Improving Reusability of Electronic Components

Abstract

The project “Improving Reusability of Electronic Components” deals with the ever-increasing concern for electronic waste, which calls for a need for sustainability in terms of electronic components\' disposal and repurposing. Electronic devices have seen massive growth due to technological advancement and are increasingly used in every corner of the globe, thereby turning into an immense environmental issue called e-waste. Based on the focus on research and development, this project establishes a robust system that shall estimate, classify, and strengthen the reusable potential of e-components, majorly focusing on laptops, using techniques of artificial intelligence and machine learning.The prediction model classified the condition of the various e-components into either fully working or non-functioning units which fall within the disposable category. A predicted category aided by the condition of a particular component guides the decision-making process to reuse, repair, recycle, or dispose of it. It actually presents a two-condition approach by giving actionable output on whether or not the device is fully functioning or non-functional. For functioning devices, it recommends possible fixes or upgrades and thereby extends the device\'s useful life. The system identifies components to be recycled for non-functional devices or states whether they can be disposed of as e-waste.Using automated classification and condition specific guidelines, the project aims?to reduce e-waste, reduce environmental impact and prolongthe lifespan of e-components. As a whole, the system seeks to promote sustainable usage, definitively achieving the reuse?and recycling of valuablee-waste materials, making the electronics industry a greener one.

Introduction

This project addresses the global challenge of e-waste by using machine learning and a web-based application to classify and assess the reusability of electronic components. It aims to promote sustainable e-waste management through automation and user-friendly technology.

Key Objectives:

• Identify electronic components from uploaded images.

• Classify them into three reusability categories:

  • Fully Functioning (ready for reuse or donation)

  • Partially Functioning (repairable or repurposable)

  • Not Functioning (requires proper recycling)

• Provide actionable recommendations for each category.

• Educate users and connect them to recycling centers via APIs.

Core Components:

1. Deep Learning Model:

• Dataset: 21,000+ labeled images (e.g., laptops, circuit boards, fans).

• Model: Convolutional Neural Network (CNN) built using TensorFlow & Keras.

• Performance: High accuracy in classifying electronic components.

2. Web Application:

• Built with Streamlit for ease of use.

• Features:

  • Image upload interface

  • Real-time predictions

  • Reusability status and suggestions

3. System Architecture:

• Frontend: Streamlit app for interaction.

• Backend: Handles image classification and logic for reusability evaluation.

• Optional Integrations: APIs for recycling centers, data storage for results.

User Workflow:

1. User uploads an image via the web app.

2. The model classifies the component.

3. The system assigns a reusability category.

4. Recommendations (repair, reuse, recycle) are provided.

5. Optional: Save results or find local recycling centers.

Methodology Highlights:

• Image Preprocessing: Resize and normalize images for model efficiency.

• Model Training: 10 epochs using Adam optimizer, sparse categorical crossentropy.

• Evaluation: Accuracy, precision, and recall metrics.

• Deployment: Local and cloud deployment (e.g., AWS, Heroku).

Literature Insights:

• E-waste stats: Over 53 million metric tons produced globally in 2019.

• Gaps: Lack of real-time, AI-powered, user-friendly tools for consumers.

• ML Benefits: CNNs can accurately classify components, improving recycling workflows.

• Circular Economy: Encourages reusability, reduces waste, and supports sustainable resource use.

Impact & Relevance:

• Bridges the tech-sustainability gap.

• Empowers users to take action on e-waste with AI assistance.

• Aligns with global environmental goals and circular economy principles.

Conclusion

Project \"Improving Reusability of Electronic Components\" demonstrated the potential of using machine learning and user-friendly interfaces to address the growing challenge in managing electronic waste. The system developed is highly scalable and efficient for classifying electronic components and estimating their reusability based on functionality. By using a deep learning model trained on a dataset of 21,000+ images, the system achieved high accuracy in identifying electronic components and classified them into three main conditions: • Fully Functioning: Ready for direct reuse or donation. • Partially Functioning: Suitable for repair or repurposing. • Non-Functioning: Requires recycling or proper disposal. The Streamlit-based web application ensures accessibility by allowing easy image uploads and provides actionable insights based on the component\'s condition. The recommendations of repair, reuse, or recycling are in harmony with the concept of a circular economy and would help reduce e-waste\'s environmental impact. This project fills the gap between technology and sustainability by offering practical tools for individuals, recyclers, and organizations to make the right decisions concerning electronic waste.

References

[1] On E-Waste and Sustainability Forti, V., Balde, C.P., Kuehr, R., & Bel,?G. (2020).Global?E-Waste Monitor 2020. International Telecommunication Union (ITU) UNU UNU International Institute for Global Health (UNU-IIGH) 317Understanding and managing the impact of plastic pollution 317International Solid Waste?Association (ISWA) ? Such statistics highlight?challenges in global e-waste and recycling practices: • European Commission. (2019). Circular Economy Action Plan. • It includes?discussions on the circular economy and ways to try to prolong a product’s life. [2] Machine Learning?and Image Classification • LeCun, Y., Bengio, Y., &?Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. • Covers the basics of deep learning and how it?is used for image classification. • Simonyan,?K., &Zisserman, A. (2014). ImageNet Classification with Deep?Convolutional Neural Networks. arXiv preprint?arXiv:1409.1556. • Explain how?CNN architectures such as VGGNet can be used for image classification tasks. • Howard, A.G., et al. (2017). MobileNets: Efficient Convolutional Neural Networks for?Mobile Vision Applications [1704.04861]?(arXiv) [3] Reusability and E- Waste Management • Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D.,?Schnellmann, M., &Böni, H. (2005). Electric Waste: A?Global Perspective International Journal?of Impact Assessment & Project Appraisal, 21(4), 232-241. • An?overview of e-waste and potential repair and recycling opportunities. • Jain, A., & Gupta, R. (2021). AI In E-Waste Management: Taking A?Step Towards Sustainability Sustainable Computing: Informatics and?Systems, 30, 100526. [4] Streamlit and Application Development • Streamlit Documentation. (n.d.). StreamlitDocumentationThe fastest way to build data?apps. • Streamlit is?open-source software, so we love sharing ideas with the community. [5] Environmental Imapct and Recycling • Kang, H.-Y., &Schoenung, J.M.?(2005). Review of U.S. Infrastructure and Technology Options for Electronic Waste?Recycling. Resources, Conservation?and Recycling, 45(4): 368-400. ? Analyzes environmental?implications of e-waste recycling technology

Copyright

Copyright © 2025 D Joy, M S Yeshwanth, K Snehith Reddy, A Vignesh Raju, K Bhargav Teja, Mr. Shankar J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.