Formation of Graphite Phase in Powder Metallurgical Ultrahigh Carbon Steels with High Wear Resistance

Han, Liuliu; Liu, Yong; Chen, Gang; Li, Kun; Wang, Jiawen; Zhou, Rui

doi:10.1002/adem.201800002

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…In this work, we investigate classic machine learning methods to get the best performance model and discuss the improvements for generalizing the predictions. The methodology developed herein is applied to ultra-high carbon steels, that are widely used due to their high wear resistance, strength and hardness [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Classification of Ultra High Carbon Steel Micrographs

Muñoz-Ródenas,

Miguel,

García-Sevilla

et al. 2023

KEM

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The aim of this investigation is to analyze the performance of several supervised machine learning algorithms for solving the automatic classification problem of steel image microstructures. We conducted an experiment using a public-domain dataset of Ultra High Carbon Steel Micrographs (UHCSM). This image database consists of a collection of scanning electron micrographs (SEM) taken from samples of a commercial roll-mill casting with a nominal carbon of 2%. Heat treatments such as annealing, water quenching, air and furnace cooling were performed on steel samples so primary microconstituents could be found in micrographs. Each of these microconstituents defines each of the categories of classification to be accomplished by machine learning algorithms. The heat treatments brought about 4 usable classes (sets of images) of primary microconstituents: pearlite, spheroidite, proeutectoid cementite network, pearlite containing spheroidite. All labeled images are prepared to improve models' accuracy in a preprocessing stage so that the image dataset is ready for feature extraction. In order to develop classification models, we put to the test distinct machine learning approaches by working with Matlab's classification learner application where we perform automated training to search for the best classification model type, including Decision Trees, Support Vector Machines (SVM), Discriminant Analysis, Nearest Neighbors, Naive Bayes, Ensemble k-NN, and Neural Network classification. For obtaining the features of the images (feature extraction) we choose the method of Bag-of-features with 400 words for the first experiment, and 327 words by removing less important features for a second experiment. The experimented models reached very different accuracy values on training, with SVM as the best classifier which gets 91.6% accuracy. We can conclude that classic machine learning algorithms solve the classification, but an accuracy improvement can be reached by investigating deep learning techniques.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Classification of Ultra High Carbon Steel Micrographs

Muñoz-Ródenas,

Miguel,

García-Sevilla

et al. 2023

KEM

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“…This problem can impair neck formation during sintering, resulting in matrix discontinuities, ultimately affecting the mechanical properties of the sintered composite . Different processing techniques can be used to improve the dispersion of lubricant particles in the metallic matrix, such as the in situ formation of the lubricant phase during sintering by the dissociation of a precursor powder . In addition, sintering may be assisted by liquid phase, which flows along the interfaces of matrix‐lubricant particles, potentially contributing to the homogenization of the mixture, which remains solid along the entire thermal cycle .…”

Section: Introductionmentioning

confidence: 99%

Effect of Liquid Phase–Assisted Sintering on the Microstructure, Mechanical Properties, and Tribological Behavior of Self‐Lubricating Ferrous Composites

et al. 2019

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A current challenge to the development of self‐lubricating metal matrix composites by conventional powder metallurgy is the dispersion of lubricant lamellar particles between the metal powders during mixing and compaction. This event hinders neck formation during sintering, negatively affecting the mechanical properties of the material. Sintering in the presence of a liquid phase can improve both densification and the distribution of lubricant particles in the microstructure of the composite material. Herein, the effect of the addition of copper in composite powder mixtures as the liquid‐phase agent on the microstructure, mechanical properties, and tribological behavior of ferrous matrix self‐lubricating composites is evaluated. Increasing the contents of copper improves the homogenization of alloying elements, the continuity of the matrix, and the morphology of discontinuities. These phenomena result in self‐lubricating composites with superior mechanical properties and tribological behaviors.

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Formation of Graphite Phase in Powder Metallurgical Ultrahigh Carbon Steels with High Wear Resistance

Cited by 2 publications

References 33 publications

Machine Learning Approaches for Classification of Ultra High Carbon Steel Micrographs

Machine Learning Approaches for Classification of Ultra High Carbon Steel Micrographs

Effect of Liquid Phase–Assisted Sintering on the Microstructure, Mechanical Properties, and Tribological Behavior of Self‐Lubricating Ferrous Composites

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