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Energy prediction and optimization for robotic stereoscopic statue processing.

Xu-Hui Cheng1, Fang-Chen Yin2, Cong-Wei Wen3

  • 1Institute of Manufacturing Engineering, HuaQiao University, Xiamen, 361021, China.

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|March 13, 2025
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Summary
This summary is machine-generated.

This study introduces a method to predict and reduce energy consumption in robotic stereoscopic statue rough machining. The approach achieved significant energy savings and reduced processing times.

Keywords:
Energy consumption in stone processingFeed speed dynamic programmingOptimization of robotic energy consumptionRobotic rough machining energy consumption modelling

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Area of Science:

  • Manufacturing Engineering
  • Robotics
  • Sustainable Manufacturing

Background:

  • Energy consumption is a major cost in stone processing, with stereoscopic statue production being particularly energy-intensive.
  • Industrial robots are crucial for stereoscopic statue processing but contribute significantly to high energy usage, especially during rough machining.

Purpose of the Study:

  • To propose a method for predicting energy consumption in robotic stereoscopic statue rough machining.
  • To implement energy-saving optimization strategies for this process.

Main Methods:

  • Developed a robot body power prediction model based on system energy characteristics.
  • Predicted robot spindle power using force-power variation analysis during grinding.
  • Applied a genetic algorithm-based feed-speed dynamic programming method for optimization.

Main Results:

  • The proposed method successfully predicted energy consumption during rough machining.
  • Energy consumption was reduced by 16.9% using the feed-speed dynamic programming method.
  • Processing time was shortened by 19.5%.

Conclusions:

  • The developed method effectively predicts and optimizes energy consumption in robotic stereoscopic statue rough machining.
  • The feed-speed dynamic programming approach offers substantial energy and time savings, contributing to sustainable manufacturing practices.