Parametric experimental investigation of additive manufacturing-based distal ulna bone plate: a response surface methodology-based design approach

Sharma, Shrutika; Gupta, Vishal; Mudgal, Deepa

doi:10.1108/rpj-06-2022-0205

Cited by 10 publications

(7 citation statements)

References 66 publications

(90 reference statements)

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“…The reported literature (Tao et al , 2020) has taken lower values of PS varying from 20 mm/s to 40 mm/s into consideration. For investigating the effect of higher values of PS, the range of PS has been selected from 20 mm/s to 100 mm/s, based on the preliminary study (Sharma et al , 2023). The range of values for process parameters used for printing of bone plates has been presented in Table 1.…”

Section: Materials and Methodologymentioning

confidence: 99%

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

Sharma,

Gupta,

Mudgal

et al. 2024

RPJ

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Purpose Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates. Design/methodology/approach The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). Findings Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments. Research limitations/implications The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study. Originality/value This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

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Section: Materials and Methodologymentioning

confidence: 99%

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

Sharma,

Gupta,

Mudgal

et al. 2024

RPJ

Self Cite

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“…The part file of the bone plate was converted to .stl file, which was sent to Ultimaker Cura (slicing software) for setting printing parameters. Based on the preliminary study, 48 the layer height, wall thickness, and print speed were set to 0.1 mm, 1.2 mm, and 20 mm/s, respectively. The infill density for development of each bone plate was selected in the range of 20–100%.…”

Section: Methodsmentioning

confidence: 99%

“…On the basis of preliminary study, four process parameters were selected for the present study 48 . The printing parameters such as layer height, wall thickness, and print speed were kept fixed at 0.1 mm, 1.2 mm, and 20 mm/s, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…On the basis of preliminary study, four process parameters were selected for the present study. 48 The printing parameters such as layer height, wall thickness, and print speed were kept fixed at 0.1 mm, 1.2 mm, and 20 mm/s, respectively. The infill density, submersion time, incubator shaker speed, and coating solution concentration were varied at five different levels.…”

Section: Design Of Experiments (Doe) and Selection Of Coating Parametersmentioning

confidence: 99%

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

Sharma,

Gupta,

Mudgal

et al. 2023

Polymer Engineering & Sci

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The three‐dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time‐consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO.Highlights Direct immersion coating of polydopamine on 3D printed PLA bone plates. Evaluating mechanical strength for bone plates coated at varying parameters. Statistical modeling and optimization of mechanical strength using RSM. Mechanical strength optimization and convergence properties for ML models. Experimental validation of RSM and ML‐based optimization algorithms.

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“…The materials are often organized predictably on more minor scales than the wavelengths of the phenomena they control (Chen et al , 2017; Fan et al , 2021, 2023; Morandi et al , 2016; Sun, 2017; Xie et al , 2013; Yang et al , 2017; Zhang et al , 2021). This field of study is rapidly expanding due to the development of the building blocks for metamaterials that may be manufactured mainly using 3D printing techniques (Agarwal et al , 2022; Fan et al , 2023; Goh et al , 2019; Guo and Leu, 2013; Hedayati et al , 2017; Hinton et al , 2016; Kennedy et al , 2019; Lee et al , 2007; Nagarajan et al , 2019; Saleh et al , 2017; Sharma et al , 2023; Vyavahare et al , 2023; Wang et al , 2020; Zhang et al , 2022; Zolfagharian et al , 2022). As a result, it is now feasible to practically use the fundamental design principles and subsequent properties of acoustic metamaterials.…”

Section: Introductionmentioning

confidence: 99%

Sound absorption advancements: exploring 3D printing in the development of tetrakaidecahedron cell-based acoustic metamaterials

Shah,

Singh,

Saini

et al. 2024

RPJ

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Purpose This paper aims to study the design and characterization of a 3D printed tetrakaidecahedron cell-based acoustic metamaterial. At present, the mitigation of low-frequency noise involves the utilization of spatially demanding materials for the absorption of sound. These materials lack the ability for targeted frequency control adjustments. Hence, there is a requirement for an approach that can effectively manage low-frequency noise using lightweight and durable materials. Design/methodology/approach The CAD model was created in SolidWorks and was manufactured using the Digital Light Processing (DLP) 3D printing technique. Experimental study and numerical simulations examined the metamaterial’s acoustic absorption. An impedance tube with two microphones was used to determine the absorption coefficient of the metamaterial. The simulations were run in a thermoviscous module. Findings The testing of acoustic samples highlighted the effects of geometric parameters on acoustic performance. Increment of the strut length by 0.4 mm led to a shift in response to a lower frequency by 500 Hz. Peak absorption rose from 0.461 to 0.690 as the strut diameter was increased from 0.6 to 1.0 mm. Increasing the number of cells from 8 to 20 increased the absorption coefficient and lowered the response frequency. Originality/value DLP 3D printing technique was used to successfully manufacture tetrakaidecahedron-based acoustic metamaterial samples. A novel study on the effects of geometric parameters of tetrakaidecahedron cell-based acoustic metamaterial on the acoustic absorption coefficient was conducted, which seemed to be missing in the literature.

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Parametric experimental investigation of additive manufacturing-based distal ulna bone plate: a response surface methodology-based design approach

Cited by 10 publications

References 66 publications

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

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

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

Sound absorption advancements: exploring 3D printing in the development of tetrakaidecahedron cell-based acoustic metamaterials

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