Response surface methodology and machine learning based tensile strength prediction in ultrasonic assisted coating of poly lactic acid bone plates manufactured using fused deposition modeling.

Autor: Sharma S; Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India., Gupta V; Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India. Electronic address: vishal.gupta@thapar.edu., Mudgal D; Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India.
Jazyk: angličtina
Zdroj: Ultrasonics [Ultrasonics] 2024 Feb; Vol. 137, pp. 107204. Date of Electronic Publication: 2023 Nov 13.
DOI: 10.1016/j.ultras.2023.107204
Abstrakt: Poly Lactic Acid (PLA) based bone plates fabricated using Fused Deposition Modeling have poor mechanical strength which can be improved by biocompatible polydopamine (PDM) coating. However, PDM particles, being heavy in nature, settle at the container bottom with increase in coating solution concentration at the time of bone plate coating using dip coating technique. Thus, the present work aims to witness the effect of ultrasonic assisted coating parameters on tensile strength of coated bone plates. The coating parameters involving power of ultrasonic vibrations, coating solution concentration and immersion time were varied. The standard Response Surface Methodology (RSM) was applied and experimental trials were performed for obtaining tensile strength of bone plates under varied coating parameters. The objective of the present study was to compare the values of tensile strength predicted using RSM and machine learning (ML) models. Based on the obtained experimental values, gradient boosting regression (GBReg), linear regression (LReg) and random forest regression (RFReg) were trained and tested for predicting tensile strength of bone plates. The accuracy and prediction errors corresponding to RSM and ML based models were compared with respect to R 2 , Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings revealed that GBReg exhibited R 2 , MSE, RMSE and MAE values as 0.9312, 1.7142, 1.2877 and 1.0861 respectively, while RSM showed R 2 , MSE, RMSE and MAE values as 0.882, 2.13, 1.4595 and 1.258 respectively. RSM model has shown minimum accuracy with high prediction errors amongst the four models. GBReg has outperformed other ML models in terms of their accuracy and error metrics. The present study therefore suggests the application of GBReg based ML model for predicting tensile strength of PDM coated bone plates in response to its accurate and robust prediction performance.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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