TiNbN Hard Coating Deposited at Varied Substrate Temperature by Cathodic Arc: Tribological Performance under Simulated Cutting Conditions

Self Cite

Hardness is one of the most crucial mechanical properties, serving as a key indicator of a material’s suitability for specific applications and its resistance to fracturing or deformation under operational conditions. Machine learning techniques have emerged as valuable tools for swiftly and accurately predicting material behavior. In this study, regression methods including decision trees, adaptive boosting, extreme gradient boosting, and random forest were employed to forecast Vickers hardness values based solely on scanned monochromatic images of indentation imprints, eliminating the need for diagonal measurements. The dataset comprised 54 images of D2 steel in various states, including commercial, quenched, tempered, and coated with Titanium Niobium Nitride (TiNbN). Due to the limited number of images, non-deep machine learning techniques were utilized. The Random Forest technique exhibited superior performance, achieving a Root Mean Square Error (RMSE) of 0.95, Mean Absolute Error (MAE) of 0.12, and Coefficient of Determination (R2) ≈ 1, surpassing the other methods considered in this study. These results suggest that employing machine learning algorithms for predicting Vickers hardness from scanned images offers a promising avenue for rapid and accurate material assessment, potentially streamlining quality control processes in industrial settings.

Section: Methodsmentioning

confidence: 99%

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

Mambuscay,

Ortega-Portilla,

Piamba

et al. 2024

Self Cite

“…Increasing the hardness (H) of the friction surface considerably improves wear resistance [1]. Various coating materials and their processes have been developed to increase surface hardness [2][3][4][5][6][7][8][9][10]. In particular, the carburising-quenchingtempering (CQT) process [11][12][13], which involves heating above 1123 K in an acetylene gas atmosphere to facilitate carbon atom diffusion into steel, has been widely industrialised.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wear Resistance in Functionally Graded Metallic Components: Insights from Nanoindentation and Mechanical Analysis

Furukimi,

Kabasawa,

Yamamoto

et al. 2024

To manufacture metallic components with high wear resistance, treatments such as nitriding and carburising followed by quenching and tempering (NQT and CQT, respectively) are applied to various types of steel to increase the hardness (H) of the friction surface. However, the wear mechanism of the resulting functionally graded materials has not been fully understood. In this study, specimens of industrial 99.82% pure iron treated with NQT at 913 and 1033 K, and CQT at 1203 K, as well as hot-rolled sheets without heat treatment were examined by performing nanoindentation tests to measure changes in their H, reduced Young’s moduli (Er), elastic deformation energies (We), and plastic deformation energies (Wp) along the depth direction. The relationship between Wp/We and the elastic strain resistance (H/Er) can be expressed for all specimens via the equation Wp/We = −1.0 + 0.16 (H/Er)−1. Furthermore, the obtained H/Er av measured at 5 µm intervals based on the specimen profile and wear volume has a good correlation depending to the sliding distance, as confirmed by the results of the ring-on-plate sliding tests conducted for the carbon-treated, nitrogen-treated, and hot-rolled specimens. This study provides a new approach, using H/Er parameters to identify the dominant factors affecting wear resistance at the initial stage of wear that may contribute to the development of wear-resistant materials.

“…Titanium nitride (TiN) coatings offer excellent mechanical, optical, and electrical properties [ 2 , 3 ], including bioinert and corrosion-resistant [ 4 ] ones. Thus, TiN coatings are widely used in medical applications [ 5 , 6 ] as well as aircraft and shipbuilding [ 7 ] to increase the wear resistance of cutting and processing tools [ 8 , 9 ]. For such applications, a large number of works on the characterization of TiN coatings deposited by various methods was fulfilled [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…For such applications, a large number of works on the characterization of TiN coatings deposited by various methods was fulfilled [ 10 , 11 , 12 , 13 , 14 ]. And to study the structure and properties, standard macromethods and equipment were used [ 9 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Nitrogen Flow on the Stoichiometric Composition, Structure, Mechanical, and Microtribological Properties of TiN Coatings

Lapitskaya,

Nikolaev,

Khabarava

et al. 2023

Utilizing reactive DC magnetron sputtering method, TiN coatings were deposited on the silicon substrates at different nitrogen flows and powers. A study of the X-ray phase composition of the coatings was carried out. The stoichiometric composition of the coatings was determined using energy dispersive x-ray spectroscopy. The structure of the surface, cross-section, and thickness of the coatings were determined using scanning electron (SEM) and atomic force microscopy (AFM). A significant change in the surface structure of TiN coatings was established with changes in deposition power and nitrogen flow. SEM images of cross-sections of all coated samples showed that the formation of coatings occurs in the form of a columnar structure with a perpendicular orientation relative to the silicon substrate. The mechanical properties (elastic modulus E and microhardness H) of TiN coatings of the first group demonstrate a maximum at a nitrogen flow of 3 sccm and are 184 ± 11 GPa and 15.7 ± 1.3 GPa, respectively. In the second group, the values of E and H increase due to a decrease in the size of the structural elements of the coating (grains and crystallites). In the third group, E and H decrease. Microtribological tests were carried out in 4 stages: at a constant load, multi-cycle for 10 and 100 cycles, and with increasing load. The coefficient of friction (CoF) and specific volumetric wear ω depend on the roughness, topology, and mechanical properties of the resulting coatings. Fracture toughness was determined using nanoscratch and depends on the mechanical properties of TiN coatings. Within each group, coatings with the best mechanical and microtribological properties were described: in the first group—TiN coating at 3 sccm (with (29.6 ± 0.1) at.% N), in the second group—TiN coating at 2 sccm (with (40.8 ± 0.2) at.% N), and in the third group—TiN coating at 1 sccm (c (37.3 ± 0.2) at.% N).