Method for determining factors influencing workpiece surface roughness measurement precision

Abstract

The invention relates to the field of computers, and particularly discloses a method for determining factors influencing workpiece surface roughness measurement precision, which comprises the following steps of: responding to an operation of setting workpiece surface roughness, generating a one-dimensional section of the workpiece surface by utilizing a Monte Carlo method based on the set roughness, and simulating particles in a two-dimensional circle, constructing a two-dimensional finite element simulation model for measuring the surface roughness of the workpiece based on machine vision; simulating the constructed two-dimensional finite element simulation model in response to an operation of setting particulate matter parameters and / or light source parameters, and acquiring light wave energy data of the energy acquisition boundary; based on the light wave energy data obtained under different parameters, factors influencing the workpiece surface roughness measurement precision are determined. Therefore, the invention provides a method for determining the factors influencing the measurement precision of the surface roughness of the workpiece, so that the precision of measuring the surface roughness of the workpiece by utilizing machine vision can be improved by reducing the influence of environmental factors on machine vision measurement.

Description

Technical Field

[0001] This invention relates to the field of computers, and more particularly to a method for determining the factors affecting the accuracy of workpiece surface roughness measurement. Background Technology

[0002] Surface roughness is an important parameter characterizing the microscopic geometric features of a surface, and it has a significant impact on the assembly performance, frictional properties, light scattering properties, biocompatibility, and service life of mechanical products. Therefore, accurate, efficient, and reliable measurement of workpiece surface roughness has important theoretical and engineering significance for modern manufacturing.

[0003] Surface roughness measurement methods are mainly divided into two categories: contact and non-contact. Stylus profilometry is a representative example of a contact-based surface roughness measurement method. However, this method easily scratches the workpiece surface and can only be used for one-dimensional (1D) measurements, making it difficult to use in industrial online inspection. Non-contact surface roughness measurement methods can be broadly classified into capacitance methods, acoustic methods, and optical methods. For ultra-precision machined surfaces, atomic force microscopy and optical interferometry can provide nanometer-level measurements, but their complexity, the need for expensive equipment, and the requirement for strictly controlled environments limit their applicability in engineering-related fields.

[0004] Visual roughness measurement, based on optical principles, involves using a camera to capture light scattered by a rough surface, analyzing the image, designing features in the image that are correlated with roughness, and finally establishing a model relating these features to the roughness. This method offers advantages such as high information acquisition capacity, high flexibility, and high cost-effectiveness, but suffers from low accuracy.

[0005] Therefore, there is an urgent need for a method to determine the factors affecting the accuracy of workpiece surface roughness measurement to solve the above problems. Summary of the Invention

[0006] Therefore, the present invention provides a method for determining the factors affecting the accuracy of workpiece surface roughness measurement, in an attempt to solve or at least alleviate the problems mentioned above.

[0007] According to one aspect of the present invention, a method for determining factors affecting the accuracy of workpiece surface roughness measurement is provided, comprising: in response to an operation of setting the workpiece surface roughness, generating a one-dimensional profile of the workpiece surface using the Monte Carlo method based on the set roughness, and constructing a two-dimensional finite element simulation model for measuring workpiece surface roughness based on machine vision by using two-dimensional circular simulated particles; in response to an operation of setting particle parameters and / or light source parameters, simulating the two-dimensional finite element simulation model to obtain light wave energy data of the energy harvesting boundary; and determining the factors affecting the accuracy of workpiece surface roughness measurement based on the light wave energy data obtained under different parameters.

[0008] Optionally, in the method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention, the particulate matter parameters include particulate matter material, particulate matter size, particulate matter concentration, and particulate matter distribution, and the light source parameters include the wavelength of light.

[0009] Optionally, in the method for determining factors affecting the accuracy of workpiece surface roughness measurement according to the present invention, the factors affecting the accuracy of workpiece surface roughness measurement are determined based on the light wave energy data obtained under different parameters, including: determining whether the particulate material is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy acquisition boundary under each particulate material and the energy distribution of the energy acquisition boundary under each particulate material; determining whether the particle size is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy acquisition boundary under each particle size at multiple wavelengths; determining whether the particle concentration is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy acquisition boundary under each particle concentration at multiple wavelengths; and determining whether the particle distribution is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy acquisition boundary under each particle distribution at multiple particle concentrations.

[0010] Optionally, in the method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention, the factors affecting the accuracy of workpiece surface roughness measurement are determined based on the light wave energy data obtained under different parameters, and further include: determining whether the wavelength of light is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy acquisition boundary at each wavelength under multiple particle sizes.

[0011] Optionally, the method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention further includes: in response to an operation of changing the workpiece surface roughness, reconstructing a two-dimensional finite element simulation model for measuring workpiece surface roughness based on machine vision, based on the changed roughness; in response to an operation of setting particulate matter concentration, simulating the reconstructed two-dimensional finite element simulation model to obtain light wave energy data received by the energy harvesting boundary; performing the steps of reconstructing the simulation model and simulating the reconstructed simulation model multiple times; and detecting whether the influence of particulate matter concentration on the accuracy of workpiece surface roughness measurement is related to the roughness of the workpiece surface, based on the average energy received by the energy harvesting boundary at each roughness under multiple particulate matter concentrations.

[0012] Optionally, the method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention further includes: after determining each factor affecting the accuracy of workpiece surface roughness measurement, determining the influence of the influencing factor on the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary under each influencing factor.

[0013] According to another aspect of the present invention, an apparatus for determining factors affecting the accuracy of workpiece surface roughness measurement is provided, comprising: a construction module adapted to, in response to an operation of setting a workpiece surface roughness, generate a one-dimensional profile of the workpiece surface using the Monte Carlo method based on the set roughness, and construct a two-dimensional finite element simulation model for measuring workpiece surface roughness based on machine vision by using two-dimensional circular simulated particles; a simulation module adapted to, in response to an operation of setting particle parameters and / or light source parameters, simulate the two-dimensional finite element simulation model to obtain light wave energy data of the energy harvesting boundary; and a determination module adapted to, based on the light wave energy data obtained under different parameters, determine the factors affecting the accuracy of workpiece surface roughness measurement.

[0014] Optionally, in the device for determining factors affecting the accuracy of workpiece surface roughness measurement according to the present invention, the particulate matter parameters include particulate matter material, particulate matter size, particulate matter concentration, and particulate matter distribution, and the light source parameters include the wavelength of light.

[0015] According to another aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing a method for determining factors affecting the accuracy of workpiece surface roughness measurement according to the present invention.

[0016] According to another aspect of the invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the method for determining factors affecting the accuracy of workpiece surface roughness measurement according to the invention.

[0017] According to the method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention, a two-dimensional finite element simulation model for measuring workpiece surface roughness based on machine vision is automatically constructed based on a set workpiece surface roughness. Then, by simulating this model under different particle parameters and light source parameters, the factors affecting the accuracy of workpiece surface roughness measurement can be determined from the various parameters of the particles. Based on this, when using machine vision to measure workpiece surface roughness, the measurement accuracy can be improved by reducing the influence of environmental factors on machine vision measurement. Attached Figure Description

[0018] To achieve the foregoing and related objectives, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The foregoing and other objectives, features, and advantages of this disclosure will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings. Throughout this disclosure, the same reference numerals generally refer to the same parts or elements.

[0019] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the present invention is shown;

[0020] Figure 2 A flowchart of a method 200 for determining factors affecting the accuracy of workpiece surface roughness measurement according to an embodiment of the present invention is shown;

[0021] Figure 3 A schematic diagram of a two-dimensional finite element simulation model according to an embodiment of the present invention is shown;

[0022] Figure 4 A schematic diagram of the electric field modes of an energy receiving surface under different particulate materials according to an embodiment of the present invention is shown;

[0023] Figure 5 A schematic diagram showing the average energy profile of the energy receiving surface under different particulate materials according to an embodiment of the present invention is illustrated.

[0024] Figure 6 A schematic diagram of the average energy of an energy-receiving surface as a function of particle size and light wavelength is shown according to an embodiment of the present invention.

[0025] Figure 7 A schematic diagram of the average energy of an energy-receiving surface as a function of particulate matter concentration and light wavelength is shown according to an embodiment of the present invention.

[0026] Figure 8 A schematic diagram of the average energy of the energy receiving surface under different particulate distribution modes at multiple particulate concentrations is shown according to an embodiment of the present invention.

[0027] Figure 9 A schematic diagram of the average energy of an energy-receiving surface as a function of particulate matter concentration and workpiece surface roughness, according to an embodiment of the present invention, is shown.

[0028] Figure 10 A schematic diagram showing the light intensity attenuation stage of incident light to the energy harvesting surface according to an embodiment of the present invention is shown;

[0029] Figure 11A structural block diagram of a device 1100 for determining factors affecting the accuracy of workpiece surface roughness measurement according to an embodiment of the present invention is shown. Detailed Implementation

[0030] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0031] Grinding is one of the most widely used precision machining methods in the machinery manufacturing industry. It is generally used as the final process in precision machining of workpieces to ensure the shape accuracy and roughness requirements of the workpiece surface. Surface roughness has a significant impact on workpiece quality; therefore, accurate, efficient, and reliable measurement of workpiece surface roughness is crucial. Among these methods, machine vision-based roughness detection has gained increasing attention in recent years. However, current machine vision-based methods for measuring the surface roughness of ultra-precision ground surfaces neglect the influence of environmental factors on the surface roughness measured by machine vision. Therefore, this invention provides a method for determining the factors affecting the accuracy of workpiece surface roughness measurement.

[0032] The method for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention can be executed in a computing device. Figure 1 A block diagram of the physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 may be implemented as a processor. The system memory 104 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memory. According to one aspect, the system memory 104 includes an operating system 105 and a program module 106, the program module 106 including a factor determination device 1100 for determining factors affecting the accuracy of workpiece surface roughness measurement, the factor determination device 1100 being configured to perform the factor determination method 200 for determining the accuracy of workpiece surface roughness measurement according to the present invention.

[0033] According to one aspect, operating system 105 is, for example, suitable for controlling the operation of computing device 100. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other applications, and are not limited to any particular application or system. Figure 1The basic configuration is illustrated by the components within the dashed lines 108. According to one aspect, the computing device 100 has additional features or functions. For example, according to one aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 1 The image is shown by removable storage 109 and non-removable storage 110.

[0034] As stated above, according to one aspect, a program module is stored in system memory 104. According to one aspect, the program module may include one or more applications. The present invention does not limit the type of application; for example, applications may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slideshow applications, drawing or computer-aided applications, web browser applications, etc.

[0035] According to one aspect, examples can be practiced on circuits including discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic components or a microprocessor. For example, it can be practiced via wherein... Figure 1 Each or many of the components shown can be implemented as an example by integrating a System-on-a-Chip (SOC) on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all integrated (or “burned in”) as a single integrated circuit onto a chip substrate. When operating via the SOC, the functions described herein can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 100. Embodiments of the invention can also be implemented using other techniques capable of performing logical operations (e.g., AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. Additionally, embodiments of the invention can be implemented within a general-purpose computer or in any other circuit or system.

[0036] According to one aspect, computing device 100 may also have one or more input devices 112, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 114, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. Computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuitry; Universal Serial Bus (USB), parallel and / or serial ports.

[0037] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 104, removable storage 109, and non-removable storage 110 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computing device 100. According to one aspect, any such computer storage medium can be part of computing device 100. Computer storage media does not include carrier waves or other transmitted data signals.

[0038] According to one aspect, a communication medium is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, a communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0039] Figure 2 A flowchart of a method 200 for determining factors affecting the accuracy of workpiece surface roughness measurement according to an embodiment of the present invention is shown. Method 200 is adapted to be used on a computing device (e.g., Figure 1 It can be executed in the computing device 100 shown, or further, it can be executed in a simulation platform residing in the computing device.

[0040] First, it should be noted that in the research on machine vision measurement of ultra-precision ground surface roughness, the applicant discovered that light interacts with particles, resulting in absorption and scattering effects. This means that the information carried by light after passing through a rough surface includes not only surface microstructure information but also information about environmental particles. Based on this, the applicant believes it is urgent to investigate the impact of particles on machine vision measurement of surface roughness. Therefore, this invention mainly focuses on the influence of airborne particles on machine vision measurement of workpiece surface roughness.

[0041] Furthermore, analyzing the impact of particulate matter on the light field is very difficult, the experiment is time-consuming, and establishing a roughness visual measurement experimental model is time-consuming and laborious. To address this, this invention uses electromagnetic wave scattering simulation to analyze the impact of particulate matter on visual measurement. In addition, the finite element method (FEM) is used to simulate the electromagnetic wave scattering of experimental variables for measuring surface roughness using machine vision technology. That is, the study of the impact of particulate matter on the surface roughness of workpieces measured by machine vision is based on electromagnetic scattering finite element simulation.

[0042] The simulation model is explained below. This invention uses a two-dimensional model for simulation. Considering that most natural and artificially ground surfaces are two-dimensional (2D) rough surfaces, and that the roughness of such surfaces generally only varies along one direction of the model, remaining almost constant in its perpendicular direction, this invention models the ultra-precision ground surface as a one-dimensional roughness surface model. For particulate matter, this invention mainly studies ultramicron particles, most of which are regular, uniform spheres; based on this, the particles are modeled as two-dimensional circles.

[0043] like Figure 2 As shown, the method 200 for determining the factors affecting the accuracy of workpiece surface roughness measurement according to the present invention begins at 210. The workpiece surface can be an ultra-precision ground SiC surface; however, this is merely an example and the present invention does not limit it.

[0044] In 210, in response to the operation of setting the surface roughness of the workpiece, a one-dimensional profile of the workpiece surface is generated using the Monte Carlo method based on the set roughness, and two-dimensional circular particles are used to simulate the particles, thereby constructing a two-dimensional finite element simulation model based on machine vision measurement of the workpiece surface roughness.

[0045] Regarding the setting of workpiece surface roughness, according to one embodiment of the present invention, an operation can be set to bring up the workpiece surface roughness setting interface, and then the user can set the surface roughness of the workpiece in the workpiece surface roughness setting interface. Specifically, the setting operation can be a preset key press operation or a preset control click operation. Thus, when the user presses the preset key or clicks the preset control, the workpiece surface roughness setting interface will be displayed. The workpiece surface roughness setting interface may include several preset roughness options and / or a custom roughness option, so that the user can set the surface roughness of the workpiece by selecting a preset roughness option or a custom roughness option. Further, in some embodiments, the workpiece surface roughness setting interface may also include a build button, so that after the user has set the surface roughness of the workpiece, clicking the build button can trigger the construction of a two-dimensional finite element simulation model.

[0046] Specifically, the Monte Carlo method is used to generate a one-dimensional profile of the workpiece surface. In particular, based on the Monte Carlo method, a one-dimensional profile of the workpiece surface is generated by superimposing numerous harmonics with independent Gaussian random variables, wherein the variance of the harmonic amplitude is proportional to the power spectral function of the wavenumber.

[0047] In some embodiments, a two-dimensional finite element simulation model based on machine vision measurement of workpiece surface roughness is constructed, including dividing the entire simulation space into two spatial domains: air (region 1) and sample block (region 2); simplifying the rough surfaces at the boundaries of the two spatial domains into one-dimensional profiles generated based on the Monte Carlo method; and classifying all remaining boundaries into two categories: one category is light source ports, and the rest are scattering boundaries. Figure 3 The illustration shows a two-dimensional finite element simulation model constructed according to an embodiment of the present invention, specifically including a port, particles, two spatial domains, a sample (a ground surface profile, i.e., a one-dimensional profile generated based on the Monte Carlo method), and an energy harvesting boundary (referred to as an energy receiving surface in some embodiments). The energy harvesting boundary simulates a camera sensor, specifically the camera's photosensitive chip, used to receive light energy.

[0048] According to one embodiment of the present invention, the dimensional parameters of the constructed two-dimensional finite element simulation model are shown in Table 1 below. Of course, the present invention is not limited thereto, and those skilled in the art can set them according to actual needs in specific embodiments.

[0049]

[0050] Table 1

[0051] The simulation model works as follows: a plane light wave emitted from the light source is used as the incident light. It passes through the air domain of region 1, is scattered by the simulated microscopic surface (i.e., the ground surface profile), and then the scattered light passes through the air domain of region 1 again, finally reaching the energy harvesting boundary. At this point, the distribution of the light field is read in the direction of the simulated camera sensor (i.e., at the energy harvesting boundary), and this data is used as the light wave energy data that triggers the camera sensor chip. In the simulation model, region 1 can be divided into two cases: air containing particulate matter and air without particulate matter. In the case where region 1 contains particulate matter, the particles are randomly and uniformly placed, and the physical properties of the particles can be set.

[0052] Furthermore, after constructing the two-dimensional finite element simulation model, according to one embodiment of the present invention, it can be imported into the finite element simulation software COMSOL Multiphysics. Further, in some embodiments, the mesh can be divided into 1 / 6 of the minimum wavelength. Of course, this is merely an example, and the present invention does not limit it.

[0053] Subsequently, at step 220, in response to the operation of setting particulate matter parameters and / or light source parameters, the constructed two-dimensional finite element simulation model is simulated to obtain the light wave energy data received at the energy harvesting boundary. That is, multiple simulations are performed by changing the particulate matter parameters and / or light source parameters in the model to obtain the corresponding simulation results. The particulate matter parameters include particulate material, particulate size, particulate concentration, and particulate distribution, while the light source parameters include the wavelength of light.

[0054] Regarding the setting of particle parameters and / or light source parameters, an operation can be set to bring up the parameter setting interface, where the user can then set each parameter. Here, the setting of particle distribution is explained; for the rest, please refer to the relevant description of workpiece surface roughness setting above. Specifically, regarding the setting of particle distribution, according to an embodiment of the present invention, the particle distribution can be set by inputting the coordinates of the center point of each particle (i.e., the center coordinates of a two-dimensional circle) in the parameter setting interface.

[0055] Furthermore, according to one embodiment of the present invention, the light wave energy data can be the average energy received at the energy acquisition boundary, specifically as follows:

[0056]

[0057] In the finite element simulation results, Energy(i,j) represents the energy value at each element node on the energy harvesting boundary, which is calculated using the electric field value. For the simulation model, due to the simplification of the one-dimensional profile, M=1 here, and N represents the number of element nodes on the energy harvesting boundary.

[0058] Subsequently, in step 230, based on the light wave energy data obtained under different parameters, the factors affecting the accuracy of workpiece surface roughness measurement are determined, as follows.

[0059] Based on the average energy received by the energy harvesting boundary under various particulate materials and the energy distribution of the energy harvesting boundary under various particulate materials, it is determined whether the particulate material is a factor affecting the accuracy of workpiece surface roughness measurement. That is, with all parameters except the particulate material unchanged, it is determined whether the particulate material is a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy of the energy harvesting boundary under different particulate materials and the energy distribution of the energy harvesting boundary under different particulate materials.

[0060] If the average energy of the energy harvesting boundary differs under different particulate materials, and the energy distribution of the energy harvesting boundary also differs under different particulate materials, then the particulate material is determined to be a factor affecting the accuracy of workpiece surface roughness measurement; otherwise, the particulate material is determined not to be a factor affecting the accuracy of workpiece surface roughness measurement. Of course, in some embodiments, the determination of whether particulate material is a factor affecting the accuracy of workpiece surface roughness measurement can be based solely on whether the average energy received by the energy harvesting boundary is the same under different particulate materials, or solely on whether the energy distribution of the energy harvesting boundary is the same under each particulate material. This invention does not limit this approach.

[0061] Based on the average energy received by the energy harvesting boundary at multiple wavelengths for each particle size, it is determined whether particle size is a factor affecting the accuracy of workpiece surface roughness measurement. That is, with all parameters except the wavelength of light and particle size fixed, the particle size is used to determine whether it affects the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary at different wavelengths for each particle size.

[0062] If the average energy received by the energy harvesting boundary at each wavelength differs for different particle sizes, then the particle size is determined to be a factor affecting the accuracy of workpiece surface roughness measurement; otherwise, it is determined not to be a factor affecting the accuracy of workpiece surface roughness measurement. Furthermore, for simplicity, in some embodiments, the particle size can be determined to be a factor affecting the accuracy of workpiece surface roughness measurement solely based on whether the average energy received by the energy harvesting boundary at a single wavelength is the same for different particle sizes. That is, except for particle size, other parameters are fixed, and the particle size is determined to be a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary at each particle size.

[0063] Based on the average energy received by the energy receiving surface at multiple wavelengths under various particulate concentrations, it is determined whether particulate concentration is a factor affecting the accuracy of workpiece surface roughness measurement. That is, with all parameters except the wavelength of light and particulate concentration fixed, the factor determining whether particulate concentration affects the accuracy of workpiece surface roughness measurement is based on the average energy received by the energy acquisition boundary at different wavelengths under various particulate concentrations.

[0064] If the average energy received by the energy harvesting boundary at different particle concentrations varies for each wavelength, then the particle concentration is determined to be a factor affecting the accuracy of workpiece surface roughness measurement; otherwise, it is determined not to be a factor affecting the accuracy of workpiece surface roughness measurement. Additionally, for simplicity, in some embodiments, the particle size can be determined to be a factor affecting the accuracy of workpiece surface roughness measurement solely based on whether the average energy received by the energy harvesting boundary at different particle concentrations for a single wavelength is the same. That is, except for particle concentration, other parameters are fixed, and the particle concentration is determined to be a factor affecting the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary at each particle concentration.

[0065] Based on the average energy received by the energy harvesting boundary under various particulate matter concentrations and distribution conditions, it is determined whether particulate matter distribution is a factor affecting the accuracy of workpiece surface roughness measurement. That is, with all parameters except particulate matter concentration and distribution fixed, the particulate matter distribution is used to determine whether it affects the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary under different particulate matter concentrations and distribution conditions.

[0066] If, at each particulate matter concentration, the average energy received by the energy harvesting boundary differs under different particulate matter distributions, then the particulate matter distribution is determined to be a factor affecting the accuracy of workpiece surface roughness measurement; conversely, if the average energy received by the energy harvesting boundary at a given particulate matter concentration is the same under different particulate matter distributions, then the particulate matter distribution is not considered a factor affecting the accuracy of workpiece surface roughness measurement. Furthermore, for simplicity, in some embodiments, the factor affecting the accuracy of workpiece surface roughness measurement can be determined solely based on whether the average energy received by the energy harvesting boundary at a given particulate matter concentration is the same under different particulate matter distributions. That is, except for particulate matter distribution, other parameters are fixed, and the factor affecting the accuracy of workpiece surface roughness measurement is determined based on the average energy received by the energy harvesting boundary under each particulate matter distribution.

[0067] According to one embodiment of the present invention, the wavelength of light can also be used to determine whether it affects the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary at each wavelength for multiple particle sizes. That is, except for particle size and wavelength, other parameters are fixed, and the wavelength of light is used to determine whether it affects the accuracy of workpiece surface roughness measurement based on the average energy received by the energy harvesting boundary at each wavelength for different particle sizes.

[0068] If the average energy received by the energy harvesting boundary at different wavelengths varies for each particle size, then the wavelength of light is determined to be a factor affecting the accuracy of workpiece surface roughness measurement; conversely, the wavelength of light is determined not to be a factor affecting the accuracy of workpiece surface roughness measurement. Additionally, for simplicity, in some embodiments, the determination of whether the wavelength of light affects the accuracy of workpiece surface roughness measurement can be based solely on whether the average energy received by the energy harvesting boundary at different wavelengths for a given particle size is the same. That is, with all parameters except the wavelength of light fixed, the determination of whether the wavelength of light affects the accuracy of workpiece surface roughness measurement is based on the average energy received by the energy harvesting boundary at each wavelength.

[0069] In some embodiments, if particulate matter concentration is determined to be a factor affecting the accuracy of workpiece surface roughness measurement, it can be further determined whether the influence of particulate matter concentration on the accuracy of workpiece surface roughness measurement is related to the surface roughness of the workpiece. This can be achieved by generating multiple one-dimensional profiles of the workpiece surface with varying roughness, constructing multiple simulation models based on these profiles, adjusting the particulate matter concentration for each simulation model, and finally determining whether the influence of particulate matter concentration on the accuracy of workpiece surface roughness measurement is related to the surface roughness of the workpiece based on the obtained simulation results. The specific details are as follows.

[0070] In response to an operation that changes the surface roughness of a workpiece, a two-dimensional finite element simulation model based on the machine vision measurement of the workpiece surface roughness is reconstructed. That is, when the user resets the roughness in the workpiece surface roughness setting interface, a one-dimensional profile of the workpiece surface is generated using the Monte Carlo method based on the reset roughness, and a two-dimensional finite element simulation model is reconstructed based on this profile. For details, please refer to the relevant descriptions above, which will not be repeated here.

[0071] Then, in response to the setting of particulate matter concentration, the reconstructed two-dimensional finite element simulation model is simulated to obtain the light energy data received by the energy harvesting boundary. That is, keeping other parameters constant, the particulate matter concentration is changed, and the reconstructed two-dimensional finite element simulation model is simulated multiple times to obtain the average energy received by the energy harvesting boundary at various particulate matter concentrations under this roughness.

[0072] Next, the steps of rebuilding the simulation model and simulating the rebuilt model are performed multiple times. That is, the surface roughness of the workpiece is adjusted multiple times, a simulation model is rebuilt for each roughness, and under each simulation model, the particle concentration is changed, and multiple simulations are performed. In this way, the average energy received by the energy harvesting boundary at each particle concentration under multiple roughnesses can be obtained.

[0073] Finally, based on the average energy received by the energy harvesting boundary at various particulate concentrations and roughnesses, it is determined whether the influence of particulate concentration on the measurement accuracy of workpiece surface roughness is related to the roughness of the workpiece surface.

[0074] It should be noted that in some embodiments, the same method can also be used to detect whether the influence of other parameters of particulate matter on the accuracy of workpiece surface roughness measurement is related to the roughness of the workpiece surface, which will not be elaborated here.

[0075] Furthermore, according to one embodiment of the present invention, after determining the factors affecting the accuracy of workpiece surface roughness measurement, the influence of the influencing factor on the accuracy of workpiece surface roughness measurement can be determined based on the average energy received by the energy acquisition boundary at multiple parameter values ​​of each influencing factor. For example, after determining that particulate matter concentration is an influencing factor, the average energy received by the energy acquisition boundary at various particulate matter concentrations can be compared to detect whether an increase in particulate matter concentration increases or decreases the accuracy of workpiece surface roughness measurement. Other parameters such as particulate matter size and light wavelength can also be analyzed in this way, which will not be elaborated here.

[0076] Furthermore, to better understand this invention, an example is provided. All simulations were performed on the same computing platform: Windows 10, Matlab 2020a, and COMSOL Multiphysics 6.0. The platform has a CPU frequency of 3.5 GHz and 8 GB of RAM. The simulation variables are shown in Table 2 below.

[0077]

[0078] Table 2

[0079] 1. Particulate materials and particle size D

[0080] Figure 4 and Figure 5 The electric field mode diagrams and average energy diagrams of the energy receiving surface of different materials are shown for D = 4 μm, N = 3, λ = 550 nm and the sample surface is a smooth SiC surface, respectively (color diagrams are used here for easy differentiation). Figure 6 The average energy plot of the energy-receiving surface as a function of D and λ is shown when the particulate material is SiO2, N is 3, and the sample surface is a smooth SiC surface.

[0081] It is evident that: 1) Different particulate materials affect the energy of the energy-receiving surface. From Figure 4 This shows that, all other things being equal, different particulate materials affect the distribution of light energy on the energy-receiving surface. From... Figure 5It can be concluded that different particulate materials have different average energy values ​​at the energy-receiving surface. H₂O has a higher average energy, and since H₂O particles are transparent, more light will pass through them. Different particulate materials affect the extinction coefficient, leading to different transmittance, which in turn affects the transmitted light energy, ultimately impacting the energy at the energy-receiving surface. Therefore, under different environmental conditions, different particulate materials will significantly affect the measurement results. Based on this, when conducting visual measurements, it is necessary to consider the presence of different particulate materials in the surrounding environment.

[0082] 2) The particle size D affects the energy received by the energy receiving surface. Under the same conditions, the larger D is, the lower the average energy of the energy receiving surface. Specifically, simulations of different λ and D show that as D changes from 2 μm to 7 μm, the area of ​​light obstruction by particles along the incident and reflected light paths increases, with most of the light being scattered to the sides, resulting in less energy reaching the energy receiving surface. Figure 6 From the average energy diagram of the energy receiving surface, it can be seen that the average energy decreases as D increases. Furthermore, different values ​​of λ also affect the average energy. D and λ affect the extinction coefficient, leading to different transmittances, while λ also affects the mirror reflectance. When D and λ are different, incident light of the same energy will reach the energy harvesting surface with different energies due to different transmittances and mirror reflectances. Figure 6 It can be concluded that, given a particle diameter, green and blue light sources have higher energy than red light sources. Therefore, green and blue light sources are more advantageous than red light sources when improving measurement accuracy.

[0083] 2. Particle concentration N and location

[0084] Figure 7 This diagram shows the average energy of the energy-receiving surface as N and λ change when the particulate material is SiO2, D is 4 μm, and the sample surface is a smooth SiC surface. Figure 8 The average energy map of the energy receiving surface at different particle locations under different N values ​​is shown when the particulate material is SiO2, D is 4 μm, λ is 550 nm, and the sample surface is a smooth SiC surface. (Color maps are used here for easy differentiation.)

[0085] It can be seen that: 1) As the particulate matter concentration N increases, the average energy of the energy receiving surface tends to decrease, and the average energy varies when different values ​​of λ are used. From Figure 7It can be concluded that, overall, the average energy is relatively stable when λ is 550nm for green light, while it fluctuates significantly when λ is 400nm for blue light and 700nm for red light. Locally, the average energy is relatively stable when N is between 1 and 3, but fluctuates significantly when N is greater than 3. That is, changes in particle concentration N will affect the visual measurement results.

[0086] 2) As the particulate matter concentration N increases, the impact of different particulate matter locations on the average energy of the energy harvesting surface becomes more significant. Since airborne particulate matter is constantly in motion, a change in the particulate matter placement method was used to simulate this motion, thus simplifying the simulation of its impact on the energy receiving surface. In the four simulation models with different particulate matter concentrations N, the particulate matter was uniformly placed. Figure 8 It can be concluded that when the particulate matter concentration N is the same, the average energy of the energy-receiving surface will be different under the four placement methods, and the average energy decreases as the concentration increases. Among them, the average energy of particulate matter concentration N of 3 and placement method one is lower than that of particulate matter concentration N of 4 and placement method two, which explains the reason for the drastic fluctuation of average energy when the particulate matter concentration N is between 3 and 5.

[0087] 3. Particulate matter concentration N and different surface roughness

[0088] Figure 9 The average energy diagram of the energy receiving surface of different roughness surfaces under different N values ​​is shown when the particulate material is SiO2, D is 4 μm and λ is 550 nm.

[0089] It can be seen that the average energy of the energy receiving surface will change with R. a The value decreases as it increases. From Figure 9 From this, we can conclude that when 0 ≤ R a When ≤0.1μm, R a The average energy of the energy receiving surface exhibits good monotonicity. As the particulate matter concentration N increases, the slope of the line graph gradually flattens, and the roughness resolution decreases. This means that when N increases to a certain level, the roughness resolution will approach 0, and visual methods will become unusable.

[0090] In summary, the factors considered include particulate material, particle size D, particle concentration N, light wavelength λ, and surface roughness R. a Both factors affect the average energy of the energy harvesting surface. Specifically, as the particle size D and particle concentration N increase, the average energy of the energy harvesting surface tends to decrease. With R... a The impact of increasing particulate matter concentration on measurement results gradually decreases. This study provides a new approach to improving the accuracy of visual measurements of surface roughness by optimizing experimental design through controlling the surrounding environment, thereby reducing the influence of environmental factors on machine vision measurements.

[0091] Furthermore, this invention analyzes the propagation equation of incident light under the combined action of suspended particles and a rough surface based on the Lambert-Beer law of light transmission and the theory of light scattering. The light intensity function of the energy-receiving surface is derived from the fact that the incident light, after passing through the particles, is reflected by the rough surface, then passes through the particles again to reach the energy-receiving surface. Here, the energy-receiving surface is the photosensitive chip of a camera. To reduce the complexity of the analysis process, this embodiment assumes that: the particles are spherical; the particles are incoherent and uniformly distributed; the root-mean-square σ of the rough surface is less than the light wavelength λ; the surface microstructure follows a Gaussian distribution; and light scattering from particles and the surface entering the light-receiving surface is ignored.

[0092] The incident light goes through three stages to reach the energy receiving surface, such as... Figure 10 As shown. The first stage is when the incident light passes through the particles and reaches the rough surface. In this stage, when the incident beam passes through the particulate air, the particles in the air interact with the beam, producing scattering and absorption effects. The scattered light propagates in all directions, deviating from the original direction of propagation. The transmitted light propagates in the same direction as the original beam, but its intensity is attenuated due to the scattering and absorption effects of the particles. The second stage is when the light reaching the grinding surface in the first stage is scattered by the rough surface. The scattered light consists of specular reflection and diffuse reflection components. The intensity of the specular reflection component that reaches the receiving surface is attenuated again. The third stage is when the mirror-reflected light from the second stage passes through the particles again and reaches the energy receiving surface. In this stage, the light intensity is attenuated again by the particles. The process of light intensity attenuation in the above three stages can be explained using the Lambert-Beer law of light transmission and the principle of light scattering, as detailed below.

[0093] According to Lambert-Beer's law of light transmission, when a beam of light with intensity I0 emitted from a light source passes through a particle, the intensity of the transmitted light beam will be attenuated to a certain extent due to the absorption, reflection, and scattering of the particle. The outgoing light intensity I t1 The relationship with the incident light intensity I0 is as follows:

[0094] I t1 =I0f=I0exp(-αL)

[0095] In the formula, I t1 Let be the light intensity after passing through the particle, f be the transmittance, α be the attenuation coefficient or turbidity of the medium, the magnitude of which depends on the physical properties of the particle and the wavelength of the incident light, and L be the distance the light source travels through the particle. In the simplest case, if there are N spherical particles with a diameter of D per unit volume of scattering medium, then the turbidity α is:

[0096]

[0097] In the formula, D is the particle size of the particles contained in the medium, N is the number of particles per unit volume, and K is the extinction coefficient, which is related to the incident light wavelength, the refractive index m of the particles, and the particle size D.

[0098] Based on this, the transmittance f is:

[0099]

[0100] According to the principle of light scattering, when a beam of parallel monochromatic light shines on a rough surface, the reflectivity can be divided into two parts: one part comes from specular reflection, and the other part comes from diffuse reflection or scattering. Figure 10 This is a schematic diagram illustrating the light intensity attenuation stage of incident light reaching the energy harvesting surface. By placing the light-receiving surface in the position shown in the diagram to receive the reflected light intensity, the received reflected light is specularly reflected light. Therefore, the incident angle θ is taken as... i and scattering angle θ s Equal, according to scattering theory, the light intensity I in the direction of specular reflection is equal. s The relationship between the surface roughness σ and the surface roughness is as follows:

[0101]

[0102] In the formula, I s Let θ be the intensity of the reflected light, R be the mirror reflectivity, I0 be the intensity of the incident light, η be a constant related to the surface material's reflectivity, σ be the root mean square of the rough surface, and λ be the wavelength of the incident light. i =θ s =45°, the simplified expression for specular reflectivity R can be obtained as:

[0103]

[0104] The incident light passes through the particles, reaches the rough surface, is reflected, then passes through the particles again, and reaches the receiving surface. The final energy at the receiving surface is given by the following formula:

[0105]

[0106] Among them, surface roughness R a The relationship with σ is R a =0.80σ.

[0107] In addition, the present invention also provides a device for determining the factors affecting the accuracy of workpiece surface roughness measurement. Figure 11 A structural block diagram of a device 1100 for determining factors affecting the accuracy of workpiece surface roughness measurement according to an embodiment of the present invention is shown, suitable for residing in a computing device 100. For example... Figure 11 As shown, the device 1100 includes a construction module 1110, a simulation module 1120, and a determination module 1130.

[0108] The construction module 1110 is adapted to respond to the operation of setting the surface roughness of the workpiece, and based on the set roughness, generates a one-dimensional profile of the workpiece surface using the Monte Carlo method, and constructs a two-dimensional finite element simulation model based on machine vision measurement of the workpiece surface roughness using two-dimensional circular particles.

[0109] The simulation module 1120 is adapted to simulate the constructed two-dimensional finite element simulation model in response to operations that set particulate matter parameters and / or light source parameters, and to acquire light wave energy data of the energy harvesting boundary. The particulate matter parameters include particulate material, particulate size, particulate concentration, and particulate distribution, while the light source parameters include the wavelength of light.

[0110] The determination module 1130 is suitable for determining the factors affecting the accuracy of workpiece surface roughness measurement based on optical energy data obtained under different parameters.

[0111] The specific details of the device 1100 for determining factors affecting the accuracy of workpiece surface roughness measurement according to the present invention are described in the section on... Figures 1 to 10 The details are disclosed in the description, and will not be repeated here.

[0112] The various techniques described herein can be implemented in combination with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, USB flash drive, floppy disk, CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into and executed by a machine such as a computer, the machine becomes an apparatus for practicing the present invention.

[0113] When the program code is executed on a programmable computer, the computing device generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; the processor is configured to execute the method of the present invention for determining factors affecting the accuracy of workpiece surface roughness measurement, according to instructions in the program code stored in the memory.

[0114] By way of example, and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals such as carrier waves or other transmission mechanisms, and includes any information delivery medium. Any combination of the above is also included within the scope of readable media.

[0115] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0116] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0117] It should be understood that, in order to simplify this disclosure and aid in understanding one or more aspects of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0118] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0119] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0120] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0121] Furthermore, some of the embodiments described herein are methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing the functions. Therefore, a processor having the necessary instructions for implementing the methods or method elements forms means for implementing the methods or method elements. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing the functions performed by elements for the purposes of carrying out the invention.

[0122] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0123] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A method for determining the factors affecting the accuracy of workpiece surface roughness measurement, comprising: In response to the operation of setting the surface roughness of the workpiece, a one-dimensional profile of the workpiece surface is generated using the Monte Carlo method based on the set roughness, and two-dimensional circular particles are used to simulate particles, thus constructing a two-dimensional finite element simulation model based on machine vision measurement of the workpiece surface roughness. In response to the operation of setting particulate matter parameters and / or light source parameters, the two-dimensional finite element simulation model is simulated to obtain the light wave energy data of the energy harvesting boundary; Based on the light wave energy data obtained under different parameters, the factors affecting the accuracy of workpiece surface roughness measurement are determined.

2. The method as described in claim 1, wherein, The particulate matter parameters include particulate matter material, particulate matter size, particulate matter concentration, and particulate matter distribution; the light source parameters include the wavelength of light.

3. The method as described in claim 1 or 2, wherein, The factors affecting the accuracy of workpiece surface roughness measurement, determined based on light wave energy data obtained under different parameters, include: Based on the average energy received by the energy harvesting boundary under each particulate material and the energy distribution of the energy harvesting boundary under each particulate material, it is determined whether the particulate material is a factor affecting the accuracy of workpiece surface roughness measurement. Based on the average energy received at the energy harvesting boundary under multiple wavelengths for each particle size, it is determined whether the particle size is a factor affecting the accuracy of workpiece surface roughness measurement. Based on the average energy received at the energy harvesting boundary under multiple wavelengths at various particulate concentrations, it is determined whether particulate concentration is a factor affecting the accuracy of workpiece surface roughness measurement; and Based on the average energy received by the energy harvesting boundary under various particulate matter concentrations and particulate matter distribution conditions, it is determined whether particulate matter distribution is a factor affecting the accuracy of workpiece surface roughness measurement.

4. The method according to any one of claims 1-3, wherein, The method of determining the factors affecting the accuracy of workpiece surface roughness measurement based on light wave energy data obtained under different parameters also includes: Based on the average energy received at each wavelength at the energy harvesting boundary for multiple particle sizes, it is determined whether the wavelength of light is a factor affecting the accuracy of workpiece surface roughness measurement.

5. The method according to any one of claims 1-4, further comprising: In response to the operation of changing the surface roughness of the workpiece, a two-dimensional finite element simulation model of the workpiece surface roughness based on machine vision measurement is reconstructed based on the changed roughness. In response to the setting of particulate matter concentration, the reconstructed two-dimensional finite element simulation model is simulated to obtain the light wave energy data received at the energy harvesting boundary; Perform the steps of rebuilding the simulation model and simulating the rebuilt simulation model multiple times; Based on the average energy received by the energy harvesting boundary at various particulate concentrations and roughnesses, the study investigates whether the influence of particulate concentration on the measurement accuracy of workpiece surface roughness is related to the surface roughness of the workpiece.

6. The method according to any one of claims 1-5, further comprising: After identifying the factors affecting the accuracy of workpiece surface roughness measurement, the influence of each factor on the accuracy of workpiece surface roughness measurement is determined based on the average energy received by the energy acquisition boundary under each influencing factor.

7. A device for determining factors affecting the accuracy of workpiece surface roughness measurement, comprising: The module is designed to respond to operations that set the surface roughness of a workpiece. Based on the set roughness, it generates a one-dimensional profile of the workpiece surface using the Monte Carlo method and uses two-dimensional circles to simulate particles, thus constructing a two-dimensional finite element simulation model based on machine vision measurement of the workpiece surface roughness. The simulation module is adapted to simulate the two-dimensional finite element simulation model in response to the operation of setting particulate parameters and / or light source parameters, and to obtain the light wave energy data of the energy harvesting boundary; The determination module is suitable for identifying factors affecting the accuracy of workpiece surface roughness measurement based on optical energy data obtained under different parameters.

8. The apparatus of claim 7, wherein, The particulate matter parameters include particulate matter material, particulate matter size, particulate matter concentration, and particulate matter distribution; the light source parameters include the wavelength of light.

9. A computing device, comprising: At least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the method as described in any one of claims 1-6.

10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method as described in any one of claims 1-6.

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