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Optimal machine learning for detecting lathe machining parameters

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Abstract

Remote manufacturing process monitoring is becoming more popular in industry for many reasons including increased production, reduced production costs, and better product quality. However, a downside to remote monitoring is that there is minimal human interaction with the machine to ensure optimal performance. Many types of sensors have been used to monitor the manufacturing process parameters of a machine, but these types of sensors are typically invasive to the machine, costly to install, and can interfere with the machining operation. A solution to this problem is to use a non-invasive sensor such as a microphone array to remotely detect manufacturing process parameters. Acoustic signals produced by a lathe are used to monitor spindle speed, depth of cut, and feed rate to ensure optimal performance. Based on these acoustic signals, machine learning algorithms are used to predict manufacturing process parameters. Two common learner algorithms were discussed and compared, such as k-nearest neighbor (kNN) and Gaussian support vector machine (Gaussian SVM). Each algorithm was classified as either “fine” or “medium.” Various pre-processing algorithms were also discussed and compared, such as autocorrelation and/or cross-correlation in both time domain and frequency domain. This study found that autocorrelation in frequency domain yielded the best overall results for all three machining parameters, with learner algorithms of fine kNN or fine Gaussian SVM for depth of cut, fine Gaussian SVM for feed rate, and either fine kNN or fine Gaussian SVM for spindle speed. For prediction of machining parameters, an accuracy range of 90–100% was obtained.

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Authors and Affiliations

  1. Electrical and Computer Engineering Technology Department, The Pennsylvania State University, Erie, PA, USA

    Keven Rall & David Loker

  2. Mechanical Engineering Department, The Pennsylvania State University, Erie, PA, USA

    Chetan P. Nikhare

Authors
  1. Keven Rall
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  2. David Loker
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  3. Chetan P. Nikhare
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Correspondence to Chetan P. Nikhare.

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Future work

The machine learning methodologies developed in this work can be expanded and utilized to monitor and predict manufacturing process parameters in multiple machines running simultaneously. With acoustic signals from individual microphones, data can be processed to achieve selective directional sensitivity for localization of sound sources.

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Rall, K., Loker, D. & Nikhare, C.P. Optimal machine learning for detecting lathe machining parameters. Int J Adv Manuf Technol 128, 779–788 (2023). https://doi.org/10.1007/s00170-023-11939-4

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  • DOI: https://doi.org/10.1007/s00170-023-11939-4

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