Tool wear monitoring in turning using image processing techniques

Bagga, Prashant J.; Makhesana, Mayur A.; Patel, Kaushik M.

doi:10.1016/j.matpr.2020.10.680

Cited by 22 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where, n represents the number of pixels in width, m the number of pixels in length, element V11 corresponds to the element in the upper left corner (see Figure 3) [19]. Thus, in its programming interface, the software uses the techniques of global thresholding, they are generally applied to divide the image into background and foreground areas with a bimodal histogram [21,22]. Therefore, the first task is to divide the image into foreground and background pixels.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

Arévalo-Ruedas

Blanco

Flórez-Solano

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The useful life of the tools is a very important economic factor in the metal industry, so any improvement of the tool or material that can extend the useful life of the tools is productive. Tool deterioration can cause damage, cracking and vibration to the tool, and even damage to the machine. Effective tool wear control is most important. Effective tool wear control is most important. At present, there are some tools that can obtain the necessary conditions through digital image processing, and study their lifespan according to their work cycle, as well as the types of possible relationships between the pixels of the image and the different modes of operation, allowing the extraction or isolation of the objects considered. This work analyzes the wear of the tool with the application of two-dimensional matrices with the toolbox of the MATLAB software, which allows to monitor the status of the inserts with the comparison of images in gray scales, in addition, a method of analysis based on interfaces is also being studied; through it, users can access the database that has been implemented, as well as a set of images used to verify the functions developed, determining the wear on the cutting tool.

show abstract

Section: Methodsmentioning

confidence: 99%

“…This step is done to identify the region of interest (ROI). Any general threshold algorithm converts an image into a binary image according to the following Equation (2) [21,22]; where, T is the threshold value selected to segment the image. f(x, y) = 0, f(x, y) > T.…”

Section: Methodsmentioning

confidence: 99%

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

Arévalo-Ruedas

Blanco

Flórez-Solano

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In the above equation where ε * i and ε i are the upper and lower slack variables. This is subject to ε -deviation y i − f(a i ) ≤ ε, the term 1 2 w 2 is a regularization that improves the generalization of the model.…”

Section: Support Vector Machine With Bayesian Optimization For Regressionmentioning

confidence: 99%

“…Tool life is one of the main parameters in machining. Tools that wear or fail a comparably lengthy duration life service can lead to a decreased production rate and surface finish capacity [ 1 ]. Tool wear is an important parameter in machining as its increase not only increases cutting forces and cutting temperatures but also produces poor finished and inaccurately machined surfaces.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Alajmi

Almeshal

2021

Materials

View full text Add to dashboard Cite

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

show abstract

“…CCD vision sensor was used, and an LED ring light source was used to obtain homogeneous illumination. An intermittent tool wear monitoring system was proposed by Bagga et al [29]. The error in measurement using image processing was 4.98% when compared to the actual size.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computer Vision Based Machine Learning Approach For Online Tool Wear Monitoring In Machining

Bagga

Makhesana

Pala

et al. 2021

Preprint

View full text Add to dashboard Cite

With the increased scope of automated machining processes, one of the essential requirements is the reliable predictions of the tool life. It is crucial to monitor the condition of the cutting tool during the machining process to achieve high-quality machining and cost-effective production. This paper presents a computer vision technique for flank wear measurement and prediction using machine learning, specifically support vector machine (SVM) and boosted decision trees has been used. The proposed methodology for tool wear measurement is illustrated for the CNC machining experimentally. The direct method of tool wear measurement and prediction have been proposed. Flank wear measurement is carried out on PVD coated tool insert, and experiments are performed on an alloy steel workpiece under dry machining. For capturing images, a CMOS camera with a lens mounted on the machine is used. To avoid environmental effects on the images LED ring light is used. Captured tool insert images are provided to the image processing algorithm built-in MATLAB software. The measurement of flank wear is also carried out using a microscope. The prediction accuracy of SVM and optimized boosted tree models is 97% and 96%, respectively, proving prediction algorithms' effectiveness. The findings showcased that the proposed methodology can measure and predict the tool wear with higher accuracy. It has demonstrated the ability to increase cutting tool utilization with the improved surface finish of the machined component.

show abstract

Tool wear monitoring in turning using image processing techniques

Cited by 22 publications

References 13 publications

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

A Novel Computer Vision Based Machine Learning Approach For Online Tool Wear Monitoring In Machining

Contact Info

Product

Resources

About