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Updated: Jul 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Design quality and robustness with neural networks.

I G Ali¹, Y T Chen

¹GE Corporate Research and Development Information Technology Lab, Niskayuna, NY 12301, USA.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This study developed accurate neural network models for injection molding quality control. These models map process data to product quality, aiding design and troubleshooting for improved manufacturing.

Area of Science:

Industrial Engineering
Materials Science
Data Science

Background:

Industrial data often contains noise, requiring advanced methodologies for accurate modeling.
Injection molding processes generate complex datasets linking process parameters to product quality.

Purpose of the Study:

To create precise neural network models for multiple quality characteristics in injection molding.
To map process measurements to product quality for design and operational improvements.

Main Methods:

Utilized designed and other experimental data for model development.
Employed linear regression, decision tree induction, nonlinear regression, and stepwise neural networks for feature selection and comparison.
Developed a final neural network model with three inputs, one hidden layer, and five outputs.

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Main Results:

Achieved high accuracy in simultaneously modeling five critical to quality (CTQ) variables.
The neural network model effectively maps process measurements to product quality outcomes.
Model visualization provided validation and deeper insights into the process-quality relationship.

Conclusions:

Neural network models offer a robust solution for managing noisy industrial data in injection molding.
The developed models support product and process design, material selection, and operational window definition.
Accurate modeling of CTQs enhances manufacturing efficiency and product reliability.