Journal ArticleDOI
A hybrid transfer learning framework for in-plane freeform shape accuracy control in additive manufacturing
Longwei Cheng,Kai Wang,Fugee Tsung +2 more
- Vol. 53, Iss: 3, pp 298-312
TLDR
A hybrid transfer learning framework is proposed to predict and compensate the in-plane shape deviations of new and untried freeform products based on a small number of previously fabricated products and demonstrates the effectiveness of this framework in predicting the shape deviation and improving the shape accuracy of new products with freeform shapes.Abstract:
Shape accuracy control is one of the quality issues of greatest concern in Additive Manufacturing (AM). An efficient approach to improving the shape accuracy of a fabricated product is to compensat...read more
Citations
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Journal ArticleDOI
When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development
TL;DR: In this article , the state-of-the-art research in human-centered additive manufacturing (AM) with a highlight on the role of artificial intelligence (AI) is reviewed.
Journal ArticleDOI
Family learning: A process modeling method for cyber-additive manufacturing network
TL;DR: A data-driven model called family learning is proposed to jointly model similar-but-non-identical products as family members by quantifying the shared information among these products in the CAMNet by optimizing a similarity generation model based on design factors.
Journal ArticleDOI
Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process
TL;DR: A new method called pyramid ensemble convolutional neural network (PECNN) is proposed to efficiently detect voids and predict the texture of CT images using layer-wise optical images to mitigate the defects.
Journal ArticleDOI
Distribution inference from early-stage stationary data streams by transfer learning
Kai Wang,Jian Li,Fugee Tsung +2 more
TL;DR: An instance-based transfer learning approach which integrates a sufficient amount of auxiliary data from similar processes or products to aid the distribution inference in the authors' target task, and an efficient online algorithm with recursive formulas to update upon every incoming data point.
Journal ArticleDOI
Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection
TL;DR: A more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning (DHTL), which adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and “L2,1+L1”-norm to formulate low dimension feature-representation transfer forThe anomaly.
References
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Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
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Wei Gao,Yunbo Zhang,Devarajan Ramanujan,Karthik Ramani,Yong Chen,Christopher B. Williams,Charlie C. L. Wang,Yung C. Shin,Song Zhang,Pablo D. Zavattieri +9 more
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Two‐step estimation of functional linear models with applications to longitudinal data
Jianqing Fan,Jin-ting Zhang +1 more
TL;DR: In this paper, a two-step local polynomial smoothing spline and kernel method is proposed to estimate the coefficient functions of functional linear models for longitudinal data analysis, which is a simple and powerful two-stage alternative.
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