Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

https://dc.engconfintl.org/cgi/viewcontent.cgi?article=1032&context=superalloys_ii

A critical review of high entropy alloys and related concepts

Microstructures and properties of high-entropy alloys

High-entropy alloys (HEAs) are alloys with five or more principal elements. Due to the distinct design concept, these alloys often exhibit unusual properties. Thus, there has been significant interest in these materials, leading to an emerging yet exciting new field. This paper briefly reviews some critical aspects of HEAs, including core effects, phases and crystal structures, mechanical properties, high-temperature properties, structural stabilities, and corrosion behaviors. Current challenges and important future directions are also pointed out.

https://www.tandfonline.com/doi/pdf/10.1080/21663831.2014.912690

High-Entropy Alloys: A Critical Review

Fatigue behavior of Al0.5CoCrCuFeNi high entropy alloys

Fatigue behavior of a wrought Al 0.5 CoCrCuFeNi two-phase high-entropy alloy

A review on the fatigue behavior of Ti-6Al-4V fabricated by electron beam melting additive manufacturing

Spatial defect patterns generated during integrated circuit (IC) manufacturing processes contain information about potential problems in the processes. The detection of these defect patterns is crucial to improve yield and reliability in IC manufacturing. This paper proposes a multistep defect analysis approach that provides clustering results with different levels of accuracy. A defect denoising step, based on the K th nearest-neighbor noise removal technique, determines the existence of any clustered local defects on a wafer. If local defects exist, the denoising step separates local defects from global defects. A defect clustering step applies a similarity-based clustering technique to group the local defects into clusters according to their spatial locations. A pattern identification step identifies the pattern for each of the local defect clusters (i.e., linear, curvilinear, amorphous, or ring-shaped patterns) via various model selection criteria. Finally, a fine tuning step is applied in order to improve the accuracy of the clustering performance. The fine tuning step is based on model-based clustering with a fixed number of clusters and known patterns for each cluster. The results of both simulated and real wafer map data demonstrate the potential of our approach, both in terms of computational speed and detection accuracy, for analyzing general defect patterns generated during the IC fabrication process.

Tao Yuan

Papers

Fatigue behavior of Al0.5CoCrCuFeNi high entropy alloys

Fatigue behavior of a wrought Al 0.5 CoCrCuFeNi two-phase high-entropy alloy

A review on the fatigue behavior of Ti-6Al-4V fabricated by electron beam melting additive manufacturing

Detection of Spatial Defect Patterns Generated in Semiconductor Fabrication Processes

A Bayesian approach to modeling two-phase degradation using change-point regression