Regression models to predict the behavior of the coefficient of friction of AISI 316L on UHMWPE under ISO 14243-3 conditions

Garcia, Adrian Luis Garcia; Alvaréz-Vera, M.; Montoya-Santiyanes, L A; Domínguez-López, I.; Montes-Seguedo, J.L.; Sosa, Julio C.; Barceinas-Sánchez, J.D.O.

doi:10.1016/j.jmbbm.2018.03.028

Cited by 14 publications

(8 citation statements)

References 33 publications

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“…Fisher and Dowson reported that the friction coefficient in artificial human joints is in the range of 0.03-0.10 [33]. However, other studies obtained higher ranges of the friction coefficient for artificial human joints, for example, 0.05-0.33 [27], 0.06-0.25 [26], and 0.06-0.17 [28,29]. Focusing on the dynamic modeling of total knee arthroplasty (TKA), some investigations have aimed at developing anatomy-based dynamic models to consider the kinetic and kinematic behavior of TKAs like quasi-static models [34][35][36][37][38][39][40][41], 2D dynamic approaches [42][43][44][45][46][47][48], and sophisticated 3D mathematical dynamic solutions [49][50][51].…”

Section: Introductionmentioning

confidence: 98%

“…Subsequently, they proposed a regression model to represent experimental data mathematically [28]. In 2018, Garcia-Garcia et al, continued their research work while conducting a series of experiments to develop prediction models of the friction coefficient between the metallic ball and UHMWPE disk lubricated with diluted bovine serum [29]. Regression parameters for a suggested model that fits well in experimental data were determined for the stance and swing phases separately.…”

Section: Introductionmentioning

confidence: 99%

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On the Effect of Friction on Tibiofemoral Joint Kinematics

Askari

Andersen

2021

Applied Sciences

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The effect of friction on nonlinear dynamics and vibration of total knee arthroplasties is yet to be investigated and understood. This research work aims at studying the influence of friction on nonlinear dynamics, friction-induced vibration, and damage of tibiofemoral joints. For this purpose, a spatial dynamic knee model is developed using an asymmetric nonlinear elastic model accounting for knee joint ligaments and a penalty contact model to compute normal contact stresses in the joint while contact detection is treated such that the associated computational time is reduced. Several friction models are considered and embedded in the dynamic model to estimate tangential friction forces in the knee joint. External loads and moments, due to the presence of all soft tissues, e.g., muscles and hip-joint reaction forces, applied to the femoral bone are determined using a musculoskeletal approach. In the post-processing stage, damage, i.e., wear and creep, are estimated using three wear models and an empirical creep formulation, respectively. In addition, a FFT analysis is performed to evaluate likely friction-induced vibration of tibiofemoral joints. Mesh density analysis is performed and the methodology is assessed against outcomes available in the literature. It can be concluded that friction influences not only the tribology, but also dynamics of the knee joint, and friction-induced vibration is likely to take place when the friction coefficient increases.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

On the Effect of Friction on Tibiofemoral Joint Kinematics

Askari

Andersen

2021

Applied Sciences

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“…Multivariate regression analysis has also found many applications in the tribology of materials [ 29 ]. The significant parameters making a major contribution to the coefficient of friction of titanium carbide (TiC) reinforced metal matrix composites were identified using Analysis of variance (ANOVA) [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

2021

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This paper presents the application of multi-layer artificial neural networks (ANNs) and backward elimination regression for the prediction of values of the coefficient of friction (COF) of Ti-6Al-4V titanium alloy sheets. The results of the strip drawing test were used as data for the training networks. The strip drawing test was carried out under conditions of variable load and variable friction. Selected types of synthetic oils and environmentally friendly bio-degradable lubricants were used in the tests. ANN models were conducted for different network architectures and training methods: the quasi-Newton, Levenberg-Marquardt and back propagation. The values of root mean square (RMS) error and determination coefficient were adopted as evaluation criteria for ANNs. The minimum value of the RMS error for the training set (RMS = 0.0982) and the validation set (RMS = 0.1493) with the highest value of correlation coefficient (R2 = 0.91) was observed for a multi-layer network with eight neurons in the hidden layer trained using the quasi-Newton algorithm. As a result of the non-linear relationship between clamping and friction force, the value of the COF decreased with increasing load. The regression model F-value of 22.13 implies that the model with R2 = 0.6975 is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.

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“…Metallic biomaterials are extensively used in bio-applications such as pharmaceutical and textile industries and surgical implants. AISI 316L stainless steel is widely employed in aerospace, food, and chemical, as well as biomaterial industries, due to its strong corrosion resistance in aggressive environments and its exceptional biocompatibility [1][2][3]. Compared to other metals and alloys, the biomaterial of 316L austenitic stainless steel is one of the most commonly used materials for fracture fixation devices due to its distinctive mechanical properties and its low cost [4,5].…”

Section: Introductionmentioning

confidence: 99%

Influences of Shot Peening Parameters on Mechanical Properties and Fatigue Behavior of 316 L Steel: Experimental, Taguchi Method and Response Surface Methodology

2021

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Severe plastic deformation methods like shot peening (SP) are known as efficient surface treatments and grain refining processes which afford more effective properties in metallic materials. In the current research, a comprehensive study was carried out on SP of AISI 316 L steel. It included 42 different SP treatments with a wide range of Almen intensities of 12–27 A and various coverage degrees (100%–1500%). Several experimental tests were conducted in order to explore the microstructure, grain size, surface topography, hardness, wettability, and residual stresses of the specimens. Next, two different approaches including Taguchi method (TM), and response surface methodology (RSM) were deployed for modeling, analysis, and optimization. RSM and TM were used to examine the influence of the effective parameters. Based on the optimized results, the fatigue behavior of the selected treatments was investigated experimentally in both smooth and notched specimens. Graphical abstract

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Regression models to predict the behavior of the coefficient of friction of AISI 316L on UHMWPE under ISO 14243-3 conditions

Cited by 14 publications

References 33 publications

On the Effect of Friction on Tibiofemoral Joint Kinematics

On the Effect of Friction on Tibiofemoral Joint Kinematics

Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

Influences of Shot Peening Parameters on Mechanical Properties and Fatigue Behavior of 316 L Steel: Experimental, Taguchi Method and Response Surface Methodology

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