Biomedical subjects

Saikat Pal

Publications and source records attributed to Saikat Pal.

4 recordsLinked to original sources

PubMed2006

Finite element-based probabilistic analysis tool for orthopaedic applications.

Orthopaedic implants, as well as other physical systems, contain inherent variability in geometry, material properties, component alignment, and loading conditions. While complex, deterministic finite element (FE) models do not account for the potential impact of variability on performance, probabilistic studies have typically predicted behavior from simplified FE models to achieve practical solution times. The objective of this research was to develop an efficient and versatile probabilistic FE tool to quantify the effect of uncertainty in the design variables on the performance of orthopaedic components under relevant conditions. Key aspects of the computational tool developed include parametric and automated FE model creation for changes in dimensional variables, efficient solution using the advanced mean-value (AMV) reliability method, and identification of the most significant design variables. Two orthopaedic applications are presented to demonstrate the ability of the computational tool to efficiently and accurately represent component performance.

Finite Element Analysis↗

PubMed2006

Comparison of long-term numerical and experimental total knee replacement wear during simulated gait loading.

Pre-clinical experimental wear testing of total knee replacement (TKR) components is an invaluable tool for evaluating new implant designs and materials. However, wear testing can be a lengthy and expensive process, and hence parametric studies evaluating the effects of geometric, loading, or alignment perturbations may at times be cost-prohibitive. The objectives of this study were to develop an adaptive FE method capable of simulating wear of a polyethylene tibial insert and to compare predicted kinematics, weight loss due to wear, and wear depth contours to results from a force-controlled experimental knee simulator. Finite element-based computational wear predictions were performed to 5 million gait cycles using both force- and displacement-controlled inputs. The displacement-controlled inputs, by accurately matching the experimental tibiofemoral motion, provided an evaluation of the simple wear theory. The force-controlled inputs provided an evaluation of the overall numerical method by simultaneously predicting both kinematics and wear. Analysis of the predicted wear convergence behavior indicated that 10 iterations, each representing 500,000 gait cycles, were required to achieve numerical accuracy. Using a wear factor estimated from the literature, the predicted kinematics, polyethylene wear contours, and weight loss were in reasonable agreement with the experimental data, particularly for the stance phase of gait. Although further development of the simplified wear theory is important, the initial predictions are encouraging for future use in design phase implant evaluation. In contrast to the experimental testing which occurred over approximately 2 months, computational wear predictions required only 2h.

Arthroplasty, Replacement, Knee↗

PubMed2006

Effects of knee simulator loading and alignment variability on predicted implant mechanics: a probabilistic study.

Inherent variability in total knee arthroplasty loading and alignment, present in vivo and in simulator testing, may ultimately influence polyethylene tibial insert wear and long-term performance. The effect of this variability was quantified on implant kinematics and contact mechanics during simulated gait loading conditions using semi-constrained and unconstrained fixed bearing, cruciate retaining implants. A probabilistic finite element model of the Stanmore knee wear simulator was utilized to estimate the envelope of anterior-posterior (AP) and internal-external (IE) position and contact pressure and to evaluate the variability in corresponding ranges of motion (ROM). Variability levels were represented by standard deviations of up to 10% of the maximum value for load inputs and 0.25 mm and 0.5 degrees for component alignment inputs. Model predictions compared well with experimental simulator results for the semi-constrained implant, with predicted positional envelopes of up to 1.8 mm (AP) an 3.4 degrees (IE) for the semi-constrained and up to 2.6 mm (AP) and 3.7 degrees (IE) for the unconstrained implant at the variability levels evaluated. ROM varied by up to 22%, while peak contact pressure variations averaged less than 2 MPa for both designs. For each implant, loading variability was more influential during the swing phase of gait, while alignment variability affected kinematics more during stance. The relative rank of sensitivities showed differences between the two designs, providing insight into critical parameters affecting kinematics and contact characteristics.

Arthroplasty, Replacement, Knee↗

PubMed2005

Probabilistic finite element prediction of knee wear simulator mechanics.

Computational models have recently been developed to replicate experimental conditions present in the Stanmore knee wear simulator. These finite element (FE) models, which provide a virtual platform to evaluate total knee replacement (TKR) mechanics, were validated through comparisons with experimental data for a specific implant. As with any experiment, a small amount of variability is inherently present in component alignment, loading, and environmental conditions, but this variability has not been previously incorporated in the computational models. The objectives of the current research were to assess the impact of experimental variability on predicted TKR mechanics by determining the potential envelope of joint kinematics and contact mechanics present during wear simulator loading, and to evaluate the sensitivity of the joint mechanics to the experimental parameters. In this study, 8 component alignment and 4 experimental parameters were represented as distributions and used with probabilistic methods to assess the response of the system, including interaction effects. The probabilistic FE model evaluated two levels of parameter variability (with standard deviations of component alignment parameters up to 0.5mm and 1 degrees ) and predicted a variability of up to 226% (3.44mm) in resulting anterior-posterior (AP) translation, up to 169% (4.30 degrees ) in internal-external (IE) rotation, but less than 10% (1.66MPa) in peak contact pressure. The critical alignment parameters were the tilt of the tibial insert and the IE rotational alignment of the femoral component. The observed variability in kinematics and, to a lesser extent, contact pressure, has the potential to impact wear observed experimentally.

Arthroplasty, Replacement, Knee↗