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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Teaching machines to optimizing machining parameters: using independent fuzzy logic controller and image data.

Harshavardhan Mamledesai1, Yufan Zheng1, Rafiq Ahmad1

  • 1Laboratory of Intelligent Manufacturing, Design, and Automation (LIMDA), Department of Mechanical Engineering, NW University of Alberta, 9211 116 street, Edmonton, Canada.

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PubMed
Summary

This study introduces a novel hybrid system using image feedback and a convolution neural network with fuzzy logic to optimize machining parameters. The method ensures tool life optimization, adapting to various materials and tool geometries for enhanced manufacturing efficiency.

Keywords:
CNNFuzzy logicParameter optimizationTool condition monitoringVision systems

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence in Manufacturing
  • Materials Science

Background:

  • Machining parameter optimization is crucial for manufacturing efficiency, but consensus on objectives is lacking.
  • Existing methods often prioritize cost or time over manufacturer-specified tool life.
  • Current tool life optimization systems struggle with adaptability across different materials and tool geometries.

Purpose of the Study:

  • To develop a novel, adaptable system for optimizing machining parameters based on tool life.
  • To address the limitations of existing closed-loop systems in handling diverse machining applications.
  • To leverage advanced AI techniques for real-time machining process control.

Main Methods:

  • A hybrid system combining image feedback with a convolution neural network (CNN) and fuzzy logic.
  • Online monitoring of tool wear through insert imaging.
  • Real-time adjustment of machining parameters (cutting speed, feed, depth of cut).

Main Results:

  • The developed system effectively optimizes machining parameters using tool life as the primary objective.
  • The image-based feedback system demonstrates invariance to different tool geometries and material types.
  • Experimental validation in turning applications confirms the methodology's robustness.

Conclusions:

  • The novel hybrid system offers a generalized solution for machining parameter optimization focused on tool life.
  • The approach enhances adaptability and reliability in manufacturing processes.
  • The methodology holds potential for broader application across various machining operations.