Mojtaba Khanzadeh

TL;DR: In this paper, a real-time porosity prediction method is developed using morphological characteristics of the melt pool boundary (i.e., features obtained via functional principal component analysis (FPCA)).

TL;DR: A novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built is proposed and is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected.

TL;DR: This study proposes a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, outdegree weight, clustering coefficient, node betweenness, and eigenvector centrality.

TL;DR: In this paper, an unsupervised machine learning (ML) approach called the self-organizing map (SOM) was used to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates.

TL;DR: In this article, a statistical process control (SPC) approach is proposed to detect process changes as soon as it occurs based on predefined distribution of the monitoring statistics, and an online dual control charting system is proposed by leveraging multivariate T 2 and Q control charts to detect changes in extracted low dimensional features and residuals, respectively.