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Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Additive manufacturing (AM) has already proved itself to be the potential alternative to widely-used subtractive manufacturing due to its extraordinary capacity of manufacturing highly customized products with minimum material wastage.
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network
It is beneficial to generate effective artificial sample data for the abnormal states to make a more balanced training set.
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing
Since AM typically fabricates a small number of customized products, this paper aims to create an online learning-based strategy to mitigate the new defects in AM process while minimizing the number of samples needed.
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems
Fault diagnosis is to identify process faults that cause the excessive dimensional variation of the product using dimensional measurements.
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs.
Robust Tensor Principal Component Analysis: Exact Recovery via Deterministic Model
Tensor, also known as multi-dimensional array, arises from many applications in signal processing, manufacturing processes, healthcare, among others.
A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors
Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images.
