Implementation of the Chicot–Lesage Composite Hardness Model in a Determination of Absolute Hardness of Copper Coatings Obtained by the Electrodeposition Processes

The influence of various electrolysis parameters, such as the type of cathode, composition of the electrolyte and electrolysis time, on the morphology, structure and hardness of copper coatings has been investigated. Morphology and structure of the coatings were analyzed by scanning electron microscope (SEM), atomic force microscope (AFM) and X-ray diffraction (XRD), while coating hardness was examined by Vickers microindentation test applying the Chicot–Lesage (C–L) composite hardness model. Depending on the conditions of electrolysis, two types of Cu coatings were obtained: fine-grained mat coatings with a strong (220) preferred orientation from the sulfate electrolyte and smooth mirror bright coatings with a strong (200) preferred orientation from the electrolyte with added leveling/brightening additives. The mat coatings showed larger both measured composite and calculated coating hardness than the mirror bright coatings, that can be explained by the phenomena on boundary among grains. Independent of electrolysis conditions, the critical relative indentation depth (RID) of 0.14 was established for all types of the Cu coatings, separating the zone in which the composite hardness can be equaled with the coating hardness and the zone requiring an application of the C–L model for a determination of the absolute hardness of the Cu coatings.

Determination of the absolute hardness of electrolytically produced copper coatings by application of the Chicot-Lesage composite hardness model

In this study, a novel procedure based on application of the Chicot?Lesage (C?L) composite hardness model was proposed for determination of an absolute hardness of electrolytically produced copper coatings. The Cu coatings were electrodeposited on the Si(111) substrate by the pulsating current (PC) regime with a variation of the following parameters: the pause duration, the current density amplitude and the coating thickness. The topography of produced coatings was characterized by atomic force microscope (AFM), while a hardness of the coatings was examined by Vickers microindentation test. Applying the C?L model, the critical relative indentation depth (RID)c of 0.14 was determined, which is independent of all examined parameters of the PC regime. This RID value separated the area in which the composite hardness of the Cu coating corresponded to its absolute hardness (RID < 0.14) from the area in which application of the C?L model was necessary for a determination of the absolute coating hardness (RID ? 0.14). The obtained value was in a good agreement with the value already published in the literature.

A simplified hardness model for WC-Co-Cubic cemented carbides

Hardness is an essential mechanical indication of cemented carbides. The current work presents an approach to predict the hardness of three-phase WC-Co-Cubic cemented carbides, which establishes a relationship among composition, structure and mechanical performance. With the input of initial composition and grain sizes of carbides, structural parameters needed to predict the hardness can be calculated by thermodynamic calculations and diffusion simulations. The calculated hardness of a series of WC-Co-Cubic cemented carbides agree reasonably with experimental data. The present model is of reference to predict the hardness for multi-phase composites and design the new type of WC-Co-based cemented carbides.

Finding the Next Superhard Material through Ensemble Learning

We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials

Finding the Next Superhard Material through Ensemble Learning

We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials

Morphology, Structure and Mechanical Properties of Copper Coatings Electrodeposited by Pulsating Current (PC) Regime on Si(111)

Copper electrodeposition on (111)-oriented Si substrate was performed by the pulsating current (PC) regime at various average current densities in the range of 15–70 mA·cm−2, obtained by varying either the frequency (30, 50, 80 and 100 Hz for the current density amplitude of 100 mA·cm−2) or the current density amplitude (120 and 140 mA·cm−2 at 100 Hz). The produced Cu coatings were examined by SEM, AFM and XRD techniques. The morphology of the coatings changed from those with large grains to fine-grained and globular, while the crystal structure changed from the strong (220) to the strong (111) preferred orientation by increasing the average current density. The mechanical characteristics of coatings were examined using Vickers micro-indentation tests, applying the Chicot–Lesage (C–L) composite hardness model for the analysis of microhardness. The maximum microhardness was obtained for the Cu coating produced at an average current density of 50 mA·cm−2, with a current density amplitude of 100 mA·cm−2 and a frequency of 100 Hz. This copper coating was fine-grained and showed the smallest roughness in relation to the other coatings, and it was obtained in the mixed activation–diffusion control between the end of the effect of the activation control and the beginning of the dominant effect of diffusion control.

Innovating Hardness Model of Bioceramics Hydroxyapatite Based on Variety of Synthetic Conditions

Hydroxyapatite is bioceramics which have conducive biocompatible property and have been widely used in bone scaffolds. Although they have potential development, there have not been any matematical model that elaborates the relation between the parameter of synthetic conditons and the mechanical properties of hydroxyapatite. That is why, this article tries to present the relation model between the hardness of hydroxyapatite based on variety of synthetic conditions and sintering temperature, relative density, and the size of particle as well. Developing hardness model is performed on the basis of its relation with the responses from each experiment factor. Validating model which was carried out through comparison between the model and the data of experiments indicates that the model proposed has a good accuracy and capable for explaining the bond between the parameter of synthetic and the hardness of hydroxyapatite.

A Study on Establishing a Microstructure-Related Hardness Model with Precipitate Segmentation Using Deep Learning Method

This paper established a microstructure-related hardness model of a polycrystalline Ni-based superalloy GH4720Li, and the sizes and area fractions of γ’ precipitates were extracted from scanning electron microscope (SEM) images using a deep learning method. The common method used to obtain morphological parameters of γ’ precipitates is the thresholding method. However, this method is not suitable for distinguishing different generations of γ’ precipitates with similar gray values in SEM images, which needs many manual interventions. In this paper, we employ SEM with ATLAS (AuTomated Large Area Scanning) module to automatically and quickly detect a much wider range of microstructures. A deep learning method of U-Net is firstly applied to automatically and accurately segment different generations of γ’ precipitates and extract their parameters from the large-area SEM images. Then the obtained sizes and area fractions of γ’ precipitates are used to study the precipitate stability and microstructure-related hardness of GH4720Li alloy at long-term service temperatures. The experimental results show that primary and secondary γ’ precipitates show good stability under long-term service temperatures. Tertiary γ’ precipitates coarsen selectively, and their coarsening behavior can be predicted by the Lifshitz–Slyozov encounter modified (LSEM) model. The hardness decreases as a result of γ’ coarsening. A microstructure-related hardness model for correlating the hardness of the γ’/γ coherent structures and the microstructure is established, which can effectively predict the hardness of the alloy with different microstructures.