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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

Information

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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References

Amir, YM and Thörnberg, B (2017) High precision laser scanning of metallic surfaces. International Journal of Optics 2017, 4134205. https://doi.org/10.1155/2017/4134205CrossRefGoogle Scholar
Badhwar, P, Kumar, A, Yadav, A, Kumar, P, Siwach, R, Chhabra, D and Dubey, KK (2020) Improved pullulan production and process optimization using novel GA–ANN and GA–ANFIS hybrid statistical tools. Biomolecules 10(1), 124. https://doi.org/10.3390/biom10010124CrossRefGoogle ScholarPubMed
Biswas, HK (2019) Automatic planning for scanning: Optimizing 3D laser scanning operations using BIM and TLS. IAENG International Journal of Computer Science 46(3), 1–9.Google Scholar
Boehnen, C and Flynn, P (2005) Accuracy of 3D scanning technologies in a face scanning scenario. In Stephanie, K (ed.), Proceedings of International Conference on 3-D Digital Imaging and Modeling, 3DIM, pp. 310–317. Ottawa, ON, Canada: IEEE.Google Scholar
Chakrabarti, A, Suwas, S and Arora, M (2022) Industry 4.0 and Advanced Manufacturing: Proceedings of I-4AM 2022. Singapore: Springer Nature.Google Scholar
Deshwal, S, Kumar, A and Chhabra, D (2020) Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement. CIRP Journal of Manufacturing Science and Technology 31, 189–199.10.1016/j.cirpj.2020.05.009CrossRefGoogle Scholar
Dhankhar, R, Kumar, A, Kumar, S, Chhabra, D, Shukla, P and Gulati, P (2019) Multilevel algorithms and evolutionary hybrid tools for enhanced production of arginine deiminase from pseudomonas furukawaii RS3. Bioresource Technology 290, 121789. https://doi.org/10.1016/j.biortech.2019.121789CrossRefGoogle ScholarPubMed
Edgar, D, Day, R, Briffa, NK, Cole, J and Wood, F (2008) Volume measurement using the Polhemus FastSCAN 3D laser scanning: A novel application for burns clinical research. Journal of Burn Care and Research 29(6), 994–1000.CrossRefGoogle ScholarPubMed
Farhan, M, Wang, JZ, Bray, P, Burns, J and Cheng, TL (2021) Comparison of 3D scanning versus traditional methods of capturing foot and ankle morphology for the fabrication of orthoses: A systematic review. Journal of Foot and Ankle Research 14(1), 1–11.CrossRefGoogle ScholarPubMed
Fitzpatrick, AP, Collins, PK and Gibson, I (2020) 3D scan process optimisation study for rapid virtualization. Procedia CIRP 91, 911–916.CrossRefGoogle Scholar
Gibson, I, Rosen, D and Stucker, B (2015) Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, 2nd ed. New York, NY: Springer. https://doi.org/10.1007/978-1-4939-2113-3CrossRefGoogle Scholar
Haleem, A and Javaid, M (2019) 3D scanning applications in medical field: A literature-based review. Clinical Epidemiology and Global Health 7(2), 199–210.10.1016/j.cegh.2018.05.006CrossRefGoogle Scholar
Henson, K, Constantino, P, O’Keefe, FR and Popovich, G (2019) Three-dimensional scanning and printing techniques to analyze and archive human skeletal remains. Library Hi Tech 37(3), 389–400.10.1108/LHT-10-2017-0206CrossRefGoogle Scholar
Kaasalainen, S, Jaakkola, A, Kaasalainen, M, Krooks, A and Kukko, A (2011) Analysis of incidence angle and distance effects on terrestrial laser scanner intensity: Search for correction methods. Remote Sensing 3(10), 2207–2221.10.3390/rs3102207CrossRefGoogle Scholar
Kaushik, A, Gahletia, S, Garg, RK, Sharma, P, Chhabra, D and Yadav, M (2022) Advanced 3D body scanning techniques and its clinical applications. In Yelve NP, Dhingra S and Sahdev RK (eds.), 2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). Bangkok, Thailand: IEEE, pp. 352–358.10.1109/ICCMSO58359.2022.00074CrossRefGoogle Scholar
Khangwal, I, Chhabra, D and Shukla, P (2021) Multi-objective optimization through machine learning modeling for production of Xylooligosaccharides from alkali-pretreated corn-cob Xylan via enzymatic hydrolysis. Indian Journal of Microbiology 61(4), 458–466.10.1007/s12088-021-00970-2CrossRefGoogle ScholarPubMed
Khorasani, M, Ghasemi, AH, Rolfe, B and Gibson, I (2022) Additive manufacturing a powerful tool for the aerospace industry. Rapid Prototyping Journal 28(1), 87–100.10.1108/RPJ-01-2021-0009CrossRefGoogle Scholar
Kumar, A and Chhabra, D (2022) Adopting additive manufacturing as a cleaner fabrication framework for topologically optimized orthotic devices: Implications over sustainable rehabilitation. Cleaner Engineering and Technology 10, 100559.10.1016/j.clet.2022.100559CrossRefGoogle Scholar
Kumar, P, Gupta, P and Singh, I (2022) Parametric optimization of FDM using the ANN-based whale optimization algorithm. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 36, e27.10.1017/S0890060422000142CrossRefGoogle Scholar
Kumar, V, Kumar, A, Chhabra, D and Shukla, P (2019) Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools. Bioresource Technology 271, 274–282. https://doi.org/10.1016/j.biortech.2018.09.115CrossRefGoogle ScholarPubMed
Lemeš, S and Zaimović-Uzunović, N (2009) Study of ambient light influence on laser 3D scanning. In Slabe JM, Grum J and Kuzman K (eds), 7th International Conference on Industrial Tools and Material Processing Technologies (ICIT & MPT). Ljubljana, Slovenija: University of Ljubljana, pp. 327–330.Google Scholar
Nayak, S and Das, RK (2020) Application of artificial intelligence (AI) in prosthetic and orthotic rehabilitation. In Sezer, V, Öncü, S and Boyraz Baykas, P (eds.), Service Robotics, pp. 167–169. London, UK: IntechOpen.Google Scholar
Pesci, A, Teza, G and Bonali, E (2011) Terrestrial laser scanner resolution: Numerical simulations and experiments on spatial sampling optimization. Remote Sensing 3(1), 167–184.10.3390/rs3010167CrossRefGoogle Scholar
Popescu, D, Zapciu, A, Tarba, C and Laptoiu, D (2020) Fast production of customized three-dimensional-printed hand splints. Rapid Prototyping Journal 26(1), 134–144.10.1108/RPJ-01-2019-0009CrossRefGoogle Scholar
Pourmostaghimi, V, Zadshakoyan, M, Khalilpourazary, S and Badamchizadeh, MA (2022) A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 36, e28.CrossRefGoogle Scholar
Seminati, E, Talamas, DC, Young, M, Twiste, M, Dhokia, V and Bilzon, JLJ (2017) Validity and reliability of a novel 3D scanner for assessment of the shape and volume of amputees’ residual limb models. PLoS One 12(9), 1–16.CrossRefGoogle ScholarPubMed
Sholts, SB, Wärmländer, SKTS, Flores, LM, Miller, KWP and Walker, PL (2010) Variation in the measurement of cranial volume and surface area using 3D laser scanning technology. Journal of Forensic Sciences 55(4), 871–876.CrossRefGoogle ScholarPubMed
Telfer, S and Woodburn, J (2010) The use of 3D surface scanning for the measurement and assessment of the human foot. Journal of Foot and Ankle Research 3(1), 1–9.CrossRefGoogle ScholarPubMed
Tikuisis, P, Meunier, P and Jubenville, CE (2001) Human body surface area: Measurement and prediction using three dimensional body scans. European Journal of Applied Physiology 85(3–4), 264–271.CrossRefGoogle ScholarPubMed
Toma, AM, Zhurov, A, Playle, R, Ong, E and Richmond, S (2009) Reproducibility of facial soft tissue landmarks on 3D laser-scanned facial images. Orthodontics and Craniofacial Research 12(1), 33–42.CrossRefGoogle ScholarPubMed
Trebuňa, P, Mizerák, M, Trojan, J and Rosocha, L (2020) 3D scanning as a modern technology for creating 3D models. Acta Tecnología 6(1), 21–24.CrossRefGoogle Scholar
Voronov, VI and Dovgolevskiy, PA (2020) Designing a subsystem for creating a three-dimensional model of an orthopedic insole based on data from a laser 3D scanning of the patient’s feet. In Dymkova S (ed), 2020 International Conference on Engineering Management of Communication and Technology (EMCTECH). Vienna, Austria: IEEE, pp. 1–6.CrossRefGoogle Scholar
Wang, CCL, Chang, TKK and Yuen, MMF (2003) From laser-scanned data to feature human model: A system based on fuzzy logic concept. CAD Computer Aided Design 35(3), 241–253.CrossRefGoogle Scholar
Zaimovic-Uzunovic, N and Lemes, S (2010) Influences of surface parameters on laser 3D scanning. In Aketagawa M (ed), 10th International Symposium on Measurement and Quality Control 2010, ISMQC 2010. Osaka, Japan: International Measurement Confederation (IMEKO), pp. 408–411.Google Scholar