage and current, and adjust the focus to obtain good image quality. Adjust the position and tilt of the sample, perform backscattered electron analysis, capture the electron pattern, and determine the crystal structure and crystal orientation.

[0061] S14: Processing the electronic image to obtain corresponding crystallographic feature information. The electronic image processing process can be implemented using relevant analysis software. The crystallographic feature information may include information such as crystal structure, crystal orientation, and grain boundaries.

[0062] In a specific embodiment, S2 includes:

[0063] S21: constructing a plurality of fitting calculation models respectively, wherein the plurality of fitting calculation models include a grain layer depth fitting calculation model, a misorientation layer depth fitting calculation model, and a twin layer depth fitting calculation model;

[0064] S22: Substitute the crystallographic characteristic information into the grain layer depth fitting calculation model, the orientation difference layer depth fitting calculation model and the twin layer depth fitting calculation model respectively to obtain the corresponding grain layer depth, orientation difference layer depth and twin layer depth, where the grain layer depth, orientation difference layer depth and twin layer depth are characteristic parameters.

[0065] For details, see Figure 3-5 As shown, S21 specifically includes:

[0066] The microstructure is divided into several layers according to the width Δh along the depth gradient direction. The analysis software is used to count and calculate the average grain diameter, average nuclear orientation difference, average twin thickness and other data of each layer, and a scatter plot of each parameter changing with layer depth is drawn.

[0067] Considering that the depth h caused by layering is not an exact value, the midpoint between the upper and lower boundaries of the current layer is assigned to h. That is, the layer depth h of the nth layer [(n-1)Δh, nΔh] can be expressed as (2n-1)Δh / 2, where n is a positive integer.

[0068] The obtained data were fitted with a function fitting method to determine the functional relationship between the average grain diameter, the average core orientation difference, the average twin thickness and the depth, which were set as D(h), K(h), and T(h) respectively. The specific results can be found in Figure 6-8 shown.

[0069] In a specific embodiment, S3 includes:

[0070] S31: determining a plurality of weight coefficients w1 and w2, which are obtained by calibration of a microhardness measurement test;

[0071] S32: A microhardness calculation model is constructed based on multiple weight coefficients, grain layer depth, misorientation layer depth, and twin layer depth. The specific expression is:

[0072]

[0073] Wherein, w1 and w2 are HV(h) and HV0, respectively, which represent the microhardness at a depth of h, the microhardness of the parent material, k, the Hall-Petch strengthening coefficient, M, the Taylor factor, α, the material constant, G, the shear modulus, μ, the step size selected in the EBSD experiment, b, the Burgers vector length, γ, the spacing between twin bands, D(h) and K(h) respectively, which represent the average grain diameter at a depth of h, the average nucleus misorientation at a depth of h, and T(h) respectively, which represent the average twin thickness at a depth of h.

[0074] S33: Calculate the microhardness according to the microhardness calculation model obtained in S32.

[0075] Specifically, microhardness is often closely related to microstructural features such as grain refinement, mechanical twinning, and dislocation structures. The intrinsic properties of a material determine its microhardness. The dislocation theory is currently the most widely accepted explanation for work hardening. It has been proposed that by considering the effects of grain refinement and dislocation strengthening on material hardness, it is possible to theoretically calculate material hardness.

[0076] In S32, the Hall-Petch strengthening coefficient k is calculated based on the yield strength of the material at different grain sizes. The specific expression is:

[0077]

[0078] Where σ0 is the initial stress dislocation motion constant of the parent material, σy is the yield stress of the material.

[0079] The Taylor factor M is used to characterize the average effect of grain direction. The specific expression is:

[0080]

[0081] Where σ represents the tensile stress and τ is the shear strain on each slip plane of the crystal.

[0082] In a specific embodiment, S4 includes:

[0083] S41: Calculate the depth of the microstructure influence layer of the recast layer. The specific expression is:

[0084] h M =w1h D +w2h K +(1-w1-w2)h T (4)

[0085] Where h D represents the average grain diameter at depth h, h K represents the average orientation difference of the nuclei at depth h, h T represents the average twin thickness at depth h;

[0086] S42: The thickness of the heat-affected layer is obtained by combining the depth of the microstructure-affected layer and the microhardness. The specific expression is:

[0087] h HAZ =max(h HV ,h M ) (5)

[0088] Where h M Indicates the depth of the microstructure influence layer, h HV Indicates microhardness.

[0089] See also Figure 9 As shown, an embodiment of the present invention further provides a device using the theoretical calculation method of the thickness of the heat-affected layer of the metal electro-machined surface described in any of the above embodiments, comprising:

[0090] A processing module is used to pre-process the heat-affected layer on the surface of the metal workpiece to obtain corresponding crystallographic feature information;

[0091] The first calculation module is used to construct multiple fitting calculation models respectively, and substitute the crystallographic feature information into the multiple fitting calculation models to obtain corresponding feature parameters;

[0092] The second calculation module is used to construct a microhardness calculation model and substitute the characteristic parameters obtained by S2 into the microhardness calculation model for processing to obtain the microhardness;

[0093] The third calculation module is used to obtain the thickness of the heat-affected layer according to the microhardness.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0095] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A theoretical calculation method for the thickness of the heat-affected layer of a metal electro-processing surface, characterized in that: The following steps are involved: S1: Pre-processing the area below the recast layer on the surface of the metal workpiece to obtain corresponding crystallographic feature information; S2: constructing a plurality of fitting calculation models respectively, and substituting the crystallographic characteristic information into the plurality of fitting calculation models to obtain corresponding characteristic parameters; S3: constructing a microhardness calculation model, and substituting the characteristic parameters obtained in S2 into the microhardness calculation model for processing to obtain the microhardness; S4: Calculate the thickness of the heat-affected layer based on the microhardness obtained in S3.

2. The theoretical calculation method for the thickness of the heat-affected layer of a metal electro-processing surface according to claim 1 is characterized in that: Said S1 specifically includes: S11: Select the area below the recast layer for wire cutting, clean the cut surface and mount the sample; S12: grinding, polishing and etching the sample surface in sequence; S13: collecting electronic patterns of the samples obtained after processing in S12; S14: Processing the electronic image to obtain corresponding crystallographic feature information.

3. The theoretical calculation method for the thickness of the heat-affected layer of a metal electro-processing surface according to claim 2, characterized in that: The S2 includes: S21: constructing a plurality of fitting calculation models respectively, wherein the plurality of fitting calculation models include a grain layer depth fitting calculation model, a misorientation layer depth fitting calculation model, and a twin layer depth fitting calculation model; S22: Substitute the crystallographic characteristic information into the grain layer depth fitting calculation model, the orientation difference layer depth fitting calculation model and the twin layer depth fitting calculation model respectively to obtain the corresponding grain layer depth, orientation difference layer depth and twin layer depth, where the grain layer depth, orientation difference layer depth and twin layer depth are the characteristic parameters.

4. The method for theoretically calculating the thickness of the heat-affected layer of a metal electro-processing surface according to claim 3, characterized in that: The S3 includes: S31: Determine multiple weight coefficients; S32: A microhardness calculation model is constructed based on multiple weight coefficients, grain layer depth, misorientation layer depth, and twin layer depth. The specific expression is: Wherein, w1 and w2 are HV(h) and HV0, respectively, which represent the microhardness at a depth of h, the microhardness of the parent material, k, the Hall-Petch strengthening coefficient, M, the Taylor factor, α, the material constant, G, the shear modulus, μ, the step size selected in the EBSD experiment, b, the Burgers vector length, γ, the spacing between twin bands, D(h) and K(h) respectively, which represent the average grain diameter at a depth of h, the average nucleus misorientation at a depth of h, and T(h) respectively, which represent the average twin thickness at a depth of h. S33: Calculate the microhardness according to the microhardness calculation model obtained in S32.

5. The theoretical calculation method for the thickness of the heat-affected layer of a metal electro-processing surface according to claim 4 is characterized in that: The S4 includes: S41: Calculate the depth of the microstructure-affected layer of the recast layer; S42: The thickness of the heat-affected layer is obtained by combining the depth of the microstructure-affected layer and the microhardness. The specific expression is: h HAZ =max(h HV ,h M ) (2) Where h M Indicates the depth of the microstructure influence layer, h HV Indicates microhardness.

6. A device using the theoretical calculation method of the thickness of the heat-affected layer of a metal electro-machined surface according to any one of claims 1 to 5, characterized in that: include: A processing module is used to pre-process the heat-affected layer on the surface of the metal workpiece to obtain corresponding crystallographic feature information; A first calculation module is used to construct a plurality of fitting calculation models respectively, and substitute the crystallographic characteristic information into the plurality of fitting calculation models to obtain corresponding characteristic parameters; The second calculation module is used to construct a microhardness calculation model and substitute the characteristic parameters obtained in S2 into the microhardness calculation model for processing to obtain the microhardness; The third calculation module is used to obtain the thickness of the heat-affected layer according to the microhardness.

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