2 research outputs found
A Novel Approach for Ridge Detection and Mode Retrieval of Multicomponent Signals Based on STFT
Time-frequency analysis is often used to study non stationary multicomponent
signals, which can be viewed as the surperimposition of modes, associated with
ridges in the TF plane. To understand such signals, it is essential to identify
their constituent modes. This is often done by performing ridge detection in
the time-frequency plane which is then followed by mode retrieval.
Unfortunately, existing ridge detectors are often not enough robust to noise
therefore hampering mode retrieval. In this paper, we therefore develop a novel
approach to ridge detection and mode retrieval based on the analysis of the
short-time Fourier transform of multicomponent signals in the presence of
noise, which will prove to be much more robust than state-of-the-art methods
based on the same time-frequency representation
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Laser-directed-energy deposition (DED) offers advantages in additive
manufacturing (AM) for creating intricate geometries and material grading. Yet,
challenges like material inconsistency and part variability remain, mainly due
to its layer-wise fabrication. A key issue is heat accumulation during DED,
which affects the material microstructure and properties. While closed-loop
control methods for heat management are common in DED research, few integrate
real-time monitoring, physics-based modeling, and control in a unified
framework. Our work presents a digital twin (DT) framework for real-time
predictive control of DED process parameters to meet specific design
objectives. We develop a surrogate model using Long Short-Term Memory
(LSTM)-based machine learning with Bayesian Inference to predict temperatures
in DED parts. This model predicts future temperature states in real time. We
also introduce Bayesian Optimization (BO) for Time Series Process Optimization
(BOTSPO), based on traditional BO but featuring a unique time series process
profile generator with reduced dimensions. BOTSPO dynamically optimizes
processes, identifying optimal laser power profiles to attain desired
mechanical properties. The established process trajectory guides online
optimizations, aiming to enhance performance. This paper outlines the digital
twin framework's components, promoting its integration into a comprehensive
system for AM.Comment: 12 Pages, 10 Figures, 1 Table, NAMRC Conferenc