Hybrid Model for Sustainability Research in Machining Process Revealed by Researchers

Strategic Insights -

Researchers at Umm Al-Qura University in Mecca, Saudi Arabia, have developed a hybrid model combining machine learning and theoretical approaches to study the trade-off between the lifetime of machined materials and energy consumption in ultrasonic assisted milling (UAM) process. The study focused on cutting Inconel 718, a challenging material for machining due to its high hardness and resistance to corrosion. The researchers conducted a series of experiments using a full factorial design, considering key process factors and machinability indicators.

Key Takeaways:

  • The developed hybrid model uses an adaptive neuro-fuzzy inference system (ANFIS) to correlate input and responses, allowing for accurate prediction of energy consumption and fatigue life of machined components.
  • The model has been verified through comparison with experimental fatigue life values of 12 samples, demonstrating a mean absolute percentage error of 13%.
  • The application of ultrasonic vibration significantly improves the life cycle of the material up to 300% and energy efficiency by more than 40%.
  • The study contributes to the development of sustainable machining processes by identifying the role of ultrasonic vibration in enhancing material lifetime and energy efficiency.

Statistics:

  • 13% mean absolute percentage error for model predictions based on 12 sets of experiments.
  • 300% improvement in material life cycle through ultrasonic vibration application.
  • 40% improvement in energy efficiency through ultrasonic vibration application.
  • 12 samples machined under different UAM conditions and applied fatigue loads using plane-bending fatigue test.

Sources:

  • NewsRx. Data on Sustainability Research Discussed by Researchers at Umm Al-Qura University (Fusion of Machine Learning and Physics-based Model To Identify the Role of Ultrasonic Vibration On Fatigue Life and Sustainability of Machined Inconel 718). Ecology, Environment & Conservation. October 17, 2025; 90.
  • The International Journal of Advanced Manufacturing Technology. "Fusion of Machine Learning and Physics-based Model To Identify the Role of Ultrasonic Vibration On Fatigue Life and Sustainability of Machined Inconel 718." 2025.
  • Umm Al-Qura University, Coll Engn & Comp Al Qunfudhah, Dept. of Industrial and Mechanical Engineering, Mecca, Saudi Arabia.