Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.
The technology of machine vision makes use of image data to investigate and inspect the component’s quality. The surface quality of the industrial elements is considered the crucial quality characteristic of ergonomic, functional, and aesthetic aspects. Machine vision techniques are used for the surface roughness characterization by making use of the concept that the image is embodied as the 2-D (two-dimensional) function of the image intensity, which is characterized by the two parameters: (1) the amount of light that hit of the surface and (2) amount of the light that reflects from the surface.
The amount of light that hits on the surface mainly relies on the illuminations. However, the light that reflects from the object or the surface is considered to be the function of surface texture or surface irregularities [
Several methods and techniques have been developed to measure the surface roughness of the machined components, ranging from ‘simple stylus probe instrument’ to that ‘sophisticated optical techniques. Therefore, Artificial intelligence (AI) based surface roughness measurement for the metal matrix composites (MMCs) is considered to be the most effective technique among several other conventional measurement techniques [
Many studies have been conducted to examine the surface roughness of a workpiece using machine vision as a substitute for roughness instruments. Machine vision is used to measure surface roughness by examining the distribution of scattered light from a rough surface. A camera captures the light scattering pattern, and the image is processed to characterize the surface.
The availability of high-resolution Charge Coupled Device (CCD) cameras, personal computer computational capacity, and digital data processing boosted the picture analysis possibilities. In combination with data processing using CCD cameras, many optical techniques produce a 3-dimensional (3-D) picture of the surface and roughness parameters related to 2-dimensional (2-D) profiles. The use of machine vision to create viable surface roughness devices is still in its early stages. The primary challenge is determining how to process the surface image in order to acquire the actual surface of the workpiece’s roughness [
Many roughness measurement estimation methods are expected to be used to measure the workpiece’s surface finish. Surface roughness measurement techniques are categorized as contact or noncontact depending on whether the measuring probe contacts the workpiece’s surface. The stylus or surface profile meter can be used in industry to measure the roughness of a workpiece. Even though this can be used as a conventional methodology for evaluating the roughness of a surface, a non-contact method is a more accurate alternative. Computer vision technology is one of the most promising non-contact methods for evaluating surface roughness in terms of accuracy and speed.
The topography and microstructure of any surface can be used to describe by measuring the surface characteristics. The depth and type of the altered material zone below the surface define the topography’s micro-geometrical qualities or texture and the microstructure [
Machine tool guiding errors, process mechanics, and process dynamics are the most common causes of surface imperfections. Form error, lay, waviness, roughness, and other texture qualities are used to identify texture features. The many aspects of surface texture are depicted in
Form errors, waviness, and roughness are all examples of surface errors. An integrated error profile is derived from the surface profile. Form errors are linked to the part’s overall geometry, and form variations can be measured with the right trace length and tools. Although the distinction between waviness and roughness is not well defined, standards include allowances to distinguish them in terms of wavelength. They are chosen based on the roughness value and manufacturing method. These wavelengths are referred to as cut-off wavelengths, and they highlight the difference between roughness and waviness. 0.025, 0.08, 0.25, 0.8, 2.5, and 8 mm are the standard values.
Many surface finish measurement techniques have been developed, ranging from a basic visual comparison that is subjective to a more sophisticated atomic force microscope that detects roughness in nanometers. The several classes of surface roughness measurement are shown in
The surface roughness is assessed using either visual observation or mechanical sensation in this technique. The specimen’s surface imperfections are compared to the surface of a known surface finish. The roughness measurement is subjective and depends on personal opinion. Surface roughness specimens made with the same process, material, and machining parameters are used in this technique. The comparison is made using a variety of methods.
This approach involves making direct contact with the surface using the inspection probe in order to assign a numerical value to the surface roughness. The stylus probe is moved over the surface by a skid that follows the profile of the surface. Following surface imperfections, the stylus probe moves vertically. Transducers take up and magnify the vertical movement of the stylus. The mechanical or electronic system keeps track of the stylus movement. The vertical movement of the stylus in terms of surface roughness is connected from the profile trace. Some of the most common stylus instruments are the profilometer, Tomlinson surface metre, Taylor-Hobson Talysurf, and Perthometer.
In 1933, Abbott and Firestone developed the first profilometer technology [
Time-consuming measurements with a stylus profilometer in 3D surface topography are a severe restriction, as shown in
Surface finish is measured using optical and non-optical technologies such as pneumatic gauges and thermal comparators. Optical techniques commonly employed to detect the fine surface quality have a lot of potential for non-destructive and online surface roughness measurements during manufacturing.
When a rough surface reflects a collimated beam of laser light, the radiation is scattered into an angular distribution according to the laws of physical optics. The intensity and the pattern of scattered radiation depend on roughness height, spatial wavelengths, and wavelength of light. In general, small spatial wavelength components diffract the light into large angles relative to the specular direction, and long spatial wavelength components diffract the light into small angles. This concept is applied in many optical techniques, some of which are explained below. When a rough surface reflects a collimated laser beam, the light is scattered into an angular distribution according to physical optics laws. The intensity and pattern of scattered radiation are determined by the roughness height, spatial wavelengths, and light wavelength. Tiny spatial wavelength components, in general, diffract light into large angles relative to the specular direction, while long spatial wavelength components diffract light into small angles. Many optical techniques use this notion, some of which are detailed below:
The most sensitive and delicate measurement probe is light. Light-emitting diodes (LEDs) and lasers are simple to make, while ultrasensitive photodetectors are simple to detect. For surface characterization, light has become an essential tool in nanometrology. As a result, optical techniques for line profiling and areal topography have been developed. These techniques can go close to the spatial resolution limit of diffraction. Optical procedures have the advantage of being non-destructive because they are noncontact. Optical imaging and microscopy technologies are also faster than contacting procedures that rely on the mechanical scanning of a contacting probe.
On the other hand, optical approaches are sensitive to a variety of surface characteristics in addition to surface height. Optical constants, surface slopes, small surface characteristics that generate diffraction, and deep valleys where multiple scattering may occur are all examples of these. Furthermore, stray light in the optical system caused by scattering from examined surfaces can reduce the accuracy of an optical profiling method. A vertical resolution of 0.1 nm is achieved using high-sensitivity technologies such as phase-shifting interferometric (PSI) microscopy [
As the demand for microelectronic components grows, their quality and surface finish requirements do. Advanced systems and sensors based on Micro-Electronics Mechanical Systems (MEMS) require micro parts, and measurement instruments with high resolution are required to assess the shape and finish of these parts. Surface assessment at the nanoscale is required. The Scanning Tunneling Microscope (STM) and Atomic Force Microscope (AFM) were created as a result of technical advancements (AFM). The Scanning Probe Microscope (SPM) is a mechanical probe microscope that scans an object in an areal space to detect surface morphology with atomic resolution [
AFM images produced on a 2D and 3D stamp [
3D-CT Technique: The computed tomography (CT) metrology employing X-rays is one of the newly created concepts in recent years. CT metrology is a technique for simultaneously measuring interior and exterior geometries in a wide range of items. As a result, the CT can be utilized as a basic inspection tool and as a measuring principle that provides precise geometrical data. Industry quality engineering is currently being revolutionized by CT [
The measured part’s internal and external 3D modeling is possible with the Metrotom CT equipment and Calypso software.
The conventional method to evaluate the surface properties is the contact-based method, which involves using a mechanical stylus tool. The stylus is a sharp tip of a diamond having a very small radius through which it touches traces the surface. Although this method has advantages like the mechanical method is easy to use and creates reliable measurements of the surface, but in the meanwhile the tip can also scratch the surface, causing damage to it [
Stylus based profilometer
Advantages: easy to use, surface independence, and stylus tip radius very small up to 20 nm [ Disadvantages: low speed of measurement, low resolution [
CMM coordinate technique
Advantages: high precision and accuracy, robustness, accurate measurement, and less labor required [ Disadvantages: very costly, less portable, problems with software [
Vertical scanning interferometry
Advantages: do not damage to sample, non-contact process, high resolution, and high accuracy. Disadvantages: exposure to the vibration and effects of the transparent thin film [
White-light interference microscopy
Advantages: fast speed, measure noncontinuous surfaces. Disadvantages: vertical scanning requires frequently consuming so much time, a complicated method [
Confocal white light microscopy
Advantages: can optically ‘section’ almost transparent materials, shallow field depth, out of focus glare eliminated [ Disadvantages: background noise, and scattering noise [
Atomic force microscopy
Advantages: generates 3D images [ Disadvantages: measurement uncertainty, complex geometry, and challenges of tip characterizations [
Digital holographic technique
Advantages: high accuracy and high efficacy [ Disadvantages: slower process, used for small objects, and does not change resolution [
Illumination types | Illumination specification | Authors | Involved machining | Remarks |
---|---|---|---|---|
Gaussian intensity profile | Fischer et al. [ |
Rolling | Nanometer-scale, in-process roughness inspection | |
Laser | Auxiliary equipment: dichroic mirror, galvanometer scanner, F-theta lens | Kwon et al. [ |
AM | Melt pool imaging, laser power monitoring |
Diffused light | Tele-lens with UV filters, CCD camera, surface roughness tester | Datta et al. [ |
Turning | Progressive wear monitoring |
Ambient light | Logitech C-910 high-resolution camera, specular light minimization | Al-Kindi et al. [ |
Milling | Both machine surface quality inspection and tool state evaluation |
Ring light | Microscopic ring LED illumination | Aminzadeh et al. [ |
AM | Image collected from every layer of AM parts |
Dome illumination | CMOS camera with miniature zoom monocular video microscope | Wang et al. [ |
Turning | Tool condition monitoring using machined surface images |
The systematic literature review was performed to explore the applications and research on machine vision using surface characterizations of any conventional and non-conventional produced parts using text mining analysis to recognize, evaluate, and analyze the published literature between 2017 and 2022. Primarily; a literature review is used to explore, choose, and evaluate related publications. It is described as a systematic, precise, and consistent process to recognize, evaluate, and combine the existing literature of documented work given by the researchers or authors. The review process usually involves multiple steps [
The research question to perform the systematic review are given as follows:
RQ1: What are machine vision methods of measuring surface characteristics, and how do they work?
RQ2: How are the machine vision methods different from the conventional evaluation methods?
RQ3: What are the advantages and limitations of the traditional and non-traditional methods for evaluating surface characteristics?
The strategy of search created for this paper contained: recognizing the keywords, searching resources, method of searching, and article selection criteria for the collection of existing and competent available articles related to the topic. The query for search used the Boolean operators who were: “machine vision techniques” or “computer vision techniques” or “machine learning” and “conventional evaluation methods” or “traditional methods of measurement” and “surface quality” or “surface characteristics” or “surface texture”. The terms used for the search were improved by lowering the synonyms while searching the databases because of the limitations of search terms.
To search for the appropriate and related articles, we performed a search by incorporating the keywords or the search terms in five databases, involving “ACM digital library, IEEE Xplore digital library, Science Direct, Springer Link, and Scopus.” These are the highly illustrative databases for scientific research and provide results directly relevant to our research topic and are comprised of a huge quantity of literature, such as review papers, journal papers, conference reports, books, etc.
Depending upon the study directions, the exclusion and inclusion criteria are defined below. The criteria for exclusion were used for the title, abstract, and list of keywords of the publication, but the inclusion criteria were applied for the full-text articles. Those articles were excluded which
Articles focusing on other than machine vision or computer vision technologies for evaluation of surface characteristics. Articles not provided in the English language.
Those articles were included in our study were:
Articles reporting machine vision or computer vision technologies, written in the English language. Articles about modifying the existing technique or introducing new techniques for the evaluation of surface characteristics.
The process of searching started with searching the publications from the databases described above using specific Boolean operators, and 10,145 articles were included. Then the articles were filtered depending on the exclusion criteria, and then only 3224 articles remained. Depending upon the inclusion criteria, only 200 articles were included for the review. Then, manual research was carried out to search for the additional sources according to the method described by [
Has the article focused on machine vision and clearly define the research aim? Has the newly introduced methodology improved the evaluation of the surface? Has the proposed methodology been clearly described? Has the design of the study been clearly presented?
This method employs a microcomputer-based vision system to analyze the pattern of scattered light from the surface to derive a roughness parameter. It is based on the analysis of the pattern of white light scattered from a surface. The microscopic waveform of the surface profile modulates the incident light beams into scattered beams whose intensities and scattering angles can be described as functions of the amplitudes and wavelengths of the surface topography. The information from the surface can be obtained by studying its light-scattering pattern. The generalized schematic arrangement of the setup used for machine vision studies [
A proposed methodology or architecture [
Machine vision is defined as the capture of image data, followed by computer processing and interpretation for a specific application. Machine vision is a fast-evolving technology with a focus on industrial inspection. Image acquisition and digitization, image processing and analysis, and interpretation are the three roles of a machine vision system [
A camera and a digitizing system are used to capture and digitize images. The camera is focused on the object of interest, and an image is created by dividing the viewing area into a matrix of discrete picture elements (pixels), each with a value proportional to the light intensity of that part of the scene. An Analog-Digital Converter converts each pixel’s intensity value into its analog-digital converter (ADC). In a binary vision, each pixel’s light intensity is converted to one of two colors: white or black, depending on whether the light intensity reaches a certain threshold. To create the grey scale image, a sophisticated vision system must be able to detect distinct shades of grey in the image. Surface and area characteristics can be determined reasonably with an eight-bit (28) memory of 256 intensity grey levels. Each frame of digitized pixel values is saved in a computer memory device known as a frame buffer. A frame is read at a rate of 30 frames per second. In most machine vision applications, two types of cameras are used. Vidicon cameras obtain relative pixels by focusing the picture onto a photoconductive surface and scanning the surface with an electron beam. Varying voltage levels correspond to different light intensities impacting different locations on the photoconductive surface. The electron beam reads the voltage level of each pixel throughout the scanning operation. The image is focused onto a 2-D array of very small, carefully spaced photosensitive components in solid-state cameras. The photosensitive elements make up the pixel matrix. Each element generates an electrical charge in response to the intensity of light impacting it. The charge is stored in a storage device made up of an array of storage elements that correspond to the photosensitive elements one-to-one. These charge values are read sequentially in a machine vision’s data processing and analysis function. Because of the time-lapse scanning, Vidicon cameras suffer from distortion in the image of a fast-moving object. Solid-state cameras are physically smaller, more robust, create a more reliable image, and thus have a wide range of applications in industries. Pixel arrays available in a variety of sizes, including 256 × 256, 512 × 512, 1035 × 1320. The more pixel elements and resolution it has, the better it can detect fine details and features in a picture. Another crucial consideration is lighting. For seeing the image using a machine vision system, the item should be well-illuminated and consistent across time. For machine vision applications, special lighting systems should be implemented, and the type of lighting changes depending on the type of inspection [
Decisions must be made based on the data captured and stored by the frame grabber. As a result, the image captured may not have all of the necessary information to make a judgment. For analyzing picture data in a machine vision system, a number of techniques have been developed:
The image must be interpreted using the extracted features for any application. The job of interpretation is to recognize the object or characteristic. Template matching and feature weighting are two typical interpretation strategies. The image is compared pixel by pixel with one or more features of the model image, which is saved as a template in template matching. Each feature (e.g., area, length, perimeter) is given a weight based on its importance, and the total score is compared to an ideal object stored in memory.
Machine vision is used extensively in manufacturing and other fields. Here are some of them:
Many attempts to employ integrated reflectivity of the surface as a surface evaluation method have been made in the past. This is how gloss meters work. The machine vision system can directly evaluate the surface picture, taking into account the surface’s reflectivity. Discrimination may be shown in the image intensity distribution of the different surface images with fixed illumination and camera arrangement. The surface picture is used to determine several intensity-based metrics that are then compared to the Ra value measured in m by the stylus instrument. A consistent and acceptable approach to surface roughness evaluation is always being sought in this sector. This paper is an attempt to investigate some of the variations in picture surface evaluation.
In Defect Analysis
A defect in any of these materials can appear during or after administration. Defect testing is constantly required to provide data for the development of surface efficacy, competency, and resilience. Consider artificial hip joints, which require a long life. Prospect hip substitute measures can be calculated by calculating the surface substance for wear, scrapes, and the profile of the artificial joint after it has been removed for substitution.
In-Process Control
To produce a final product, industrialists must manage processes. Surface estimation controls the process when precision in surface engineering is required; based on inspection results, the approach appears to be adequate.
Surface Roughness Measurement Concerns
Shape: Surface topology is the calculus of the attention region in its entirety. The adjournment confers to the request “Area of Interest.”
Roughness: Roughness of surface Ra calculates the roughness of the linear profile or the area by estimating the surface finish. The roughness of the surface area (Sa) is calculated as a line covering the full region in 3D optical profilometry.
Surface Asperity: Asperities are characteristic features. For the purposes of inaccuracy engineering, these asperities usually refer to submicron height and form irregularities. For asperity measurements, AFM and TEM & SEM have greater resolution and are commonly utilized.
Optical methods, including computer vision techniques, have a more significant potential for ‘surface characteristics measurement’ and a broader range of options. ‘Optical microscopy’, ‘light scattering techniques’, and ‘vision systems’ are some of the most common optical technologies for measuring surface quality. Two forms of light, “coherent and incoherent light,” are used in computer vision-based methods. Surface characteristics can also be measured using light that scatters or reflects from the surface [
Many experiments on the surface characterization of various machined surfaces have used scattering. Tian et al. [
The mutual interference of dispersed light generated by the uneven surface’s spatial variations produces a speckle picture of a coherent light beam (laser) projected over the rough surface. Numerous laser speckle methods for measuring surface roughness have recently appeared. The speckle images obtained can measure roughness because surface roughness causes ‘light scattering’; the speckle images obtained can measure roughness. To describe surface roughness, a researcher used a speckle contrast approach. The speckle pattern is created by lighting the rough surface with a He-Ne laser. Surface roughness measurements and characterization are judged based on the distinct parameters of the speckle pattern. ‘Surface roughness characterization’ is evaluated based on the contrast parameters of the speckle pattern. The contrast parameters are calculated by varying the intensities of the speckle image. Various studies have been conducted regarding surface roughness using statistical properties and the ‘distribution of speckle image intensity.’ The standard deviations of the intensity fluctuations in the speckle patterns were found to have a linear relationship with surface roughness values.
A step-by-step machine vision-based condition monitoring and surface roughness measurement process reviewed in this paper has been illustrated in
Machine vision-based procedures are appropriate for ‘online assessment of machined components’ surfaces and are considered safe for both the surfaces that are to be measured and the measurement system. In various studies, it has been observed that the obtained surface images using a vision system and quantified surface roughness using regression analysis. The surface image’s average grey value (Ga) was computed and calibrated using the stylus’s measured average surface roughness (Ra). Various authors have used the ‘Gray Level Co-occurrence Matrix (GLCM) procedure’ [
Nowadays, the manufacturing industry’s productivity needs high-quality NC, CNC, and automated machine shops widely used for higher productivity. Quality scrutiny of the product also requires higher productivity as a critical feature. Inspection methods are categorized into direct and indirect techniques. Besides machine vision, there exists a new and innovative technology that is used to analyze and calculate the products with the help of ‘CCD camera’ as well as the ‘image processing techniques’ such as ‘image acquisition’ first step in digital image processing, de-noising with filters and comparison between actual and accurate image, mapping in image, image processing technique. The main methods discussed in this section is surface characteristics measurement’. Vision-based measurements have great attention in industries due to their high capacity and faster measurement using hardware, camera, and sensors. In the inception of dimensional accuracy, geometry features surface finish are significant features in the machining area; newer measurement techniques optical measurement plays a vital role [
It has also been observed that surface characteristics, i.e., surface roughness, dimensional accuracy and flatness, and other surface flaws, are characterized and measured using the machine vision system [
The technology of machine vision is used to examine the quality of parts using image-based data. Surface texture can be characterized using this approach as its data is represented in the two-dimensional intensity of the image produced, which depends on the amount of light incident on the surface and the amount of light reflected. Hence, machine vision could provide a contactless and automated method of measuring surface roughness, which replaces conventional methods [
The other technique of roughness evaluation includes the use of atomic force microscopy, which images the surfaces depending on their hardness, smoothness, or roughness [
Similarly, the evaluation of the surface structure was performed on aluminum thin films by Mwema et al. [
Regarding the onsite roughness evaluations at the micro or nano level, the precision is hindered by the constraints of the principle of the instrumentation involving imaging with inadequate light, high-resolution techniques with microsecond level exposure, and in the case of microscopic imaging, the restriction of motion blur [
The machine vision process is done using the following procedure [ Image capturing: The first step in machine vision is the image capturing from the CCD camera when the light emits and hits on the source. The image is transformed into a digital image with the help of imaging sensors. Image Acquisition: This processing step converts the optimal image to that of the digital image by following three different procedural steps, which are (1) image sensing, (2) image data representation, and (3) digitization. Image Processing: This step is used to arrange the pixel values, and it changes these pixel values into a more appropriate form so that further processing can be done. It entails five distinct operators (1) global pattern, (2) point operation, (3) neighborhood operation, (4) temporal operation (5) geometric operation. Feature Extraction: It identifies the ‘inherent features’ of the item/image or object. Pattern Classification: It is considered the last and final step in machine vision processes. It determines the unknown image or the item from the available set of items.
‘Surface texture’ is considered to be a significant aspect of machine design. If the surface finishing is done poorly, it will affect the functional performance of various machined components. The ‘direct con-tact components’ like scratch cards and profile meters are used for surface roughness measurement in various industries, especially manufacturing [ Proper lighting and optics Image processing algorithm High computer configuration, i.e., speed, storage, and capacity, surface finish measurement procedure.
The surface finish measurement on machined components procedure is carried out as follows:
Image Capturing:
a. The light that is reflected from that of the machined surface is captured using a CCD camera. Surface nature and roughness are assessed using these images. Filtering the Image:
a. Image filtering is done via three steps as follows:
At first low-pass filter is applied over the original image to get the ‘low pass filtered image.’ This image is then deducted from that of the original one to acquire the ‘surface roughness image’. The filtered ‘surface roughness image’ is then quantified through the ‘grey level average’. Usually, these processes are done according to the 2D standard ‘ISO 11562-1996’. To get better computing efficiency, ‘Fourier transform’ can be used for image filtering.
The quantified and the binaries images have been analyzed in terms of ‘matrix form’ based on the light intensity. The following algorithm is followed:
The intensity of the white area is denoted by 1. The intensity of the black area is denoted by 0.
‘Surface Roughness’ is based on the ‘variation in the intensity values’ starting from 1 to 0. The following algorithm is considered to measure the ‘surface profile.’
It is started by scanning the first pixel of the first column in the image matrix. On obtaining 0, scanning is stopped, and the second row is considered. If the obtained value is not 0, then the second pixel is scanned in the row. The scanning process keeps on finding the 0 pixels in the first row. Scanning of the 0-value pixel is done in the second row. This process is repeated for each row.
The classification of various computer vision techniques to measure surface characteristics of any part manufactured by various traditional manufacturing, i.e., CNC machining, casting, forging, additive manufacturing (AM), and non-traditional manufacturing processes. Electric discharge machining (EDM), Laser surface processing (LSP), etc., are given below.
Metal machining surfaces via various procedures, for instance, milling, planning, grinding, or EDM, generate the particular lay pattern. For example, a milled surface comprises a typical periodic and regular layer pattern [ The peak amplitude or surface valley. Wavelength among the valleys and peaks.
The measurements of the surface are usually articulated as surface profile denoted as y(x) in 2-D and are expected to be equal to the 3-D expressions. The ‘average surface roughness parameter (Ra)’ denotes the average surface profile deviation in regard to the mean line. Ra is usually utilized for the measurement of sur-face roughness characterization and measurement. For several years, the ‘stylus instrument’ has been significantly utilized to measure the surface roughness parameters and the high-reliability percentage. The vertical tip movement of the stylus is calculated for the predetermined horizontal length. The ‘high-frequency components of surface roughness’ are filtered with the help of the stylus tip and also the non-linear deformation within the surface. Furthermore, the tip of the stylus may disrupt or may get disrupted when making contact with the surface that needs to be measured. The requirement for a non-contact, high-speed, and highly reliable surface measurement system is considered to be on the rise. Even though several techniques are there for the measurement of the ‘surface roughness,’ that also includes ‘optical techniques. It has been observed that no techniques have yet been established that are robust and reliable enough for floor applications. The technique of ‘biometric recognition’ has proven to be not only robust but reliable and is found highly recommendable for surface characterization. It comes under the non-contact method utilizing the surface imaging to calculate the Euclidean distance and the hamming distance of reference images and for testing the surface image to make the comparison. The ‘surface roughness measurements’ of the ‘reference surface’ are done using the ‘stylus method,’ and corresponding images have been saved within the database. Testing surface image is characterized based on Hamming as well as Euclidean distance [
The steps involved in the measurement of surface roughness using the image processing are as follows.
To perform image acquisition, a ‘Basler PiA2400gm CCD camera’ is fitted using a Zoom 6000 lens whose optical magnification can be done up to 45.0X. Besides this, a lighting system and two halogen bulbs are also used. The specimen is held using the adjustable table and set the cameras to some specific angles. The CCD camera is adjusted in angle to the specimen using a protractor located in the center. The images can be taken by adjusting the camera to different angles. It is then ensured that uniform illumination is there in the setup by diffusing the light source. Surface images are taken for all the specimens at different positions.
To perform the procedure, specimens are collected so that they can be saved in the database as reference images. For every specimen, various images are taken, and among these, one is saved and stored in the database; however, the rest are used as test images. Immediately after capturing it, images are dealt with the lighting. Fluctuations and variations in image acquisition can affect image processing. Through normalization, the image matrix is transformed to have the equal and uniform intensity of each captured image pixel.
To perform the surface characterization, feature extraction is done, and then a comparison is made using the two metrics, i.e., ‘Euclidean and Hamming distance.’ Up till now, these metrics have performed significantly in ‘iris recognition’ in human identification. The Euclidean distance is considered to be the spatial distance between the vectors, suppose p and q. It also measures the ‘dissimilarity’ among the two vectors, p, and q. If the Euclidean value is higher, then higher would be the value of dissimilarity. ‘The circular shaft-based matching’ removes the possibility of a ‘simple shift’ within the image, which could affect the Euclidean distance [
Types of descriptors | Involved machining operations | Year | Authors | Data | Specific techniques |
---|---|---|---|---|---|
Structural descriptors | Grinding, milling, shaping | 1993 | Ramamoorthy |
Machined surface images | Gray level histogram |
Turning | 2000 | Mannan |
Machined surface images, sound data | Sobel descriptor, thresholding-based segmentation, PSD | |
Turning | 2000 | Kassim |
Machined surface images | Sobel descriptor & thresholding | |
Turning | 2008 | Prasad |
Machined surface images | Amplitude parameters | |
Turning | 2010 | Wang |
Machined surface images | LOG operator, Hough transform | |
Grinding | 2017 | Zhao |
Machined surface images | Intensity histogram-based analysis | |
AM (Powder bed fusion) | 2019 | Zhang |
Machined surface images melt pool, plume & spatters | Median filtering, global thresholding, designed comparison function | |
First-order statistical descriptors | End milling | 2001 | Bradley |
Machined surface images | Intensity histogram, spatial domain texture descriptors |
Turning, face milling, polishing | 2004 | Gadelmawla |
Machined surface images | GLCM descriptors, the maximum width of the matrix | |
Milling | 2008 | Elango |
Scattered pattern Image of machined surface | Taguchi technique | |
Milling | 2012 | Ai-Kindi |
Machined surface Image | Histogram-based feature extraction | |
Turning, grinding, H-M, V-M, lapping, shaping | 2016 | Ashour |
Machined surface image | Histogram-based Feature extraction, | |
Laser welding | 2018 | Zhang |
Plume | Geometric features (area, perimeter, etc.) | |
Second-order statistical vdescriptors | Turning | 2012 | Dutta |
Machined surface images | GLCM descriptors, pixel pair spacing |
Turning | 2016 | Bhat |
Machined surface images | VT descriptors | |
Turning | 2016 | Dutta |
Machined surface images | VT & DWT descriptors | |
Turning | 2016 | Bhat |
Machined surface images | GLCM descriptors | |
Transformed domain descriptors | Textile fabrics, milling | 2000 | Tsai |
Machined surface images machined surface images | Gabor Filter |
Eight engineering processes comparison | 2001 | Bharati |
Seel surface image | FNW transform-based descriptors | |
Rolling | 2004 | Stachowiak |
Tribological damaged surface image | PLS-DA, 2D-FFT, MIA, WTA | |
Sandblasting, abrading | 2005 | Dutta |
Machined surface images | DWT, Gabor filter, and LBP descriptors | |
End milling | 2016 | Lei |
Machined surface images | DWT, GLCM descriptors |
Vision-based measurement in the industrial field has more attention due to fast measurement combined with cameras, hardware, and sensors [
In [
In [
First, Luk’s procedure analyses the influence of ambient light using the root mean square height parameter and a regular gray-level distribution differentiation, and a co-occurrence matrix solution. The input variables then suggest the new RBF neural network, which is the average grey value for the context sample area, the average gray-scale value, and the second-order of the work-piece sample area for the co-occurrence matrix, and their corresponding inspection values. This approach used five shafts and II shafts for the neural network training.
Yao et al. [
Patel et al. [
In [
Joshi et al. [
In [
Kamguem et al. [
Chethan et al. [
Due to surface consistency, the surfaces produced are analyzed using a vision method using a Ga parameter with shaping, milling, and grinding with a surface finishing range of Ra 0.3 to 30 μm. Just regular exemplars use the stylus instrument to calibrate the Ra values. However, in most cases, Ga has an excellent relationship with Ra must be stated in particular.
In [
Jeyapoovan et al. [
Dhanapalan et al. [
Vision-based surface roughness evaluation system for end milling in this paper digital reconstruction and calibration of inspected surface and qualitative evaluation of surface texture. Vision-based results vary from 9% to 11% compared to the stylus-based ones. Spacing parameters were also implemented including autocorrelation length and angular power spectral density function. Cusp lines and tool marks and analysis on further evaluation of surface texture. Texture evaluations are implemented in software that interacts with the microscope camera. A microscope camera is used for image acquisition to ensure repeatability, accuracy, and high precision for the centralization of the inspected surface [
In [
Morala Argüello et al. [
The surface condition of all machined parts was difficult to verify in the mass manufacturing process [
The findings of [ Surface ruggedness (Ra) can be accurately predicted by using input variables such as cutting depth, cutting speed, and feed rate. The built model of surface roughness (Ra) can be correctly predicted as a correlation factor between the artificial neural network prediction.
Articles | Year | Techniques | Objective |
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[ |
2021 | ANN | Non-contact-based surface characterization |
[ |
2019 | Parameters of ANN | To assess the feasibility of using signal features for vibration measurement in the milling process. Prediction of surface roughness |
[ |
2020 | ANN, ANFIS, and GA | To extract statistical features7 by measuring surface roughness through computing approaches |
[ |
2021 | BPNN and automatic acquisition | To perform the rapid detection of surface roughness |
[ |
2016 | Micro-milling processing technique/ANN | To assess the effects of surface roughness through computational fluid dynamics |
[ |
2015 | Grey level invariant moment technique | To perform surface roughness measurement with ‘sub-pixel edge detection’ in finish turning |
[ |
2019 | Histogram analysis using machine vision | To perform the non-contact evaluation of surface roughness texture |
[ |
1999 | - | To measure the surface roughness using optical techniques |
[ |
2002 | Computer vision-based ANN technique | Enhancement of surface roughness using computer vision techniques. |
[ |
2015 | Blob analysis | Tool status monitoring for surface roughness measurement |
[ |
2021 | CNN-a deep neural network approach | For measurement of non-contact surface roughness |
[ |
2021 | AI techniques (ANN, RSM) | AI-based surface estimation |
[ |
2019 | Machine vision based DL | For identification of chatter and estimation of surface roughness |
In [
In [
The research is part of a study whose overall purpose is to develop a web framework for monitoring machined surface roughness in real-time [
Articles | Publication year | Technique | Objective |
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[ |
2020 | ANFIS | Validation of surface roughness characterization |
[ |
2012 | ANFIS & ANN | To identify the surface characterization and damage of structure |
[ |
2017 | ANFIS process | For surface roughness modelling |
[ |
2005, 2012 | ANFIS process | For predicting the surface roughness |
[ |
2019 | ANFIS and GA | Optimization of surface roughness within thermal drilling |
In [
Fang et al. [
Using a neural network, an estimate was made of the Ra surface roughness parameter in [
Articles | Publication year | Technique | Objective |
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[ |
2019 | Deep CNN | For surface roughness prediction |
[ |
2018, 2020 | CNN | For estimating the roughness of the non-contact surface |
[ |
2021 | CNN | Visual measurement of surface roughness |
[ |
2017, 2012 | Deep CNN | ImageNet classification |
[ |
2018 | CNN | To detect the damage and defect on the metal surface |
Articles | Publication year | Technique | Objective |
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[ |
2019 | Deep learning | To evaluate the damage condition of steel structure |
[ |
2016 | DL approach | Recognition of the surface drill condition |
[ |
2021 | DL approach using drill classification | To perform the dill wear classification for surface characterization |
[ |
2020 | Machine learning and DL approach | For material roughness |
[ |
2020 | Machine Vision Approach | Colored illumination on features of surface textured |
Ali et al. [
Metallic surface characterization is an important problem in developing new products and devices in various industries, from the metallurgical to the medical industry, and understanding fundamental aspects of wave dispersion from rough surfaces [
For surface texture characterization, a view-based system was tried using the grey image approach in [
Kumar et al. [
Tootooni et al. [
A visual-based calculation of the machined surface roughness parameters for online surveillance is defined in [
It is also investigated that ambient light has no marked effect on measuring vision-based parameters during image capture. It is also assumed that the roughness of machined surfaces can be measured successfully using these vision-based parameters. In order to monitor influencing parameters, including rpm, feed, and depth of cuts, if the values of the surface finish are above the permitted levels stored in a device, the machine sends a feedback signal.
Narayanasamy et al. [
Özcan et al. [
Gharechelou et al. [
The viability of machined surfaces’ optical characterization using a cheap visual method has been tested in [
A final set of laser texture-based textures for elevation characteristics and a comprehensive and detailed texture characterization through confocal microscopy were constructed using various geometries [
In [
Aulbach et al. [
Al-Kindi et al. [ High precision calculation accuracy down to 0.2 μm Ra, checked by a stylus profilometer with high precision, Calculation of noncontact that prevents potential contamination and sample surface injury, Compact architecture that can be integrated for Three-stage movement control which reduces the robot and positioning vibration Mechanisms of movement, and Low-cost architecture relative to the style or optical profilometer desktop system.
In [ Before running surface roughness calculations, different considerations such as shininess, scan machine constraints, depth of view, scan path, and dot density have been studied and understood. The study concluded that, after the application of a cut-off filter wavelength, the roughness values specified on the comparator plate had been determined. Based on the absence of wavelength components (availability), The PCA roughness values obtained differed from those of the comparator. An independent ‘ACI’ specimen was then created and validated with the recognized ‘C9’ comparator specimen. A correlation curve was used. The surface ruggedness of unknown SCRATA surface texture plates has been quantitatively defined according to satisfactory findings. For casting surface, it is apparent that the proposed methodology to approximate roughness by sampling area (Sq), as opposed to traditional methods for calculating roughness based on online sampling, is reliable and has a lower coefficient of variance.
A design to quantify the surface roughness using the speckle images was attempted in [
In [ The surface profile is scanned with a laser vision device consisting of a diode laser and a CCD sensor. The points in the world coordination structure are reconstructed using an imaging coordination system by calibrating the camera and the laser plane. Any potential cause of the error, including the tangential lens distortion and uncertainties in the image process algorithms for reconstruction accuracy, is discussed. The laser vision sensing approach is shown to be an accurate and cheap method for characterizing the roughness of the surfaces of WAAM deposited components.
In [
This approach expects to exact and capable of resulting model. Detection of patterns in the roughness value of the commodity that are information for effective process management steps. Moreover, the process optimization can be expanded to include the parameters of the extraction protocol and the algorithm settings of the machine. The online implementation and assessment will also be carried out of the system built in the production context. A water surface can be assembled with a stereo vision device for three-dimensional measurements, as proposed in [
Hameed et al. [
Chen et al. [
Research on CNC-making surface consistency for applications using Internet-based diagnostic in-instruments offers an informative case study of principles and methods of equality [
The study of the CNC surface consistency [
The CNC turning workpiece was used in [
Different pictures of tile and wood flooring are considered in [
A surface characterization can be studied using texture parameters as presented in [
Sun et al. [ ResNet has shown that its surface ruggedness assessment approach is successful. The proposed approach is non-contact, without additional surface defects, instead of conventional calculation approaches for measuring surface roughness. This model does not focus on prior experience because of ResNet’s involvement with practical learning. Studies in surface friction show the potential way to change the image variabilities is the proposed texture skew corrector process. A surface roughness estimate on milled components would verify the effectiveness of the proposed new process. This approach can differentiate between different roughness of the surface and high accuracy grades. A filter analysis has shown that the feature networks can be considered as an automated and intelligent manual surface roughness estimate based on reference specimens.
Chang et al. [
Naresh et al. [
The study discussed in [
Hameed et al. [
In [ There has been a found strong association between six texture features (SVAR, SENT, DVAR, ASM, CSH, SAVR) and Ra (correlation coefficient over or equal to 0.9). Correlation equations for strongly corresponding texture characteristics were taken from Excel graphs, and calculation equations were obtained to determine the value of Ra from the texture characteristics measured. A new Cpp-module has been developed in order to approximate the surface ruggedness of the like specimens with known Ra values using the very correlated texture characteristics. The system was tested, and the results revealed that the overall error percentage between the real Ra and the predicted Ra was about 7%. The effects of the used vision system may be affected by certain parameters. If these conditions are to change, the machine has to be calibrated to solve this problem. The used vision method will also be used to estimate surface roughness for various machining operations in mass manufacturing.
In [ Evaluated the binary image matrix in Networks of preparation, black and white lines route. Chosen extended parallel in the excess path of these lines. The recognition efficiency of the training network improves in the horizontal direction. The best results of 300 to 240 resolutions have been achieved in pictures. Log-sigmoid was chosen for the training networks as a transition function, combination gradient scale (SCG) was employed as an algorithm for testing, and the Full number of neurons in training was chosen Network. The overall output of the qualified networks of AA 5083 Aluminium was 99.926%, and AISI 1040 steel, on average, 99.932%. In comparison with experimental findings obtained in the first photographs, they validated each other at 99.999%.
In Pino et al. [
Abidi et al. [
In [
In [
In [
In [
Al-Kindi et al. [
Shahabi et al. [
To obtain sea surface ruggedness measurements from observable pictures based on a novel sea surface random field principle, Pan et al. [
In [ A strong association was found between six textures (SVAR, SENT, DVAR, ASM, CSH, SAVR) and Ra (coefficient of correlation over or equal to 0.9). In the case of graphs drafted by Excel, the correlation coefficients of the strongly correlated texture characteristics were derived, and then the value of Ra was computed from the calculated texture characteristics. In order to approximate the surface roughness of comparable specimens with defined Ra values, the new Cpp has been written to use strongly correlated texture characteristics.
In [
In [
In [
Fu et al. [ The similarity between the picture parameters and the design parameters is very weak for white-light imaging. This may be due to the effect of the incidence angle of the light source on the surface leading to the peaks that create shadows in the immediate vicinity. The parameters of image strength are very well related to the parameters of the stylus. The style parameters for the arithmetical average pitch (Rda), and root average square pitch (Rdque) have been found to correspond well with the picture parameters in Speckle because at different points of the free area, the skyscrapers are responsible for the reflection of lights rays. Measurement efficiency can be measured by greater sample size studies.
In [ Experimental design to analyze the problem is suitable and effective. Similar results are provided by analysis of the variance methodology and the S/N ratio method. The angle of grazing is the light condition that affects the Ga value in terms of both physical and statistical aspects. The next influence factor is the angle of striation. For the interactions analyzed in the ga estimate, the grassing angle/angle of striations interaction has the greatest physical importance. There is no physical importance to the rest of the relationships in Ga. The mistakes associated with the ANOVA table in relation to the parameters demonstrate a satisfactory consideration of the contributing factors. The higher the grazing angle, the smaller the difference of the ga value with the parameter roughness for image analysis through the vision process.
The analysis of photographs may be a reliable way of calculating various textile characteristics [
A new strategy for adaptive image improvement is developed by analyzing the image characteristics of the adaptive images in [
Abellan-Nebot et al. [
Cuthbert et al. [
A fundamental technological hurdle that inhibits firms from adopting additively produced (AM) components is a lack of quality assurance. This is especially true for high-value utilizations the failure of components cannot be accepted. The advancement in process control has enabled substantial advancements in AM methods. As a result, the use of AM methods is accelerating. In contrast to buildup subtractive processes in which monitoring of in-process is ubiquitous, AM techniques need to include monitoring technology that gives room for the detection of discontinuities in the process. Process control advancements have enabled considerable advances in AM methods, significant increases in surface roughness and material characteristics, and a decrease in inter-build variance while the incidence of “embedded material discontinuities” [
Hashmi et al. have reviewed the various surface finishing techniques, i.e., preprocessing and post-processing techniques, to improve the surface quality [
“Surface topography can be characterized by geometric parameters, including Ra (i.e., the average of the set of independent measurements of a surface’s peaks and valleys), Rmax (i.e., the maximum height roughness), etc.”
In the study [
On the basis of wavelet transformation, the research of Josso et al. [
In 2007, Al-Kindi et al. [
Okamoto et al. [
Most of the methods to inspect surface defects are designed to increase product uniformity and the efficiency of detection, with the goal of gradually replacing or supplementing the inspection with manual methods in the conventional manufacturing lines. JFE Steel JFE TMBP is the manufacturing line for the final product of “Tin Mill Black Plate” (TMBP) and has the greatest operating speed in the world. Sasaki et al. [
Hashmi et al. [
Gonzalez-Val et al. [
Xie [
Saini et al. [
The technology of machine vision makes use of image data to investigate the component’s quality. The industrial components’ surface quality is considered the crucial quality characteristic from various aspects. Machine vision techniques are used for the surface roughness characterization by making use of the concept that the image is embodied as the 2-D (two dimensional) function of the image intensity, which is characterized by the two parameters: (1) the amount of light that hit of the surface and (2) amount of the light that reflects from the surface. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate surface quality. Various conventional methods and approaches to measure the surface roughness be not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This article discusses the characterization of the surface texture through a computer/machine vision approach and assessment of the surface roughness on the basis of various machine vision parameters. This paper has also discussed different machine vision techniques to perform the surface characterization measurement. Computer vision techniques can be used for multiple aspects of intelligent manufacturing philosophies. The surface characteristics measured using computer vision techniques, as shown in
For future work, it is suggested to perform a more in-depth analysis of machining of surface roughness using machine/computer vision as well as image processing. Machine vision has emerged as the innovative approach to executing surface roughness measurement. It is capable of providing economic, automated, quick as well as reliable solutions. There exists very little data on the machined surface roughness using computer vision. From the literature, it has been observed that these new and novel techniques perform well for surface characterization. With more research on machine vision-based systems, surface characterization measurement can be improved. In the three factors below, challenges and opportunities are depicted:
New problems and opportunities are presented by emerging deep learning techniques and present transition procedures toward smart manufacturing, primarily examined from two perspectives: streaming data processing and imbalanced categorization. The computer vision techniques should be implemented using smart sensors in The implementation of smart and intelligent manufacturing could be done using computer vision techniques to measure surface characteristics. Due to the limited availability of standardized statistics in the early stages of the commercial big data era, a transfer learning-based strategy could be a viable alternative where the dataset contained is insufficient. The rising pace with which goods are upgraded in modern industries focuses on short-cycle production with quick response capabilities. On-computer vision equipment’s trial-and-error tests have a significant impact on production. The question of how to shorten the time it takes to define parameters for The computer vision techniques for measuring surface characteristics of additively manufactured parts should be implemented. The measurement accuracy should be improved by processing a large set of data using advanced computation methodology, i.e., deep learning technique or big data analytics. The following computer vision technique may be implemented for the measurement of surface characteristics of additively manufactured parts, as shown in