Manufacture and mechanical-tribological assessment of diamond-reinforced Cu-based coatings for cutting/grinding tools

This work proposes the design of laser-textured patterns for the development of surfaces with high anchor densities capable of promoting solid mechanical bonding of highly abrasive sintered diamond/carbide-reinforced. Cu-based composite coatings with excellent wear resistance. The results show the development of dense and well-adhered coatings onto the patterns and how the higher nucleation of TiC/Fe3C on the diamond surfaces favours its greater bonding within the matrix, preventing its graphitization, and detachment. Reductions above 40% and 60% in the CoF were observed, under dry conditions, for coatings reinforced with 10 & 30 wt% of diamonds, respectively. Depending on the percentage of diamond used, the best glass/coating and alumina/coating wear volume ratios were around 20 and 6. These values would indicate that these composite coatings show high performance and therefore could be used to make tools for grinding and/or cutting glass and ceramic materials

An Efficient Angle-based Universum Least Squares Twin Support Vector Machine for Classification

Universum-based support vector machine incorporates prior information about the distribution of data in training of the classifier. This leads to better generalization performance but with increased computation cost. Various twin hyperplane-based models are proposed to reduce the computation cost of universum-based algorithms. In this work, we present an efficient angle-based universum least squares twin support vector machine (AULSTSVM) for classification. This is a novel approach of incorporating universum in the formulation of least squares-based twin SVM model. First, the proposed AULSTSVM constructs a universum hyperplane, which is proximal to universum data points. Then, the classifying hyperplane is constructed by minimizing the angle with the universum hyperplane. This gives prior information about data distribution to the classifier. In addition to the quadratic loss, we introduce linear loss in the optimization problem of the proposed AULSTSVM, which leads to lesser computation cost of the model. Numerical experiments are performed on several benchmark synthetic, real-world, and large-scale datasets. The results show that proposed AULSTSVM performs better than existing algorithms w.r.t. generalization performance as well as computation time. Moreover, an application to Alzheimer’s disease is presented, where AULSTSVM obtains accuracy of 95% for classification of healthy and Alzheimers subjects. The results imply that the proposed AULSTSVM is a better alternative for classification of large-scale datasets and biomedical applications.</jats:p

Manufacture and mechanical-tribological assessment of diamond-reinforced Cu-based coatings for cutting/grinding tools

Available online 26 September 2022This work proposes the design of laser-textured patterns for the development of surfaces with high anchor densities capable of promoting solid mechanical bonding of highly abrasive sintered diamond/carbide-reinforced. Cu-based composite coatings with excellent wear resistance. The results show the development of dense and well-adhered coatings onto the patterns and how the higher nucleation of TiC/Fe3C on the diamond surfaces favours its greater bonding within the matrix, preventing its graphitization, and detachment. Reductions above 40% and 60% in the CoF were observed, under dry conditions, for coatings reinforced with 10 & 30 wt% of diamonds, respectively. Depending on the percentage of diamond used, the best glass/coating and alumina/ coating wear volume ratios were around 20 and 6. These values would indicate that these composite coatings show high performance and therefore could be used to make tools for grinding and/or cutting glass and ceramic materials.This work has been supported by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Inter-nacionalizacao (POCI) with the project references POCI-01-0145- FEDER-006941 and national funds through the Portuguese Foundation for Science and Technology, under the projects PTDC/CTMCOM/ 30416/2017 and UIDB/00285/2020

Machine learning techniques for the diagnosis of alzheimer's disease: A review

© 2020 ACM. Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer's. Many novel approaches are proposed by researchers for classification of Alzheimer's disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer's is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer's with possible future directions