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Machine vision for the measurement of machining parameters: A review
Abstract and Figures
Machining parameters have significant value in manufacturing and machining industries as they result in quality and dimensional accuracy of the product. The machining parameters are measured using various machine vision systems. In this review, machine vision and its various procedures have been discussed that are used to measure machining parameters, i.e., tool condition monitoring (TCM) tool wear and surface characteristics like surface roughness, surface defects, etc. Nowadays, Tool condition monitor is a significant machining parameter is developed in manufacturing and machining industries. The development of various techniques of machine vision explore in tool condition monitoring is of significant interest because of the improvement of non-tactile applications and computing hardware. The review also discusses the enhancement of machine vision systems in tool condition monitoring.
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... Commonly, industrial in-process applications limit the functionalities of MV to visual feedback to human operators, identification of tags, monitoring of faults in production lines and so forth [3,8]. The outputs of these applications are typically data-driven simplified logical or numerical values, which inform process control and monitoring [9,10]. The problem with these simplified outputs is that without rigorous metrological assessment, it becomes difficult to determine the reliability of the conveyed information. ...
... When an artefact, such as checkerboard is imaged, the known physical dimensions of the artefact and the observed distorted dimension of the artefact can be used to solve for the inverse of the distortion model in Eq. (9). The raw pixel data [ũ,ṽ] ⊤ , obtained by imaging a checkboard artefact during the camera pre-calibration process, is used to solve the inverse distortion problem by determining the radial distortion coefficients and the distortion centre. ...
New developments in vision algorithms prioritise identification and perception over accurate coordinate measurement due to the complex problem of resolving object form and pose from images. Consequently, many vision algorithms for coordinate measurements rely on known targets of primitive forms that are typically planar targets with coded patterns placed in the field of view of vision systems. Although planar targets are commonly used, they have some drawbacks, including calibration difficulties, limited viewing angles, and increased localisation uncertainties. While traditional tactile coordinate measurement systems (CMSs) adopt spherical targets as the de facto artefacts for calibration and 3D registration, the use of spheres in vision systems is limited to occasional performance verification tasks. Despite being simple to calibrate and not having orientation-dependant limitations, sphere targets are infrequently used for vision-based in-situ coordinate metrology due to the lack of efficient multi-view vision algorithms for accurate sphere measurements. Here, we propose an edge-based vision measurement system that uses a multi-sphere artefact and new measurement models to extract sphere information and derive 3D coordinate measurements. Using a spatially encoded sphere identities embedded in the artefact, a sphere matching algorithm is developed to support pose determination and tracking. The proposed algorithms are evaluated for robustness, measurement quality and computational speed to assess their performance. At the range of 500 mm to 750 mm, sphere size errors of less than 25 μm and sphere-to-sphere length errors of less than 100 μm are achievable. In addition, the proposed algorithms are shown to improve robustness by up to a factor of four and boost computational speed.
... These algorithms can learn complex patterns and anomalies from large datasets, enabling the detection of subtle wear features and identifying abnormal wear conditions. By training machine vision systems with extensive wear data, these algorithms can enhance the system's ability to identify wear patterns accurately and predict wear progression [31,32]. ...
... This allows the system to adapt to various conditions and increase the effectiveness of the analysis. Hashmi et al. [32] have focused on a variety of machine vision systems for the assessment of machining parameters. The ongoing advancement of different machine vision methods in the realm of monitoring tool conditions is of great importance due to the enhancement of touchless applications and the evolution of computer hardware. ...
Automated monitoring of cutting tool wear is of paramount importance in the manufacturing industry, as it directly impacts production efficiency and product quality. Traditional manual inspection methods are time-consuming and prone to human error, necessitating the adoption of more advanced techniques. This study explores the application of ViDiDetect, a deep learning-based defect detection solution, in the context of machine vision for assessing cutting tool wear. By capturing high-resolution images of machining tools and analyzing wear patterns, machine vision systems offer a non-contact and non-destructive approach to tool wear assessment, enabling continuous monitoring without disrupting the machining process. In this research, a smart camera and an illuminator were utilized to capture images of a car suspension knuckle’s machined surface, with a focus on detecting burrs, chips, and tool wear. The study also employed a mask to narrow the region of interest and enhance classification accuracy. This investigation demonstrates the potential of machine vision and ViDiDetect in automating cutting tool wear assessment, ultimately enhancing manufacturing processes’ efficiency and product quality. The project is at the implementation stage in one of the automotive production plants located in southern Poland.
... vision-based detection has advantages such as lightweight, portability, and ease of online operation, making it an increasingly popular option in the precision manufacturing industries [2,3]. Two commonly used camera types are monocular and binocular ones. ...
... Some of these challenges, like the chip manipulation, are uneconomical and are capable of increasing the metal removal and wear rate of the tool [13]. However, in the quest to obtain optimal machining parameters, studies involving the application of optimization techniques have also been employed [14][15][16][17]. In the machining of AISI 4340 steel with alumina ceramic inserts as cutting tool, Mondal et al. [18] employed the Taguchi technique in obtaining the optimal cutting conditions and developing a mathematical model for the prediction of the associated flank wear of the ceramic inserts. ...
With the rising need to promote productivity that is based on quality, energy, and cost, it is imperative that cutting tools are not just selected on the basis of its suitability, but on its efficiency. It is particularly important that workpiece materials are aligned to specific cutting tools, to improve manufacturing costs, lead time, and quality of the overall product and to create flexibility. For this reason, a comparative study of the performance of high-speed steel and tungsten carbide cutting tools has been performed to determine the most suitable for the machining of a 304 Austenite Steel cylindrical bar. The tool wear of the cutting tools was selected as a measured to ascertain their performance in the turning operation. Response surface methodology was employed to analyzing the results and determining their optimal performance. From the study, the tungsten carbide tool recorded optimal parameters as follows: cutting speed 1303 m/min, feed rate 0.354 mm/rev, and depth of cut 0.458 mm with a tool wear of 1.173 mm and for the high-speed steel tool, cutting speed 1321 m/min, feed rate 0.208 mm/rev, and depth of cut 0.682 mm with a tool wear of 2.073 mm. The study judging from the tool wear shows the efficiency of the tungsten carbide tool over the high-speed steel cutting tool, as it can be seen from the results obtained that the lowest tool wear in the turning of the cylindrical steel bar is recorded from the use of the tungsten carbide cutting tool. From the 3D surface plots, it can be confirmed that to obtain a good performance from the different cutting tools, the cutting speed best suited for the cutting tool must be taken into consideration.
... With the development of machine vision, vision systems have been widely used in industrial fields, mainly in the inspection and quality control process [1] . Most factories use robots and combine machine vision for automatic loading. ...
To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part's surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.
... Current methods of dimension measurement are either time-consuming or costly. Besides that, there are very few existing measurement methods that can measure all the visual features and reduce the measurement time to some extent (Hashmi et al., 2022). Metrology can be viewed as a challenge to guarantee the quality of parts (Alonso et al., 2019). ...
To target the problem of dimension measurement of objects in industry, a new computer vision method is proposed based upon Harris-corner detection. The proposed research delivers an alternative to the requirement of various precise measurement devices and skilled labour. In order to measure the various dimensions of an object, the proposed algorithm separates the corner points from the background based on variations in pixel intensity. An algorithm has been proposed to analyze captured object images and perform measurements and inspection processes. The aim of this paper is to utilize computer vision detection algorithms to control the quality of manufactured parts by sorting them on size tolerance. The length of various objects such as screws, bolts, and a rectangular iron piece was determined from the images captured using smartphone camera (Samsung Galaxy F62). The evidence for a total of eight different measurements is presented, and the accuracy of the method is proved up to 99 percent against the dimensions measured using the Vernier calliper.
... Similarly, the authors have also reviewed the mathematical modelling and simulation technique for abrasive finishing techniques for different applications, including AM parts, to predict the material removal (MR) mechanism and surface roughness [38][39], and studied artificial intelligence and machine vision application to investigate the different process parameters during any manufacturing process, i.e. AM applications [40][41]. The schematic of different variants of the AFM process [42], which may be employed for improving the surface finish of AM parts, is shown in Figure 5 The authors discovered that the surface roughness and texture of the inferior surface of IN625 samples might be greatly improved by removing semi-welded particles using a combination of chemical and abrasive flow polishing [45]. ...
For the ASO-S/HXI payload, the accuracy of the flare reconstruction is reliant on important factors such as the alignment of the dual grating and the precise measurement of observation orientation. To guarantee optimal functionality of the instrument throughout its life cycle, the SAS is imperative to ensure that measurements are accurate and reliable. This is achieved by capturing the target motion and utilizing a physical model-based inversion algorithm. However, the SAS optical system's inversion model is a typical ill-posed inverse problem due to its optical parameters, which results in small target sampling errors triggering unacceptable shifts in the solution. To enhance inversion accuracy and make it more robust against observation errors, we suggest dividing the inversion operation into two stages based on the SAS spot motion model. Firstly, the as-rigid-as-possible (ARAP) transformation algorithm calculates the relative rotations and an intermediate variable between the substrates. Secondly, we solve an inversion linear equation for the relative translation of the substrates, the offset of the optical axes, and the observation orientation. To address the ill-posed challenge, the Tikhonov method grounded on the discrepancy criterion and the maximum a posteriori (MAP) method founded on the Bayesian framework are utilized. The simulation results exhibit that the ARAP method achieves a solution with a rotational error of roughly ±3.5 arcsec (1/2-quantile); both regularization techniques are successful in enhancing the stability of the solution, the variance of error in the MAP method is even smaller - it achieves a translational error of approximately ±18μm (1/2-quantile) in comparison to the Tikhonov method's error of around ±24μm (1/2-quantile). Furthermore, the SAS practical application data indicates the method's usability in this study. Lastly, this paper discusses the intrinsic interconnections between the regularization methods.
Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.
Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy.
Aiming at the low tool utilization rate caused by tool wear in the milling process, a tool wear automatic monitoring system based on machine vision is proposed. The tool wear images are automatically acquired by a charge-coupled device (CCD) camera. The system selects the image with obvious characteristics and cuts the wear area for processing, thus extracting the tool wear value. On one hand, the reliability of using the wear area of flank face as a technical index to judge the degree of tool wear is explored. On the other hand, the changes in the surface texture of workpiece are also analyzed by the gray-level co-occurrence matrix (GLCM) method. A milling experiment was carried out and the wear value measured by the monitoring system was compared with the real wear value. The result showed that the accuracy of the monitoring system met the industrial requirements. The wear area of the flank face and the wear width are consistent in trend under different cutting parameters, which means that the wear area of the flank face could be used as an index for judging the degree of tool wear. In addition, the characteristic parameters of the surface texture of workpiece change regularly with the tool wear, which shows that the tool wear can be characterized from another aspect.
Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques.
Nanoparticles (NPs) have drawn immense attention due to the full range of
new applications in various fields of industries such as electronics, optical, biomedical,
pharmaceutical and cosmetics. NPs gained importance due to their exceptional
properties like antibacterial activity, high resistance to oxidation, exceptional
adhesive properties, better thermal conductivity and many more. Various interdisciplinary
researches have been done in the field and still going on. The aim of this
paper is to briefly describe the details of NPs processing methods, their benefits and
limitations and the need of new process in the field. In this paper, electrical discharge
machining (EDM) has been presented as possible new process for the synthesis of
NPs. The challenges in the development of EDM as a NPs synthesis process have
also been discussed in this paper.
The vial, a bottle known to store the drug, should be controlled to meet the requirements of the standard dimension. Due to problems with a visual inspection, there is a need to develop an automated inspection system. In this paper, a machine vision system for measuring and controlling the dimensional characteristics of medical glass vials has been developed. In this regard, because of the difficulty of taking images of glass vials and reflecting the light that may have these images, some innovative actions have been taken to determine the way for obtaining the appropriate images. Also, the effectiveness of several common segmentation methods has been examined and a heuristic segmentation method is proposed to extract vial borders. Finally, using to integrate heuristic segmentation method and appropriate post-processing methods as well as employing machine learning, an automated approach for measuring different dimensional characteristics of vials is proposed and evaluated by real samples.
Tool wears directly affect the quality of product and service life of tool. This paper proposes a machine vision-based measurement method for chisel edge wear of drills. Firstly, the full contour of a drill is extracted by local variance threshold segmentation. Secondly, the image is enhanced by using an adaptive contrast enhancement algorithm based on bidimensional local mean decomposition (BLMD). A threshold segmentation method is proposed to extract contour of the non-worn area. After the above two contours are superimposed, the centroid of each region in the binary image is calculated as the starting point to fill in the overflow water for tool wear extraction. Finally, the chisel edge wear can be measured directly by counting the number of pixels. The experiment in the process of drilling is performed to verify the effectiveness of the proposed method. The experimental results show that the proposed method effectively implements the measurement of tool wear.
Tool wear is usually the most relevant parameter of tool performance detection, which directly affects the product quality and tool life. Online tool condition monitoring can effectively reduce workpiece scrap and machine failure. Chisel edge wear is one of the main wear forms in high-speed steel (HSS) twist drill. A machine vision method for chisel edge wear measurement is proposed in this paper to improve measure accuracy and reduce testing costs. Firstly, Otsu segmentation based on Laplacian edge information is proposed for tool image segmentation. A morphology-based Canny operator is developed to perform edge detection and image registration for rough positioning of the tool wear area. Zernike moment-based sub-pixel edge detection is proposed to improve the measurement accuracy. Finally, principal curve is employed to fit sub-pixel edge points to obtain smooth edge curve of the tool wear. Finally, the chisel edge wear can be detected and measured effectively based on the proposed method. In the real machining process, the test results show that the proposed system shows high response speed and inspecting accuracy, which is more convenient and efficient than manual measurement with an electron microscope. This system can be effectively applied to the real-time monitoring of tool wear in industry.
Observing the tool wear is very important in metal cutting industry as it affects the dimensional accuracy and surface quality of work part. Generally, tool wear is measured directly using an optical microscope which is an offline and time-consuming technique. In the indirect technique, the parameter such as force, surface finish, temperature etc. that is affected by tool wear is measured and correlated with tool life. It has been observed that measurement of tool wear using image processing techniques has a lot of scope. Digital image processing techniques automate the task of measurement and monitoring of tool wear. A novel on-line tool wear measuring algorithm is proposed to monitor tool wear using machine vision. This work is focused on implementing digital image processing techniques to automate the task of two dimensional flank wear measurement. The tool wear images are acquired by a camera, and the wear boundaries are recognized by image pre-processing, threshold segmentation and edge detection to measure the wear on tool. The wear values obtained by proposed method are compared with manual measurement. The maximum flank wear with proposed algorithm and manual measurement is found as 730 and 718 µm respectively. It is found that the maximum variation in the results obtained by algorithm and manual measurement is 4.98%. This shows the higher detection accuracy for on-line tool wear monitoring by the proposed algorithm.
This proceedings book presents a collection of research papers from the 10th International Conference on Robotics, Vision, Signal Processing & Power Applications (ROVISP 2018), which serves as a platform for researchers, scientists, engineers, academics and industrial professionals from around the globe to share their research findings and development activities. The book covers various topics of interest, including, but not limited to:
•Robotics, Control, Mechatronics and Automation
•Vision, Image, and Signal Processing
•Artificial Intelligence and Computer Applications
•Electronic Design and Applications
•Biomedical, Bioengineering and Applications
•RF, Antenna Applications and Telecommunication Systems
•Power Systems, High Voltage and Renewable Energy
•Electrical Machines, Drives and Power Electronics
•Devices, Circuits and Embedded Systems•Sensors and Sensing Techniques