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Taylor-Gorilla troops optimized deep learning network for surface roughness estimation.

Syed Jahangir Badashah1, Shaik Shafiulla Basha2, Shaik Rafi Ahamed3

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Summary
This summary is machine-generated.

A novel Taylor-Gorilla troops optimizer-based Deep Neuro-Fuzzy Network (Taylor-GTO based DNFN) estimates machined surface roughness without contact. This method offers accurate surface roughness assessment, overcoming limitations of traditional contact stylus profilometry.

Keywords:
DNFNDeep learningGorilla troop’s optimizerTaylor seriessurface roughness estimation

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Surface roughness assessment is crucial for machined product quality.
  • Contact stylus profilometry is common but causes surface degradation.
  • A non-contact, accurate method for surface roughness estimation is needed.

Purpose of the Study:

  • To propose a novel, non-contact surface roughness assessment technique.
  • To develop a Deep Neuro-Fuzzy Network (DNFN) optimized by a hybrid algorithm for roughness estimation.
  • To evaluate the performance of the proposed Taylor-GTO based DNFN model.

Main Methods:

  • A hybrid optimization algorithm, Taylor-Gorilla Troops Optimizer (Taylor-GTO), was developed by combining Taylor series and Gorilla's troop optimizer.
  • A Deep Neuro-Fuzzy Network (DNFN) was trained using the Taylor-GTO algorithm for surface roughness estimation.
  • The methodology involved pre-processing, data augmentation, feature extraction, feature fusion, and roughness estimation.

Main Results:

  • The proposed Taylor-GTO based DNFN model achieved high accuracy in surface roughness estimation.
  • The model demonstrated minimal Mean Absolute Error (0.403), Mean Square Error (0.416), and Root Mean Square Error (1.149).
  • The developed technique effectively estimates surface roughness without causing workpiece degradation.

Conclusions:

  • The Taylor-GTO based DNFN offers a reliable and accurate non-contact method for surface roughness assessment.
  • This approach overcomes the limitations of traditional contact-based methods, preventing surface damage.
  • The study highlights the potential of hybrid AI optimization techniques in precision manufacturing quality control.