Predicting the compressive strength of additively manufactured PLA‐based orthopedic bone screws: A machine learning framework

Abstract

Additive manufacturing (AM) technology is an innovative technique that hasshown potential in several surgical innovations such as the fabrication of costeffective orthopedic screws. It is imperative that the process parameters used during the fabrication of bone screws determine the strength of the screw to support the load-bearing fracture sites. In this present work, the application of machine learning (ML) models was leveraged for predicting the compressive strength of additively manufactured orthopedic cortical screws. Different ML models such as k-nearest neighbors, support vector regression, decision trees, and random forest were compared to offer a robust predictive ML model. During the study, fused deposition modeling based additive manufacturing technology is used to fabricate poly(lactic acid) based cortical screws and compressive strength was monitored using the universal testing machine. The predictive ML models were developed by various independent parameters such as infill percentage, layer height, infill pattern and wall thickness. The adequacy and robustness of different ML models were observed at different error metrics. The error metrics of the random forest model were comparatively lower for testing data with a mean absolute error of 2.06 ± 0.91, root mean square error of 2.37 ± 0.9 and coefficient of determination of 0.96 ± 0.042. The results highlight that the random forest is the most adequate ML model for predicting the compressive strength of additively manufactured cortical screws.additive manufacturing, bone screw, compressive strength, machine learning | INTRODUCTIONAdditive manufacturing (AM) technology provides the ideal tailor-made solution for different orthopedic applications. [1] This technology offers various merits and functionalities such as precision, [2] ease of the process, [3] less material wastage, [4] personalization, [5] degree of freedom, [6] accuracy, [7] biocompatibility, [8] and customization [9] for the

Three point bending and tensile properties of bio‐additive polydopamine‐coated 3D printing‐based distal ulna small locking bone plates: Future need of orthopedic implants

“…Hence, the selection of varying infill density and coating parameters along with their levels has been done on the basis of preliminary study and literature review. [48][49][50] After selection of parameters, on the basis of run order in DOE (Table 2 described in Section 3), bone plates were fabricated with different infill densities. These 3D printed bone plates were immersed in coating solutions with varying coating parameters based on DOE, to examine their effect on tensile and flexural properties of coated bone plates.…”

Machine learning for forecasting the biomechanical behavior of orthopedic bone plates fabricated by fused deposition modeling

“…The hybrid model led to significant improvements in tensile strength. Agarwal et al (2022c) predicted the compressive strength of FDM-based cortical screws with varying WT, ID, LH and infill patterns. Random forest (RF) models accurately predicted compressive strength.…”

Experimental investigation on punch shear strength of poly lactic acid specimens for biomedical applications

“…8 Furthermore, they have been reported to release toxic ions, 9 exhibiting poor corrosion and wear resistance on interaction with human biological environment. 10 Fused Deposition Modeling (FDM) has witnessed advancement in the biomedical domain [11][12][13] by fabrication of patient specific implants with complex intricate geometry. 14,15 This technique assists in the development of porous structured implants that enhance cell and tissue growth upon implantation in human body.…”