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Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN

Published online by Cambridge University Press:  10 May 2024

Ashwani Kumar  and
Deepak Chhabra Open the ORCID record for Deepak Chhabra [Opens in a new window]
Ashwani Kumar
Affiliation:
Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak 124001, Haryana, India
Deepak Chhabra*
Affiliation:
Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak 124001, Haryana, India
*
Corresponding author: Deepak Chhabra; Email: deepak.chhabra@mdurohtak.ac.in
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Abstract

This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human wrist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end optimally using a portable SENSE 2.0 3D scanner. A central composite design (CCD) matrix was developed for three input variables; light intensity (LI = 12–20 W/m2), capture angle (CA = 10°–50°), and scanning distance (SD = 10–20 inches) for executing the experimental runs. For accuracy evaluation, the wrist perimeter on the distal end was checked using CREO Parametric software for wrist perimeter error (WPE). Various AHML tools were developed using: response surface methodology (RSM), multi-objective genetic algorithm RSM, and multi-objective genetic algorithm neural networking (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach combined with the Bayesian regularization algorithm (trainabr) provided the best mutual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to LI = 12.001 W/m2, CA = 29.428°, and SD = 18.214 inch, with a significant percentage reduction of 55.83% in WPE. Executing 3D scanning of the human wrist over the optimized process parameters predicted by AHML tools will ensure the availability of precise scans for the rapid prototyping of customized orthotic devices in a reliable manner.

Keywords

artificial intelligencerapid prototyping3D scanningprocess optimizationMOGANN
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e7
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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