Topology Optimization of Structures Using Higher Order Finite Elements in Analysis

Mukherjee, Sougata; Lu, Dongcheng; Dutta, Subhrajit; Raghavan, Balaji; Breitkopf, Piotr; Xiao, Manyu

doi:10.1007/978-981-15-9829-6_61

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whether under the rubric of space-time behavior or stimuli-responsiveness, these devices hold immense potential for decreasing the invasiveness of medical procedures and treatments. However, there are still structural issues to overcome, such as TO for multiscale structures versus high computational costs or dynamic loads versus fatigue life analysis (Mukherjee et al, 2021).…”

Section: Process Overview: Am Medical Implant Developmentmentioning

confidence: 99%

Review on structural optimization techniques for additively manufactured implantable medical devices

Peto,

García-Ávila,

Rodriguez

et al. 2024

Front. Mech. Eng.

View full text Add to dashboard Cite

Recent developments in additive manufacturing (AM) have led to significant opportunities in the design and fabrication of implantable medical devices due to the advantages that AM offers compared to conventional manufacturing, such as high customizability, the ability to fabricate highly complex shapes, good dimensional accuracy, a clean build environment, and reduced material usage. The study of structural design optimization (SDO) involves techniques such as Topology Optimization (TO), Shape Optimization (SHO), and Size Optimization (SO) that determine specific parameters to achieve the best measurable performance in a defined design space under a given set of loads and constraints. Integration of SDO techniques with AM leads to utmost benefits in designing and fabricating optimized implantable medical devices with enhanced functional performance. Research and development of various lattice structures represents a powerful method for unleashing the full potential of additive manufacturing (AM) technologies in creating medical implants with improved surface roughness, biocompatibility, and mechanical properties. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) in structural optimization has expanded opportunities to improve device performance, adaptability, and durability. The review is meticulously divided into two main sections, reflecting the predictability of the implant’s internal structure: (a) unpredictable interior topology, which explores topology-based optimization techniques, and (b) predictable inner topology, concentrating on lattice structures. The analysis of the reviewed literature highlights a common focus on addressing issues such as stress shielding, osseointegration enhancement, customization to individual needs, programmable functionalities, and weight reduction in implant designs. It emphasizes significant advances in reducing stress shielding effects, promoting osseointegration, and facilitating personalized implant creation. The review provides a detailed classification of optimization methods, with each approach scrutinized for its unique contribution to overcoming specific challenges in medical implant design, thus leading to more advanced, effective, and patient-oriented implantable devices.

show abstract

Section: Process Overview: Am Medical Implant Developmentmentioning

confidence: 99%