Session: DFMLC-04-01: Design for Manufacturing , Assembly and Product Service Systems

Paper Number: 71403

Start Time: August 19, 10:00 AM

71403 - Deep Learning and Machine Learning Techniques to Classify Electrical and Electronic Equipment 

Remanufacturing sites often receive products with different brands, models, conditions, and quality levels. Proper sorting and classification of the waste stream is a primary step in efficiently recovering and handling used products. The correct classification is particularly crucial in future electronic waste (e-waste) management sites equipped with Artificial Intelligence (AI) and robotic technologies. Robots should be enabled with proper algorithms to recognize and classify products with different features and prepare them for assembly and disassembly tasks. In this study, two categories of machine learning (ML) and deep learning techniques are used to classify consumer electronics. The ML models include Naïve Bayes with Bernoulli, Gaussian, and Multinomial distributions, and deep learning models include VGG-16, GoogLeNet, Inception-v3, Inception-v4, and ResNet-50. The above-mentioned models are used to classify three laptop brands, including Apple, HP, and ThinkPad. First the Edge Histogram Descriptor (EHD) is used to extract features as inputs to ML models for classification. The deep learning models use laptop images without pre-processing on feature extraction. The trained models are slightly overfitting due to the limited dataset and complexity of model parameters. Despite slight overfitting, the models can identify each brand. The findings prove that deep learning models outperform machine learning. Among the deep learning models, GoogLeNet has the highest performance in identifying the laptop brands.

Presenting Author: Shuaizhou Hu University of Florida

Authors:

Shuaizhou Hu University of Florida
Xinyao Zhang University of Florida
Hao-yu Liao University of Florida
Xiao Liang University at Buffalo, SUNY
Minghui Zheng University at Buffalo, SUNY
Sara Behdad University of Florida