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From AuburnUniversity import Advanced_Analytics_for_Intelligent_ Manufacturing_and_Systems(AIMS)_Lab as future
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In-situ Process Monitoring
Layer-wise Spatial Modeling of Porosity in Additive Manufacturing
The objective of this work is to model and quantify the layer-wise spatial evolution of porosity in parts made using additive manufacturing (AM) processes. An augmented layer-wise spatial log Gaussian Cox process (ALS-LGCP) model is proposed. The ALS-LGCP approach quantifies the spatial distribution of pores within each layer of the AM part and tracks their sequential evolution across layers.
Machine learning–driven in situ process monitoring with vibration frequency spectra
Frequency spectra representation of the microelectromechanical systems (MEMS) vibration sensor signals during subtle process changes is investigated, and the signal patterns uncovered by frequency spectra are utilized to formulate a machine learning–driven in situ process monitoring approach to detect process anomalies in CMP.
Wafer quality monitoring using spatial Dirichlet process based mixed-eﬀect proﬁle modeling scheme
A mixed eﬀect proﬁle monitoring (MEPM) scheme is proposed. The MEPM scheme adaptively groups proﬁle data into clusters and models the inter-cluster variations, consequently, enabling a robust statistical process control scheme for detecting deviant proﬁle data. Capturing the clustering information of the proﬁle data leads to a deep understanding and an accurate modeling of the spatial data.
Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification
A new deep learning-based classiﬁcation methodology, namely epileptic EEG signal classiﬁcation (EESC), is proposed in this paper. This methodology ﬁrst transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and ﬁnally classiﬁes four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure).