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Related Experiment Video

Updated: Feb 17, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Forward and reverse mapping for milling process using artificial neural networks.

Rashmi L Malghan1, Karthik Rao M C2, Arun Kumar Shettigar2

  • 1Department of Mechanical Engineering, National Institute of Technology, Surathkal, Karnataka, India.

Data in Brief
|December 1, 2017
PubMed
Summary
This summary is machine-generated.

This study uses artificial neural networks (ANN) to predict machining responses like cutting force and surface finish for AA6061-4.5%Cu-5%SiCp composites. The models optimize process parameters for desired outcomes.

Keywords:
ANNForward mappingMilling processReverse mapping

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Intelligence

Background:

  • Machining of metal matrix composites, specifically AA6061-4.5%Cu-5%SiCp, presents challenges in optimizing process parameters.
  • Accurate prediction of machining responses such as cutting force, surface finish, and power consumption is crucial for efficient manufacturing.
  • Artificial Neural Networks (ANN) offer a powerful tool for modeling complex relationships in machining processes.

Purpose of the Study:

  • To develop and validate an ANN-based model for predicting machining responses during the milling of AA6061-4.5%Cu-5%SiCp composite.
  • To create a comparative reverse model to recommend optimal process parameters (spindle speed, feed rate, depth of cut) for desired machining outcomes.
  • To assess the efficacy of the developed modeling approaches in forecasting machining performance and parameter settings.

Main Methods:

  • Utilized a forward back propagation neural network approach (ANN) to model the relationship between process parameters and machining responses.
  • Input parameters included spindle speed, feed rate, and depth of cut.
  • Developed a reverse model to determine optimal parameter settings based on desired response values.

Main Results:

  • The ANN model successfully predicted key machining responses, including cutting force, surface finish, and power utilization.
  • The reverse model effectively recommended optimal process parameter settings to achieve desired machining outcomes.
  • The modeling approaches demonstrated high proficiency in forecasting machining benefits and parameter configurations.

Conclusions:

  • ANN-based modeling is a proficient technique for predicting machining responses and optimizing process parameters for AA6061-4.5%Cu-5%SiCp composites.
  • The developed models provide valuable insights for manufacturers seeking to improve efficiency and achieve specific machining quality targets.
  • This data-driven approach supports informed decision-making in advanced composite material machining.