Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression

Zhao, Jun; Zhang, Youqiang; Cao, Wei; Fu, Chen

doi:10.1080/13102818.2021.1892523

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Their efforts were aimed at identifying the optimal prosthesis design to reduce the probability of dislocation. Similarly, Jun et al [ 30 ] used results produced using an FE model analysis to train a machine learning method that combines both principal component analysis (PCA) and support vector regression (SVR) in an effort to predict the contact stress of the hip prosthesis acetabular lining. The prediction model performance was then compared with the ridge regression and lasso models for validation.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems

Akkad

Mehboob

Alyamani

et al. 2023

JFB

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Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10–80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis.

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Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems

Akkad

Mehboob

Alyamani

et al. 2023

JFB

View full text Add to dashboard Cite

show abstract

“…Merali et al adapted the RF model to estimate the outcome of degenerative cervical myelopathy surgery [24]. Jun et al proposed a contact stress prediction method on total hip replacement implants using principal component analysis and SVM [33]. Kruse et al reported that the XGB algorithm is capable of predicting hip fractures [34].…”

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty

Sohail

Kim

2023

Mathematics

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Total knee arthroplasty (TKA) is a surgical technique to replace damaged knee joints with artificial implants. Recently, the imageless TKA has brought a revolutionary improvement to the accuracy of implant placement and ease of surgical process. Based on key anatomical points on the knee, the software guides the surgeon during the TKA procedure. However, the number of revision surgeries is increasing due to malalignment caused by registration error, resulting in imbalanced contact stresses that lead to failure of the TKA. Conventional stress analysis methods involve time-consuming and computationally demanding finite element analysis (FEA). In this work, a machine-learning-based approach estimates the contact pressure on the TKA implants. The machine learning regression model has been trained using FEA data. The optimal preprocessing technique was confirmed by the data without preprocessing, data divided by model size, and data divided by model size and optimal angle. Extreme gradient boosting, random forest, and extra trees regression models were trained to determine the optimal approach. The proposed method estimates the contact stress instantly within 10 percent of the maximum error. This has resulted in a significant reduction in computational costs. The efficiency and reliability of the proposed work have been validated against the published literature.

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“…Tapia et al built a surrogate model by applying Gaussian processes regression (GPR) to predict the melt pool depth of the laser powder bed fusion process [14]. Numerous methods have been applied in other studies to construct surrogate models; these methods include support vector regression (SVR) [15][16][17][18], the response surface methodology [19][20][21], kriging [22][23][24][25], and the adaptive neuro fuzzy inference system (ANFIS) [26].…”

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confidence: 99%

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

Shiu

Liu

2022

Int J Adv Manuf Technol

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In this study, the clamping stress and force involved in the centering of optical glass lens were evaluated and quantified. On the basis of the key design parameters of the examined clamps, the finite element method was applied to measure clamping stress under various parameter combinations. Support vector regression, Gaussian process regression, and adaptive neuro fuzzy inference system algorithm of surrogate models were established using the results obtained through finite element simulation. These surrogate models, which can predict clamping stress on the basis of key parameters, can reduce the time required to perform finite element analysis while providing references for optimizing clamp configuration.Keywords：Centering process, Surrogate model, Finite element analysis, Machine learning IntroductionAn optical axis is defined as the line that connects the centers of the curvature of the curved surfaces on both sides of a lens. If the optical axis deviates from the lens' geometric center axis, the imaging position of the lens will be affected and cause aberration. Centering is a key procedure in optical glass lens manufacturing because it is required to minimize the aforementioned phenomenon [1-3]. During centering, a lens is secured by a pair of bell-shaped clamps that come into contact with both of the polished surfaces of the lens. The forces between the clamps and the lens are radially balanced. Under this condition, the optical axis coincides with the geometric center line. The lens is then grounded by a grinding wheel, such that its shape becomes perfectly symmetrical with respect to the optical axis. To prevent lens clamps from scratching the polished surfaces of lens, they must be made from soft materials.Consequently, lens clamps are prone to deforming under clamping stress, which causes the central axis of a lens to become offset. With unstable clamping, a lens can be easily pushed and decentered by a grinding wheel feed. Therefore, effective methods for evaluating and analyzing clamping stress are required for the centering process.The finite element method (FEM) is widely used to solve engineering problems, including the evaluation of stress distribution [4] and simulation of mechanical behavior [5,6]. FEM-derived results can also be used to conduct pre-machining assessments [7,8] and support parameter optimization [9,10].However, to obtain accurate simulation results, precise engineering modeling and meshing are required.Simulating and analyzing a complex finite element model are usually tasks that require substantial time and computational resources [11,12]. Surrogate models are often applied as substitutes of complex computational models in engineering design; they are established by using simple computational models

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Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression

Cited by 5 publications

References 23 publications

A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems

A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems

Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty

Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

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