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Related Experiment Video

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P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study.

Dian Huang1, Ming Li1, Jingfei Fu1

  • 1School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a platform-to-platform cloud manufacturing method using deep learning and additive manufacturing to reduce production time and costs. The approach enables object-to-object fabrication from photos, with successful 3D model generation and printing.

Keywords:
3D printing3D reconstructionP2P cloud manufacturingdeep learningpersonalized business modelreverse engineering

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Area of Science:

  • Manufacturing Engineering
  • Computer Science
  • Materials Science

Background:

  • Traditional manufacturing processes face challenges with long production cycles and high costs.
  • Personalized custom business models require efficient methods for bespoke product creation.
  • Integrating advanced technologies is crucial for modernizing manufacturing.

Purpose of the Study:

  • To propose a novel platform-to-platform (P2P) cloud manufacturing method.
  • To enable object-to-object fabrication from 2D images using deep learning and additive manufacturing.
  • To reduce production cycle times and manufacturing costs for personalized products.

Main Methods:

  • Development of an object detection extractor using the YOLOv4 algorithm.
  • Construction of a 3D data generator leveraging DVR technology for 2D-to-3D conversion.
  • Integration of deep learning and additive manufacturing (AM) within a P2P cloud framework.
  • Case study involving online sofa and car photos for a 3D printing service scenario.

Main Results:

  • Achieved object recognition rates of 59% for sofas and 100% for cars.
  • Demonstrated a 2D-to-3D data conversion time of approximately 60 seconds.
  • Successfully manufactured three unindividualized and one individualized 3D printed model, maintaining original shape.
  • Validated the proposed method for personalized 3D model transformation and fabrication.

Conclusions:

  • The P2P cloud manufacturing method effectively bridges the gap between digital concepts and physical products.
  • The integration of YOLOv4 and DVR technology facilitates rapid 3D model generation from images.
  • The approach supports personalized customization, offering a viable solution for efficient, cost-effective manufacturing.