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Displaying 1 - 25 of 136

Segmentation of Additive Manufacturing Defects Using U-Net

June 30, 2022
Author(s)
Vivian W. Wong, Max Ferguson, Kincho Law, Yung-Tsun Lee, Paul Witherell
Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to the deficit performance of the fabricated part. X-ray computed tomography (XCT)

Segmentation of Addictive Manufacturing Defects Using U-Net

August 17, 2021
Author(s)
Vivian Wong, Max Ferguson, Kincho Law, Yung-Tsun Lee, Paul Witherell
Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a

Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net

March 23, 2020
Author(s)
Vivian W. Wong, Max Ferguson, Kincho Law, Yung-Tsun Lee, Paul Witherell
Segmentation of defects in additive manufacturing is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can however provide quality control for additive manufacturing. Over recent years, 3D

An Assistive Learning Workflow on Annotating Images for Object Detection

December 9, 2019
Author(s)
Vivian W. Wong, Max K. Ferguson, Kincho H. Law, Yung-Tsun Lee
We present an end-to-end workflow to generate annotated image datasets for object detection. With this workflow, which we call assistive learning, we are able to reduce manual annotation time on two experimental datasets by 79.4% and 83.1%. The

Infrastructure for Model Based Analytics for Manufacturing

December 9, 2019
Author(s)
Sanjay Jain, Anantha Narayanan Narayanan, Yung-Tsun Lee
Multi-resolution simulation models of manufacturing system, such as the virtual factory, coupled with analytics offer exciting opportunities to manufacturers to exploit the increasing availability of data from their corresponding real factory at different

A Standardized Representation of Convolutional Neural Networks for Reliable Deployment of Machine Learning Models in the Manufacturing Industry

August 18, 2019
Author(s)
Max K. Ferguson, Seongwoon Jeong, Kincho H. Law, Anantha Narayanan Narayanan, Svetlana Levitan, Jena Tridivesh, Yung-Tsun Lee
The use of deep convolutional neural networks is becoming increasingly popular in the engineering and manufacturing sectors. However, managing the distribution of trained models is still a difficult task, partially due to the limitations of standardized

A Review Of Machine Learning Applications In Additive Manufacturing

August 17, 2019
Author(s)
Saadia A. Razvi, Shaw C. Feng, Anantha Narayanan Narayanan, Yung-Tsun Lee, Paul Witherell
Variability in product quality continues to pose a major barrier to the widespread application of additive manufacturing (AM) processes in production environment. Towards addressing this barrier, the monitoring of AM processes and the measuring of AM

Complexity and Entropy Representation for Machine Component Diagnostics

July 9, 2019
Author(s)
Srinivasan Radhakrishnan, Yung-Tsun Lee, Sudarsan Rachuri, Sagar Kamarthi
The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs) of a time series. The representation can be used for both

COMPARISON OF DATA ANALYTICS APPROACHES USING SIMULATION

December 9, 2018
Author(s)
Sanjay Jain, Anantha Narayanan Narayanan, Yung-Tsun Lee
Selecting the right data analytics (DA) approach for an application is rather complex. Obtaining sufficient and right kind of data for evaluating these approaches is a challenge. Simulation models can support this process by generating synthetic data where

A Survey on Knowledge Transfer for Manufacturing Data Analytics

November 5, 2018
Author(s)
Seung Hwan Bang, Ronay Ak, Anantha Narayanan Narayanan, Yung-Tsun T. Lee, Hyunbo Cho
Data analytics techniques have been used for numerous manufacturing applications in various areas. A common assumption of data analytics models is that the environment that generates data is stationary, that is, the feature (or label) space or distribution

Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

October 2, 2018
Author(s)
Saideep Nannapaneni, Anantha Narayanan Narayanan, Ronay Ak, David Lechevalier, Thurston Sexton, Sankaran Mahadevan, Yung-Tsun Lee
Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A

Model-Based Approach Towards Integrating Manufacturing Design and Analysis

July 10, 2018
Author(s)
Anantha Narayanan Narayanan, Yung-Tsun T. Lee
As internet technologies and cloud-based services evolve, a new market structure is emerging across various industries, and particularly in the data-driven-analytics-services industry. This could have a great impact in the smart manufacturing sector, where

Automatic Localization of Casting Defects with Convolutional Neural Networks

December 11, 2017
Author(s)
Ronay Ak, Max Ferguson, Yung-Tsun T. Lee, Kincho H. Law
Automatic localization of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. Convolutional neural networks (CNN) have recently shown outstanding performance in both image classification and

Simulating a Virtual Machining Model in an Agent-Based Model for Advanced Analytics

September 25, 2017
Author(s)
David J. Lechevalier, Seungjun Shin, Sudarsan Rachuri, Sebti Foufou, Yung-Tsun T. Lee, Abdelaziz Bouras
Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data