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A Sensor based turning dataset for data-driven surface roughness estimation.

N R Sakthivel1, H Harigovind2, Binoy B Nair3

  • 1Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India. nr_sakthivel@cb.amrita.edu.

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|March 25, 2026
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Summary
This summary is machine-generated.

This study presents a dataset for machining Inconel-625, a challenging alloy. The data aids in developing machine learning models for accurate surface roughness estimation, improving efficiency.

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Mechanical Engineering

Background:

  • Surface roughness significantly impacts workpiece performance, especially in aerospace applications.
  • Machining difficult-to-machine alloys like Inconel-625 presents challenges in achieving desired surface finish.
  • Accurate surface roughness prediction is crucial for reducing waste and optimizing machining efficiency for high-value alloys.

Purpose of the Study:

  • To present a comprehensive dataset of machining parameters and outcomes for Inconel-625.
  • To facilitate the development of advanced machine learning models for real-time surface roughness estimation.
  • To support the scientific community in improving machining efficiency and reducing material wastage.

Main Methods:

  • Collected extensive machining data, including vibration, force, and moment, during the dry turning of Inconel-625.
  • Utilized a triaxial accelerometer and dynamometer for data acquisition, resulting in over 382 million samples.
  • Measured surface roughness using a Mitutoyo Surface Roughness Tester after each operation.

Main Results:

  • A large, publicly available dataset comprising 27 sets of machining data for Inconel-625 turning.
  • Detailed records of vibration, force, moment, and corresponding surface roughness measurements.
  • The dataset captures the complexities of machining Inconel-625, a material resistant to softening at high temperatures.

Conclusions:

  • The presented dataset is a valuable resource for researchers in machining and materials science.
  • Enables the creation and validation of machine learning and deep learning models for on-line surface roughness prediction.
  • Aims to enhance machining efficiency and reduce costs associated with difficult-to-machine alloys like Inconel-625.