Surface modification layer thickness prediction device and thickness prediction method

Abstract

A surface modification layer thickness prediction device for a workpiece formed using a laser has a laser irradiation condition acquisition section that acquires laser irradiation conditions including at least a processing speed and a laser output condition for each position on the surface of the workpiece, a material information acquisition section that acquires material information for the workpiece, and a surface modification layer thickness prediction section that predicts the thickness of the surface modification layer for each position on the workpiece based on the laser irradiation conditions and the material information, without additional pre-processing confirmation or processing or post-processing inspection procedures.

Description

Technical Field

[0001] This disclosure relates to a thickness prediction device and method for surface-modified layers, and more particularly to a thickness prediction device and method for surface-modified layers of workpieces formed using lasers. Background Technology

[0002] For example, Patent Document 1 and Patent Document 2 describe a method for surface modification of a workpiece by irradiating the surface of the workpiece with a laser.

[0003] Patent document 1 describes a method for manufacturing a surface-modified substrate by irradiating the surface of a metal substrate with a laser to modify the surface of the substrate.

[0004] Specifically, Patent Document 1 describes a method for manufacturing a surface-modified substrate, which includes: a step of setting a resin layer that transmits laser light on the surface of a metal substrate; a step of irradiating the surface of the substrate with laser light through the resin layer to melt the surface of the substrate, and using the heat of the substrate to thermally decompose the resin layer.

[0005] Patent document 2 describes a method for manufacturing a surface-modified component, which can obtain a modified layer with high adhesion to the substrate and thick film by reducing the mixing of substrate components.

[0006] Specifically, Patent Document 2 describes a method for manufacturing a surface-modified component, which includes: a step of forming a spray-coated film on a substrate; a step of irradiating the surface of the spray-coated film with a high-energy beam to melt and solidify the entire spray-coated film and a portion of the substrate in the thickness direction, thereby forming a dense modified layer; a step of forming a spray-coated film on the newly formed modified layer; and a step of irradiating the surface of the spray-coated film with a high-energy beam to melt and solidify the entire spray-coated film and a portion of the newly formed modified layer in the thickness direction, thereby forming a dense modified layer.

[0007] Existing technical documents

[0008] Patent documents

[0009] Patent Document 1: International Publication No. 2016 / 103385

[0010] Patent Document 2: Japanese Patent Application Publication No. 2019-163550 Summary of the Invention

[0011] The problem that the invention aims to solve

[0012] In surface modification of workpieces using lasers, such as laser hardening and laser cladding (coating), mechanical parts are locally strengthened.

[0013] In surface modification processes, the thickness of the surface-modified layer is important, and a thickness prediction device and method for predicting the thickness of the surface-modified layer are desired.

[0014] Methods for solving problems

[0015] A representative first aspect of this disclosure is a thickness prediction device for a surface modification layer of a workpiece formed using a laser, wherein the thickness prediction device comprises:

[0016] The laser irradiation condition acquisition unit acquires laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece.

[0017] The material information acquisition unit acquires the material information of the workpiece.

[0018] The surface modification layer thickness prediction unit predicts the thickness of the surface modification layer at various locations on the workpiece based on the laser irradiation conditions and the material information.

[0019] A representative second approach disclosed herein is a method for predicting the thickness of a surface-modified layer, wherein,

[0020] The computer, acting as a thickness prediction device for the surface modification layer of a workpiece formed using a laser, performs the following processing:

[0021] A process for obtaining laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece;

[0022] Processing to obtain the material information of the workpiece;

[0023] The process of predicting the thickness of the surface modification layer at each location of the workpiece based on the laser irradiation conditions and the material information. Attached Figure Description

[0024] Figure 1 This is a block diagram illustrating a structural example of the thickness prediction device according to the first embodiment of this disclosure.

[0025] Figure 2 This is an explanatory diagram illustrating laser quenching.

[0026] Figure 3 This diagram illustrates the situation where the workpiece is moved relative to the laser head while the laser head irradiates the workpiece with laser light.

[0027] Figure 4 It is a cross-sectional view showing the thickness of the surface modification layer formed on the workpiece.

[0028] Figure 5An example of a display image that shows the degree of thickness of a surface modification layer by changing the way the workpiece is displayed.

[0029] Figure 6 This is an explanatory diagram illustrating laser cladding.

[0030] Figure 7 This refers to a situation where the workpiece is moved relative to the laser head and powder is supplied while the laser head irradiates the workpiece with laser light.

[0031] Figure 8 It is a cross-sectional view showing the thickness of the surface modification layer formed on the workpiece.

[0032] Figure 9 This describes a method for calculating the area irradiated by laser per unit time.

[0033] Figure 10 It is a 3D diagram representing the workpiece after it has been divided into grids.

[0034] Figure 11 It is a graph representing the heat flux imparted at time t.

[0035] Figure 12 It is a graph showing the temperature distribution across the cross-section of the workpiece.

[0036] Figure 13 It is a cross-sectional view showing the thickness of the surface modification layer formed on the workpiece.

[0037] Figure 14 This is a flowchart illustrating an example of the operation of a thickness prediction method using a thickness prediction device.

[0038] Figure 15 This is a block diagram illustrating a structural example of the thickness prediction device according to the second embodiment of this disclosure.

[0039] Figure 16 This is a diagram illustrating an example of a processing procedure.

[0040] Figure 17 It is a diagram showing the position of the workpiece origin relative to the machining origin.

[0041] Figure 18 It is a diagram representing the machining path relative to the machining origin.

[0042] Figure 19 This is a block diagram illustrating a structural example of the thickness prediction device according to the third embodiment of this disclosure.

[0043] Figure 20 This is a block diagram illustrating a structural example of the thickness prediction device according to the fourth embodiment of this disclosure.

[0044] Figure 21 A display example showing the thickness of the surface modification layer at the determined location. Detailed Implementation

[0045] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0046] The embodiments disclosed herein are based on a thickness prediction device and a thickness prediction method for surface modification of a workpiece using laser. The surface modification method is not particularly limited, but the following description will focus on the cases of laser quenching and laser cladding (coating).

[0047] (First Implementation)

[0048] Figure 1 This is a block diagram illustrating a structural example of the thickness prediction device according to the first embodiment of the present disclosure.

[0049] like Figure 1 As shown, the thickness prediction device 10 includes a laser irradiation condition acquisition unit 11, a material information acquisition unit 12, a surface modification layer thickness prediction unit 13, and a display unit 14. The display unit 14 is provided as needed. The display unit 14 can also be replaced by a structural unit capable of outputting information including the thickness of the surface modification layer to the user. For example, the display unit 14 can also be replaced by a printer or a communication unit that sends information to an external device.

[0050] The laser irradiation condition acquisition unit 11 acquires laser irradiation conditions including processing speed and laser output conditions at various positions on the surface of the workpiece. Processing speed, for example, refers to the speed at which the workpiece moves relative to the laser head irradiating it with laser light. Laser output conditions, for example, are the laser output and the spot diameter. In the case of laser cladding, the laser irradiation conditions include, in addition to processing speed and laser output conditions, supplementary material supply conditions. Supplementary material supply conditions, when the supplementary material is a powder such as metal powder, are, for example, the powder supply amount. The powder supply amount is the amount of powder supplied to the workpiece irradiated by the laser per unit time. The laser oscillator receives an output command and generates laser light, and the laser head focuses the laser light to irradiate the workpiece.

[0051] The material information acquisition unit 12 acquires the material information of the workpiece. The material information indicates at least one of the following: application, composition, physical properties, and mechanical properties. For example, when the material information is indicated as S45C, S45C indicates that it is a type of medium carbon steel for mechanical structures with a carbon content of 0.45%.

[0052] The surface modification layer thickness prediction unit 13 predicts the thickness of the surface modification layer at each location of the workpiece based on the laser irradiation conditions obtained from the laser irradiation conditions acquisition unit 11 and the material information obtained from the material information acquisition unit 12. In the case of quenching, the thickness of the surface modification layer is, for example, the depth reaching the quenching temperature; in the case of laser cladding, it is the thickness of the heat-affected portion and the weld overlay portion that are affected by heat from the surface of the workpiece.

[0053] Display unit 14 displays the thickness of the surface modified layer predicted by surface modified layer thickness prediction unit 13. Display unit 14 is, for example, a liquid crystal display device, which displays the thickness of the surface modified layer numerically. Display unit 14 can also display the degree of thickness of the surface modified layer by changing the display mode of the workpiece.

[0054] The following describes the method by which the surface modification layer thickness prediction unit 13 predicts the thickness of the surface modification layer at each location of the workpiece.

[0055] (First Method)

[0056] The first method involves using a table containing experimentally derived information to predict the thickness of the surface-modified layer in the case of laser hardening.

[0057] Figure 2 This is an illustrative diagram illustrating laser hardening. (For example...) Figure 2 As shown, a laser is used to irradiate the workpiece 30, such as metal, to heat its surface. Then, the laser moves relative to the workpiece 30, causing the workpiece 30 to cool down through self-cooling, thus performing quenching. For example, as... Figure 3 As shown, while the workpiece 30 is moved at a processing speed, a laser is irradiated onto the workpiece 30 from the laser head 20. The specific laser irradiation conditions and the material information of the workpiece are determined based on these conditions. Figure 4 The cross-sectional view shows the thickness D of the surface modification layer of the workpiece 30. The thickness D of the surface modification layer is the length (depth) in the vertical direction from the surface of the workpiece to the quenching temperature.

[0058] The surface modification layer thickness prediction unit 13 includes a storage unit that stores a table, obtained experimentally, showing the relationship between laser irradiation conditions, material information, and the thickness of the surface modification layer. Specifically, the storage unit stores a table arranged with processing speed (m / sec), laser output (W), spot diameter (m), material information, and the thickness of the surface modification layer. Processing speed, laser output, and spot diameter are laser irradiation conditions, as already explained. Material information is, for example, S45C. The surface modification layer thickness prediction unit 13 refers to the table and calculates the thickness of the surface modification layer corresponding to the obtained laser irradiation conditions and material information.

[0059] Display unit 14 displays the thickness of the surface-modified layer obtained by the method described above. Display unit 14 may display the thickness of the surface-modified layer, for example, by displaying a numerical value or by changing the display method of the workpiece, thereby displaying the degree of thickness of the surface-modified layer. In addition to displaying the thickness of the surface-modified layer, display unit 14 may also display at least one of the following: laser irradiation conditions and material information.

[0060] The display unit 14 displays the thickness of the surface modification layer numerically, for example, "the thickness of the surface modification layer is 1 mm".

[0061] Figure 5 An example is shown where a display image shows the degree of thickness of a surface-modified layer by varying the way the workpiece is displayed. Figure 5 In this context, with the thickness of the surface modification layer set to 0~1mm, the area from 0.8mm to 1mm is represented by the blackened area AR1, and the area from 0.6mm to 0.8mm is represented by the diagonally lined area AR2. Figure 5 In this diagram, the thickness of the surface modification layer is indicated by two display methods (blacked-out areas and diagonally lined areas), but more than three display methods can also be used to represent three or more thickness levels, such as 0.8mm to 1mm, 0.6mm to 0.8mm, and 0.4mm to 0.6mm. Changes in display methods can also be represented by color. For example, red can represent the 0.8mm to 1mm area, orange the 0.6mm to 0.8mm area, and yellow the 0.4mm to 0.6mm area.

[0062] (Second Method)

[0063] The second method, in the case of laser cladding (coating), uses a table containing information obtained experimentally to predict the thickness of the surface modification layer.

[0064] Figure 6 This is an illustrative diagram illustrating laser cladding. (For example...) Figure 6 As shown, a laser is used as a heat source to melt powders such as metal powder, which are then deposited onto the surface of the workpiece 31 with the same or different materials, thereby performing laser cladding. Figure 6 As shown, the weld overlay 31A is formed by laser cladding. The molten pool 31C is formed by laser irradiation, and the heat-affected zone 31B is formed by cooling.

[0065] like Figure 7 As shown, when laser light is irradiated onto the workpiece 31 from the laser head 20 at a predetermined processing speed and powder is supplied, the determination is made based on the laser irradiation conditions including the powder supply amount and the material information of the workpiece. Figure 8The thickness D1 of the surface modification layer shown. The thickness D1 of the surface modification layer is the sum of the thickness of the heat-affected portion 31B that is affected by heat from the surface of the workpiece and the weld overlay portion 31A.

[0066] The surface modification layer thickness prediction unit 13 includes a storage unit that stores a table, determined experimentally, showing the relationship between laser irradiation conditions (including powder supply amount), material information, and the thickness of the surface modification layer. Specifically, the storage unit stores a table arranged with processing speed (m / sec), laser output (W), spot diameter (m), powder supply amount (g / sec), material information, and the thickness of the surface modification layer. As already explained, processing speed, laser output, and spot diameter are laser irradiation conditions. The material information is, for example, S45C. The surface modification layer thickness prediction unit 13 refers to the table and determines the thickness of the surface modification layer corresponding to the obtained laser irradiation conditions (including powder supply amount) and material information.

[0067] The display unit 14, similar to the first method described above, displays the thickness of the surface modified layer using numerical values ​​or by changing the display method of the workpiece to indicate the degree of thickness of the surface modified layer, thereby displaying the thickness of the surface modified layer.

[0068] (Third method)

[0069] The third method is to predict the thickness of the surface modification layer by calculating using laser irradiation conditions and material information in the case of laser quenching.

[0070] The surface modification layer thickness prediction unit 13 first calculates the heat flux q based on the laser irradiation conditions.

[0071] Figure 9 This describes the method used to calculate the area irradiated by laser light per unit time. For example... Figure 9 As shown, the laser head 20 moves relative to the workpiece 30 at a processing speed v, and the laser spot moves at the same speed v. The surface modification layer thickness prediction unit 13 calculates the laser irradiation area S per unit time (S = v × d) by multiplying the processing speed v by the laser spot diameter d. Additionally, the surface modification layer thickness prediction unit 13 calculates the heat input P per unit time (P = LP × AB) by multiplying the laser output LP by the thermal absorptivity AB. Then, the surface modification layer thickness prediction unit 13 calculates the heat flux q (q = P / S) by dividing the heat input P by the laser irradiation area S. The thermal absorptivity AB is included in the material information.

[0072] Next, the surface modification layer thickness prediction unit 13 calculates the temperature distribution using the finite element method. The method for calculating the temperature distribution using the finite element method is described, for example, in...

[0073] https: / / www.jstage.jst.go.jp / article / imono / 63 / 1 / 63_1_32 / _pdf / -char / ja; and

[0074] https: / / www.jstage.jst.go.jp / article / jjasnaoe1968 / 1984 / 156 / 1984_156_406 / _pdf / -char / en.

[0075] Specifically, such as Figure 10 As shown, the surface modification layer thickness prediction unit 13 performs mesh segmentation on the workpiece, thereby discretizing the heat conduction equation shown in Equation 1 (hereinafter Equation 1). Thermal conductivity κ, density ρ, and specific heat c are included in the material information.

[0076] [Mathematical Expression 1]

[0077]

[0078] Next, the surface modification layer thickness prediction unit 13 provides a condition that the heat flux q is applied to the surface of the workpiece only within the spot area, as a boundary condition for the discretized heat conduction equation. Figure 11 This represents the heat flux imparted at time t. The surface modification layer thickness prediction unit 13 solves the discretized heat conduction equations for each element of the grid, calculates the temperature of each element, and calculates the temperature distribution. Figure 12 The temperature distribution across the cross-section of the workpiece 30 is shown. For example... Figure 12 As shown, the temperature decreases with distance from the surface.

[0079] The surface modification layer thickness prediction unit 13 calculates the depth at which the quenching temperature is reached based on the calculated temperature distribution and uses this as the thickness of the surface modification layer. Figure 13 This is a cross-sectional view showing the thickness of the surface modification layer formed on the workpiece. The thickness is determined by the laser irradiation conditions and the material information of the workpiece. Figure 13 The cross-sectional view shows the thickness D2 of the surface modification layer on the workpiece 30.

[0080] Next, the thickness prediction method of the thickness prediction device 10 will be explained using a flowchart.

[0081] Figure 14 This is a flowchart illustrating an example of the actions performed by the thickness prediction method using the thickness prediction device 10. Figure 14 An example of the operation of the thickness prediction device 10 in the case of laser quenching is shown, but the operation is the same in the case of laser cladding.

[0082] In step S11, the laser irradiation condition acquisition unit 11 determines whether it can acquire laser irradiation conditions that include the processing speed and laser output conditions at each position on the surface of the workpiece. If the laser irradiation condition acquisition unit 11 can acquire the laser irradiation conditions ("Yes" in step S11), it acquires the laser irradiation conditions and outputs the acquired laser irradiation conditions to the surface modification layer thickness prediction unit 13. Then, the material information acquisition unit 12 performs the operation of step S12. If the laser irradiation condition acquisition unit 11 cannot acquire the laser irradiation conditions ("No" in step S11), it performs the processing of step S11 again.

[0083] In step S12, the material information acquisition unit 12 determines whether it can acquire the material information of the workpiece. The material information is, for example, S45C. If the material information acquisition unit 12 can acquire the material information ("Yes" in step S12), it acquires the material information and outputs the acquired material information to the surface modification layer thickness prediction unit 13. Then, the surface modification layer thickness prediction unit 13 performs the operation of step S13. If the material information cannot be acquired ("No" in step S12), the material information acquisition unit 12 executes the processing of step S12 again. Step S12 can be performed before step S11 or in parallel with step S11.

[0084] In step S13, the surface modification layer thickness prediction unit 13 predicts the thickness of the surface modification layer at each location of the workpiece based on the laser irradiation conditions and material information.

[0085] In step S14, the display unit 14 displays the thickness of the surface modified layer predicted by the surface modified layer thickness prediction unit 13.

[0086] The thickness prediction device 10 of this embodiment described above can predict the thickness of the surface modification layer without the need for additional pre-processing confirmation or in-process or post-processing inspection procedures.

[0087] (Second Implementation)

[0088] Figure 15 This is a block diagram illustrating a structural example of the thickness prediction device according to the second embodiment of this disclosure.

[0089] like Figure 15 As shown, in addition to the structure of the thickness prediction device 10, the thickness prediction device 10A also includes a CNC (Computerized Numerical Control) data storage unit 15, a machining simulation execution unit 16, and a workpiece setting information acquisition unit 17.

[0090] The CNC data storage unit 15 stores CNC data including parameter settings and machining programs. Among the parameter settings are, for example, parameters related to speed control. Examples of speed control-related parameter settings include the time constants for the acceleration and deceleration of the cutting feed on each of the X, Y, and Z axes.

[0091] Figure 16 An example representing a processing procedure.

[0092] exist Figure 16 In the machining program shown, S represents the power command (W), P represents the frequency command (Hz), Q represents the duty cycle command (%), and F represents the feed rate command (mm / min).

[0093] The machining simulation execution unit 16 performs machining simulation based on CNC data, generating point data along the machining path that includes at least coordinate values, feed rate, and laser output conditions. In the case of laser cladding, the machining simulation execution unit 16 generates point data along the machining path that includes at least coordinate values, feed rate, laser output conditions, and additional material supply conditions.

[0094] The workpiece setting information acquisition unit 17 acquires workpiece setting information, including at least the shape of the workpiece and its relative position to the machining path. The shape of the workpiece can be obtained from CAD (Computer-aided design) / CAM (Computer-aided manufacturing). The relative position of the workpiece to the machining path can be calculated as follows. Typically, during machining preparation, after setting the workpiece, a contact probe or the like is used to measure the workpiece origin relative to the center of the workpiece's machining origin, and the measurement results are used to set the coordinate system offset (workpiece coordinate system offset). This offset becomes the relative position of the workpiece to the machining path. Figure 17 In this context, the relative position (x', y', z') is equivalent to the relative position. Figure 18 This indicates the machining path relative to the machining origin.

[0095] The laser irradiation condition acquisition unit 11 calculates the laser irradiation conditions on the surface of the workpiece based on the point data and the workpiece setting information.

[0096] The thickness prediction device 10A described above can predict the thickness of the surface modification layer based on the results of processing simulation before processing.

[0097] (Third Implementation)

[0098] Figure 19 This is a block diagram illustrating a structural example of the thickness prediction device according to the third embodiment of this disclosure.

[0099] like Figure 19 As shown, compared with the thickness prediction device 10A, the thickness prediction device 10B removes the CNC data storage unit 15 and the machining simulation execution unit 16, and adds a motion data acquisition unit 18.

[0100] The motion data acquisition unit 18 acquires motion data obtained during processing, including at least coordinate values, feed rate, and laser output conditions. In the case of laser cladding, the motion data acquisition unit 18 acquires motion data obtained during processing, including at least coordinate values, feed rate, laser output conditions, and additional material supply conditions.

[0101] The laser irradiation condition acquisition unit 11 calculates the laser irradiation conditions on the surface of the workpiece based on motion data and workpiece setting information.

[0102] The thickness prediction device 10B described above can predict the thickness of the surface modification layer based on action data during or after processing.

[0103] (Fourth Implementation)

[0104] Figure 20 This is a block diagram illustrating a structural example of the thickness prediction device according to the fourth embodiment of this disclosure.

[0105] like Figure 20 As shown, the thickness prediction device 10C, in addition to having the structure of the thickness prediction device 10, also has a designated position determination unit 19.

[0106] The designated position determination unit 19 determines the position on the surface of the workpiece. The position on the surface of the workpiece is determined by the user through an input device such as a keyboard or a touch panel on an LCD display device, or it can be determined by storing the designated position in advance in the storage unit.

[0107] Display unit 14 displays the thickness of the surface modification layer at the determined location. Figure 21 An example showing the thickness of the surface modification layer at the determined location. Figure 21 The figure shows that at the determined location, the thickness of the surface modification layer is 1.5 mm from the surface.

[0108] The thickness prediction device 10C described above can predict the thickness of the surface modification layer at the determined location.

[0109] In order to implement the functional blocks included in the thickness prediction device in each embodiment, the thickness prediction device can be implemented by hardware, software, or a combination thereof. Here, implementation by software means implementation by reading and executing a program by a computer.

[0110] To implement the structural components of the thickness prediction device through software or a combination thereof, the thickness prediction device includes a processing unit such as a CPU (Central Processing Unit). The processing unit functions as an execution unit. In addition, the thickness prediction device also includes auxiliary storage devices such as HDDs (Hard Disk Drives) that store various control programs such as application software or operating systems (OS), and main storage devices such as RAM (Random Access Memory) that store data temporarily needed by the processing unit when executing programs.

[0111] Furthermore, the processing unit of the thickness prediction device reads application software or operating system from the auxiliary storage device, expands the read application software or operating system in the main storage device, and performs calculations based on the application software or operating system. Additionally, based on the calculation results, it controls various hardware components of the thickness prediction device. Thus, the functional blocks of this embodiment are implemented.

[0112] The structural components of a thickness prediction device can be implemented using hardware, including electronic circuits. When a thickness prediction device is constructed from hardware, for example, integrated circuits (ICs) such as ASICs (Application Specific Integrated Circuits), gate arrays, FPGAs (Field Programmable Gate Arrays), and CPLDs (Complex Programmable Logic Devices) can constitute part or all of the functionality of the structural components of the thickness prediction device.

[0113] Programs can be stored and provided to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., hard disk drives), optical-magnetic recording media (e.g., optical discs), CD-ROMs (Read-Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash memory ROMs, and RAMs (Random Access Memory)). Additionally, programs can also be provided to a computer using various types of transient computer-readable media.

[0114] According to the thickness prediction apparatus and thickness prediction method of this disclosure, which include the embodiments described above, the thickness of the surface modification layer can be predicted without the need for additional pre-processing confirmation or in-process or post-processing inspection steps.

[0115] The above-described embodiments are preferred embodiments of the present invention, but the scope of the present invention is not limited to the above-described embodiments. Various modifications can be made without departing from the spirit of the present invention.

[0116] For example, the thickness prediction device and thickness prediction method used in laser quenching or laser cladding can also be applied to spraying and the manufacturing methods described in Patent Document 1 and Patent Document 2.

[0117] Regarding the above-described embodiments, the following notes are further disclosed.

[0118] (Postscript 1)

[0119] A device for predicting the thickness of a surface modification layer on a workpiece formed using a laser, wherein...

[0120] The thickness prediction device includes:

[0121] The laser irradiation condition acquisition unit acquires laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece.

[0122] The material information acquisition unit acquires the material information of the workpiece.

[0123] The surface modification layer thickness prediction unit predicts the thickness of the surface modification layer at various locations on the workpiece based on the laser irradiation conditions and the material information.

[0124] (Postscript 2)

[0125] According to the thickness prediction device described in Appendix 1, wherein,

[0126] The thickness prediction device includes a display unit that displays the thickness of the surface modification layer.

[0127] (Note 3)

[0128] According to the thickness prediction device described in Appendix 1, wherein,

[0129] The thickness prediction device includes:

[0130] The machining simulation execution unit generates point data along the machining path, which includes at least coordinate values, feed rate, and laser output conditions, based on CNC data containing at least parameter settings and machining programs.

[0131] The workpiece setting information acquisition unit acquires workpiece setting information that includes at least the shape of the workpiece and the relative position of the workpiece to the processing path.

[0132] The laser irradiation condition acquisition unit calculates the laser irradiation conditions on the surface of the workpiece based at least on the point data and the workpiece setting information.

[0133] (Note 4)

[0134] According to the thickness prediction device described in Appendix 1, wherein,

[0135] The thickness prediction device includes:

[0136] The motion data acquisition unit acquires motion data obtained during processing, including at least coordinate values, feed rate, and laser output conditions.

[0137] The workpiece setting information acquisition unit acquires workpiece setting information that includes at least the shape of the workpiece and the relative position of the workpiece to the processing path.

[0138] The laser irradiation condition acquisition unit calculates the laser irradiation conditions on the surface of the workpiece based at least on the motion data and the workpiece setting information.

[0139] (Note 5)

[0140] According to the thickness prediction device described in Appendix 2, wherein,

[0141] The thickness prediction device includes a designated position determination unit that determines the position on the surface of the workpiece.

[0142] The display unit shows the thickness of the surface modification layer at the determined location.

[0143] (Note 6)

[0144] According to the thickness prediction device described in Appendix 3, wherein...

[0145] The machining simulation execution unit generates point data along the machining path that includes at least coordinate values, laser output conditions, feed speed, and additional material supply conditions.

[0146] (Note 7)

[0147] The thickness prediction device according to any one of Appendices 1 to 6, wherein,

[0148] The thickness of the surface-modified layer is displayed numerically.

[0149] (Postscript 8)

[0150] The thickness prediction device according to any one of Appendices 1 to 6, wherein,

[0151] The thickness of the surface modification layer is indicated by changes in the way the workpiece is displayed.

[0152] (Note 9)

[0153] A method for predicting the thickness of a surface-modified layer, wherein,

[0154] The computer, acting as a thickness prediction device for the surface modification layer of a workpiece formed using a laser, performs the following processing:

[0155] A process for obtaining laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece;

[0156] Processing to obtain the material information of the workpiece;

[0157] The process of predicting the thickness of the surface modification layer at each location of the workpiece based on the laser irradiation conditions and the material information.

[0158] Explanation of reference numerals in the attached figures

[0159] Thickness prediction devices 10, 10A, 10B, 10C

[0160] 11. Laser Irradiation Condition Acquisition Section

[0161] 12. Material Information Acquisition Department

[0162] 13. Surface Modification Layer Thickness Prediction Unit

[0163] 14 Display Section

[0164] 15 CNC Data Storage Department

[0165] 16. Machining Simulation Execution Department

[0166] 17. Workpiece Setting Information Acquisition Department

[0167] 18 Motion Data Acquisition Department

[0168] 19. Designated location determination section.

Claims

1. A device for predicting the thickness of a surface modification layer on a workpiece formed using a laser, characterized in that, The thickness prediction device includes: The laser irradiation condition acquisition unit acquires laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece. The material information acquisition unit acquires the material information of the workpiece. The surface modification layer thickness prediction unit predicts the thickness of the surface modification layer at various locations on the workpiece based on the laser irradiation conditions and the material information.

2. The thickness prediction device according to claim 1, characterized in that, The thickness prediction device includes a display unit that displays the thickness of the surface modification layer.

3. The thickness prediction device according to claim 1, characterized in that, The thickness prediction device includes: The machining simulation execution unit generates point data along the machining path, which includes at least coordinate values, feed rate, and laser output conditions, based on CNC data containing at least parameter settings and machining programs. The workpiece setting information acquisition unit acquires workpiece setting information that includes at least the shape of the workpiece and the relative position of the workpiece to the processing path. The laser irradiation condition acquisition unit calculates the laser irradiation conditions on the surface of the workpiece based at least on the point data and the workpiece setting information.

4. The thickness prediction device according to claim 1, characterized in that, The thickness prediction device includes: The motion data acquisition unit acquires motion data obtained during processing, including at least coordinate values, feed rate, and laser output conditions. The workpiece setting information acquisition unit acquires workpiece setting information that includes at least the shape of the workpiece and the relative position of the workpiece to the processing path. The laser irradiation condition acquisition unit calculates the laser irradiation conditions on the surface of the workpiece based at least on the motion data and the workpiece setting information.

5. The thickness prediction device according to claim 2, characterized in that, The thickness prediction device includes a designated position determination unit that determines the position on the surface of the workpiece. The display unit shows the thickness of the surface modification layer at the determined location.

6. The thickness prediction device according to claim 3, characterized in that, The machining simulation execution unit generates point data along the machining path that includes at least coordinate values, laser output conditions, feed speed, and additional material supply conditions.

7. The thickness prediction device according to any one of claims 1 to 6, characterized in that, The thickness of the surface modification layer is displayed numerically.

8. The thickness prediction device according to any one of claims 1 to 6, characterized in that, The thickness of the surface modification layer is indicated by changes in the way the workpiece is displayed.

9. A method for predicting the thickness of a surface-modified layer, characterized in that, The computer, acting as a thickness prediction device for the surface modification layer of a workpiece formed using laser, performs the following processing: A process for obtaining laser irradiation conditions that include at least the processing speed and laser output conditions at each position on the surface of the workpiece; Processing to obtain the material information of the workpiece; The process of predicting the thickness of the surface modification layer at each location of the workpiece based on the laser irradiation conditions and the material information.

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