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September 17, 2025JST Smart Systems and DevicesOpen Access

Research on Modeling and Optimization for Surface Roughness of Al6061 Spherical in Ultra-Precision Turning Based on Neural Network and Bee Colony Optimization Algorithm

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Authors

NHNgoc Dong HoangXDXuan Bien DuongKBKim Hoa Bui

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Overview

This research demonstrates neural network and artificial bee colony optimization for improving surface roughness in Al6061, suggesting effective strategies in precision machining.

Key Points

  • The best surface roughness prediction was achieved with an ANN model showing a coefficient of determination of 0.98.
  • Cutting parameters of spindle speed at 823 rev/min, feed rate of 13 mm/min, and depth of cut of 1 µm minimized roughness to 0.76 nm.
  • The research integrates neural networks with the artificial bee colony algorithm for optimizing machining operations.
  • Findings can extend to other materials and complex geometries in precision machining applications.

Cite This Study

Hoang et al. (2025) studied this question.

synapsesocial.com/papers/68d45e6231b076d99fa5ed14https://doi.org/10.51316/jst.184.ssad.2025.35.3.5
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A comparative modeling and optimization of surface roughness in the end milling of Al 3003 subjected to non-equal angular pressing (NECAP)2024 · 1 citations
  2. 2Development of Hybrid ANN–Genetic Algorithm Model for Optimization of Surface Roughness in CNC Turning of AISI 1040 Steel2026
  3. 3Hybrid ANN–GA and Machine Learning Approaches for Surface Roughness Prediction in CNC Step Turning of Aluminium Alloy2026
  4. 4A Novel Hybrid RSM-ANN Model for Surface Roughness Prediction in Turning of Al 6061 Alloy2024
  5. 5Research on optimization of surface roughness of aspherical aluminum during Single-Point Diamond Turning using BBK model and PSO algorithm2025