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Intelligent temperature modeling in robotic cortical bone milling process based on teaching-learning-based

Vahid Tahmasbi1, Amir Hossein Rabiee1

  • 1Department of Mechanical Engineering, Arak University of Technology, Arak, Iran.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an adaptive neuro-fuzzy inference system (ANFIS) to predict bone milling temperatures, crucial for preventing thermal necrosis in orthopedic surgery. The ANFIS model accurately estimated temperatures, ensuring safer surgical procedures.

Keywords:
ANFIS networkautomatic millingboneteaching-learning algorithmtemperature

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Area of Science:

  • Biomechanical Engineering
  • Medical Engineering
  • Orthopedic Surgery

Background:

  • Bone milling is a critical biomechanical process in orthopedic surgery, dentistry, and fracture treatment.
  • Elevated temperatures during bone milling can cause thermal necrosis, a significant surgical complication.
  • Automatic numerical control (ANC) surgical milling machines have advanced this procedure.

Purpose of the Study:

  • To model and estimate temperature variations during robotic bone milling.
  • To optimize the ANFIS model parameters using the teaching-learning-based optimization (TLBO) algorithm.
  • To ensure the accuracy and reliability of temperature prediction in bone milling.

Main Methods:

  • Design of experiments considering rotational speed, feed rate, depth of cut, and tool diameter.
  • Development of an adaptive neuro-fuzzy inference system (ANFIS) for temperature modeling.
  • Optimization of ANFIS parameters using the teaching-learning-based optimization (TLBO) algorithm.
  • Training (75%) and testing (25%) the ANFIS model with experimental data.

Main Results:

  • The ANFIS model accurately predicted temperatures during the automatic bone milling process.
  • The model achieved low error rates: 1.74% for training and 3.17% for testing.
  • Statistical criteria (RMSE, R-coefficient, MAPE) confirmed the model's accuracy.

Conclusions:

  • The developed ANFIS model effectively predicts bone milling temperatures.
  • Accurate temperature prediction is vital for preventing thermal necrosis and improving surgical outcomes.
  • This approach enhances the safety and precision of robotic bone milling procedures.