Session: 02-14-01: 3D/4D BioManufacturing, BioMaterials, & Computational Modeling

Paper Number: 100252

100252 - Numerical Study of Shape Distortions and Residual Stresses in 3d Printed Components for Knee Replacements 

Numerical simulation has become an essential feature of the current industry 4.0. These advancements are continuously being made to improve the efficiency of manufacturing operations. One transformational solution is an additive manufacturing (AM), which uses CAD design data to deposit materials layer by layer to construct a physical 3D object.

Depending on the use of the design, the printing substance can be selected from various options such as polymers, ceramics, and metals. This method can be more customizable than the conventional operations of removing or forming materials. Along with increasing the efficiency of manufacturing a part, it also contributes to creating a part that is lighter in weight. Therefore, additive manufacturing has become an attractive option for creating implants because of the customizable aspects, allowing for a patient-specific prosthesis. Currently, most implants are prefabricated to different sizes, so when used on the patient, it is up to the surgeon's discretion on how much to alter to achieve an optimal fit. Using additive manufacturing and finite element analysis in the process would allow for an implant that is created to fit the patient precisely. Hence, minor damage is done to the bones and less approximation during the operation. In this study, a simulations framework was introduced: where we started by obtaining the data from computerized tomography (CT) scan, converting the DICOM files into 3D Slicer, meshing the created 3D object, and ended by performing the simulation model using Abaqus to predict the distortions and residual stresses that arise from additive manufacturing, along with the resultant stresses that occur when under loading, which was then used to evaluate the performance of the knee-joint under typical conditions. To verify the simulation framework's precision and accuracy, we collected the knee structure and data from an open-access source provided by Denver University. Denver University has CT data detail for five patients, ranging from 37 – 72 years old to 54 – 127 kg. 

Evaluating the actual knee will provide similar results to what a knee replacement would experience for simulation purposes. The patient chosen to analyze in this study was a 50-year-old male, 175 cm in height and 127 kg in weight. The DICOM files were retrieved from Denver University, which is also known as the Center for Orthopedic Biomechanics. Finally, parametric studies will be conducted to show a viable option for the 3D printed implant and how the design may be improved along with additive manufacturing process planning for optimal performance.

Presenting Author: Osama Aljarrah Youngstown State University

Presenting Author Biography: Osama Aljarrah is an assistant professor of industrial and systems engineering at Youngstown State University in Youngstown, Ohio, USA. He obtained his undergraduate degree in industrial engineering/manufacturing engineering (2006) from Jordan University of science and technology and his master's degree in business administration (2013) from Yarmouk University. Aljarrah earned his doctoral degree in engineering and applied science and industrial and systems engineering from the University of Massachusetts at Dartmouth in May 2021. After a brief time serving in different industries, Aljarrah was appointed as an assistant professor at Rayen school of engineering. Current research emphasizes the use of surrogate modeling, evolutionary computation, simulation, and informed physics machine learning techniques to determine the effect of process parameters variability on the additively manufactured part mechanical properties. Assess the impact of selecting different input arguments on the felid of output instead of a single of multiple outputs using machine and deep learning techniques. Evaluate the degree to which compensation strategy efforts successfully improve the quality of additively manufactured parts using surrogate modeling.

Authors:

Osama Aljarrah Youngstown State University
Stephanie Decarvalho University of Massachusetts Dartmouth
Jun Li University of Massachusetts Dartmouth