Optimization of polydopamine coating process for poly lactic acid‐based <scp>3D</scp> printed bone plates using machine learning approaches

Sharma, Shrutika; Gupta, Vishal; Mudgal, Deepa; Srivastava, Vishal

doi:10.1002/pen.26546

Cited by 6 publications

(1 citation statement)

References 71 publications

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“…In the realm of orthopedic implants, the work by Sharma et al [105] marks a significant advancement in the application of ML for optimizing the mechanical properties of 3D-printed bone plates. The study utilized ML algorithms, including genetic algorithm (GA) and differential evolution, to enhance the tensile and flexural strength of PLA bone plates coated with polydopamine (PDM).…”

Section: Machine Learning For Materials Selection or Optimizationmentioning

confidence: 99%

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Bin Abu Sofian,

Lim,

Chew

et al. 2024

ChemBioEng Reviews

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The integration of machine learning (ML) with algae‐derived biopolymers in 3D printing is a burgeoning area with the potential to revolutionize various industries. This review article delves into the challenges and advancements in this field, starting with the critical problem it addresses the need for sustainable and efficient additive manufacturing processes. Algae‐based biopolymers, such as alginate and carrageenan, are explored for their viability in 3D printing, highlighting their environmental benefits and technical challenges. The role of ML in enhancing material selection, predictive modeling, and quality control is examined, showcasing how this synergy leads to significant improvements in 3D printing processes. Key findings include the enhanced mechanical properties of algae‐based biopolymers and the optimization of printing parameters through ML algorithms. Examples like the use of Spirulina in creating a range of materials and the application of carrageenan in bone tissue engineering are discussed. The conclusion underscores the transformative impact of combining ML with algae‐based biopolymers in 3D printing, paving the way for innovative, sustainable solutions in additive manufacturing. Despite existing challenges, this integration holds promise for a future of advanced, eco‐friendly manufacturing techniques.

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Section: Machine Learning For Materials Selection or Optimizationmentioning

confidence: 99%

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Bin Abu Sofian,

Lim,

Chew

et al. 2024

ChemBioEng Reviews

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Amino‐functionalized nano‐hydroxyapatite boosts the grafting efficiency of poly (l‐lactic acid) to enhance interfacial bonding in composite bone scaffold

Yang,

Fan,

Qiu

et al. 2024

Polymer Composites

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Nano‐hydroxyapatite (nano‐HAP)/poly (l‐lactic acid) (PLLA) bone scaffold is expected to overcome the deficiencies and achieve the complementary advantages of individual constituents, but the weak interfacial bonding due to their thermodynamic incompatibility is detrimental to the mechanical properties. Herein, the PLLA chains were grafted onto nano‐HAP with 3‐aminopropyltriethoxysilane (KH550) as a coupling reagent to enhance the interfacial bonding with PLLA. Specifically, the silicon hydroxyl group produced by KH550 hydrolysis could form covalent bonding with the hydroxyl group of nano‐HAP, and the amino group of KH550 initiated the ring‐opening polymerization of l‐lactide monomers to graft PLLA chains onto nano‐HAP more effectively, leading to a higher grafting ratio of 16.7% compared with 7.2% in direct grafting without KH550 modification. Consequently, the tensile and compressive strength of the modified nano‐HAP/PLLA scaffold were improved by 40.8% and 59.5% enhancement due to the enhanced interfacial bonding in the composite scaffold, respectively, compared to the original nano‐HAP/PLLA scaffold. Additionally, the bone scaffold was conducive to cell adhesion and proliferation, making it an ideal candidate for bone defect repair.Highlights Amino‐functionalized nano‐HAP boosted the grafting efficiency of PLLA chains. Interfacial bonding between nano‐HAP and matrix was enhanced. Bone scaffold showed better mechanical properties and benign cytocompatibility.

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Integrating machine learning for predicting biomechanical properties of layered manufactured bone implants

Sharma,

Sidhu,

Bansal

et al. 2024

Polymer Engineering & Sci

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The layered manufacturing (LM) based locking bone plates (LBPs) are highly favored for biomedical applications due to its implant customization flexibility, control over porosity, and functional parameters. Design of experiments mainly relies on predefined experimental run orders; however, machine learning (ML) can handle complex and nonlinear relationships for predicting output responses. This research investigates the application of various ML models for predicting the impact strength, torque values, and punch shear strength of poly lactic acid (PLA) based LBPs. A dataset of 100 data points for each response variable was developed, analyzing the influence of printing parameters, like infill density (ID), layer height (LH), wall thickness (WT), and print speed (PS). The ID, LH, WT, and PS were varied within the ranges of 20%–100%, 0.1–0.5 mm, 0.4–1.2 mm, and 20–100 mm/s, respectively. Among the ML models evaluated, XGBoost and Adaboost exhibited strong alignment of the predicted values with actual values. The decision tree regression, showed the lowest predictive accuracy due to its tendency to overfit and lack of iterative refinement. The findings suggest that ML models can effectively predict the mechanical properties of PLA‐based LBPs, offering valuable insights for optimizing printing parameters to enhance the performance of orthopedic implants. The findings can guide biomedical engineers in making data‐driven decisions to enhance the strength of LBPs, thereby improving patient outcomes in orthopedic treatments.Highlights Predicted impact strength, torque, and shear strength of biomedical specimens. Evaluated 100 data points per variable to assess printing parameters' influence. XGBoost and Adaboost showed superior accuracy, with R2 consistently above 0.9. Decision Tree regression exhibited the lowest accuracy due to overfitting issues. Aims to guide biomedical engineers in data‐driven decisions for better patient outcomes.

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Optimization of polydopamine coating process for poly lactic acid‐based 3D printed bone plates using machine learning approaches

Cited by 6 publications

References 71 publications

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Advancing 3D Printing through Integration of Machine Learning with Algae‐Based Biopolymers

Amino‐functionalized nano‐hydroxyapatite boosts the grafting efficiency of poly (l‐lactic acid) to enhance interfacial bonding in composite bone scaffold

Integrating machine learning for predicting biomechanical properties of layered manufactured bone implants

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