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This research explores the development of predictive artificial intelligence techniques, specifically artificial neural networks (ANN), as a robust alternative to traditional methods for determining optimal machining parameters that minimize surface roughness (Ra) and tool flank wear (VBmax) during the turning of Stellite 6, a cobalt-based superalloy. A real-world machining experiment was conducted to assess the proposed model's effectiveness in accurately predicting and optimizing surface roughness and tool flank wear. The findings reveal a high level of consistency between the predicted and experimental values, validating the model's predictive accuracy.

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Prediction of Surface Roughness and Tool Wear During Turning of Stellite 6 Through Artificial Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Riadh Saidi,
  • Tarek Mabrouki,
  • Salim Belhadi,
  • Mohamed Athmane Yallese

摘要

This research explores the development of predictive artificial intelligence techniques, specifically artificial neural networks (ANN), as a robust alternative to traditional methods for determining optimal machining parameters that minimize surface roughness (Ra) and tool flank wear (VBmax) during the turning of Stellite 6, a cobalt-based superalloy. A real-world machining experiment was conducted to assess the proposed model's effectiveness in accurately predicting and optimizing surface roughness and tool flank wear. The findings reveal a high level of consistency between the predicted and experimental values, validating the model's predictive accuracy.