Automatic Method for Vickers Hardness Estimation by Image Processing

Abstract

Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of 5N and 10N on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of 2.05 seconds with an accuracy of 98.3% and a maximum error of 4.5% with respect to the values obtained manually, used as a golden standard.

Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments

“…Previous studies employing image processing techniques, like those by Polanco et al [10], reported hardness measurement errors ranging from 0.32% to 4.5% for both manual and their proposed methods, with an average processing time of 2.05 s. Similarly, Buitrago et al [19] utilized convolutional neural networks (CNNs) and achieved manual hardness errors between 0.17% and 5.98%, with an average execution time of 6 s. Our proposed method demonstrates comparable error margins to these existing approaches, while significantly reducing processing time, thereby leading to lower computational costs.…”

“…ML techniques also enable the classification of material defects, such as fractures or surface stains [4][5][6], or the prediction of coating properties like hardness, friction coefficient, corrosion rate, based on deposition parameters [7][8][9]. One way to control material quality and ensure it meets the appropriate characteristics for its application is through the Vickers hardness test [10]. This test involves measuring the plastic deformation or indentation mark produced on the surface after applying a load with a pyramidal diamond indenter, and the hardness value is determined by Equation (1).…”

“…On the other hand, researchers have implemented image processing methods and machine learning techniques for the determination of Vickers hardness. Polanco et al estimated the Vickers hardness value using image processing methods to detect the corners of the indentation imprint, achieving a maximum error of 4.5% compared to manually obtained values [10]. Dovale et al used a gradient boosting regressor (GBR) to predict hardness based on mechanical properties such as bulk modulus (B), shear modulus (G), Young's modulus (Y), and Poisson's ratio (ν).…”

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

“…[5] uses sensor images for real-time strain prediction. Material hardness detection has been investigated using image processing techniques such as [35].…”

Determination of Vickers Hardness in D2 Steel and TiNbN Coating Using Convolutional Neural Networks

“…Polanco et al employed thresholding and mathematical morphology techniques for edge determination and introduced a quadrature index to choose among methods: maximum local radius, perimeter, and Hough transform, which yielded the best result. They obtained the Vickers hardness value with an error of 4.5% compared to manual measurements [11].…”