Friction, wear, and the consequent energy dissipation pose significant challenges in systems with moving components, spanning various domains, including nanoelectromechanical systems (NEMS/MEMS) and bio-MEMS (microrobots), hip prostheses (biomaterials), offshore wind and hydro turbines, space vehicles, solar mirrors for photovoltaics, triboelectric generators, etc. Nature-inspired bionic surfaces offer valuable examples of effective texturing strategies, encompassing various geometric and topological approaches tailored to mitigate frictional effects and related functionalities in various scenarios. By employing biomimetic surface modifications, for example, roughness tailoring, multifunctionality of the system can be generated to efficiently reduce friction and wear, enhance load-bearing capacity, improve self-adaptiveness in different environments, improve chemical interactions, facilitate biological interactions, etc. However, the full potential of bioinspired texturing remains untapped due to the limited mechanistic understanding of functional aspects in tribological/biotribological settings. The current review extends to surface engineering and provides a comprehensive and critical assessment of bioinspired texturing that exhibits sustainable synergy between tribology and biology. The successful evolving examples from nature for surface/tribological solutions that can efficiently solve complex tribological problems in both dry and lubricated contact situations are comprehensively discussed. The review encompasses four major wear conditions: sliding, solid-particle erosion, machining or cutting, and impact (energy absorbing). Furthermore, it explores how topographies and their design parameters can provide tailored responses (multifunctionality) under specified tribological conditions. Additionally, an interdisciplinary perspective on the future potential of bioinspired materials and structures with enhanced wear resistance is presented.

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine.

A Machine Learning (ML)-based surrogate modeling framework is presented for mapping structure–property relationships in architected Ti6Al4V cylindrical TPMS metamaterials subjected to quasi-static compression. A Python–nTop pipeline automatically generated 3456 cylindrical shell lattices (Gyroid, Diamond, Split-P), and ABAQUS/Explicit simulations with a Johnson–Cook failure model for Ti6Al4V quantified their mechanical response. From 3024 valid designs, key mechanical properties targets including elastic modulus (E), yield stress (Y), ultimate strength (U), plateau stress (PL), and energy absorption (EA) were extracted alongside geometric descriptors such as surface area (SA), surface-area-to-volume ratio (SA/VR), and relative density (RD). A multi-output surrogate model (feedforward neural network) trained on the simulated set accurately predicts these properties directly from seven design parameters (thickness; unit cell counts in X, Y, and Z directions; unit cell orientation; height; diameter), enabling rapid property estimation across large design spaces. Topology-dependent trends indicate that Split-P exhibits the highest strength, energy absorption, and total SA, and shows the largest variation in SA/VR; Gyroid exhibits the lowest SA with a moderate SA/VR; and Diamond is the most compliant lattice and maintains a higher SA/VR than Gyroid despite lower SA. RD increases with both SA and SA/VR across all topologies. The framework provides a reusable computational tool for architectured lattices, enabling quick prescreening of implant designs without repeated finite-element analyses.

Patient-specific lattice implants (PSLIs) and modular porous scaffolds have emerged as promising solutions for treating diaphyseal segmental defects of the femur and tibia, particularly where conventional reconstruction methods fall short. This second part of our two-part review focuses on how current studies transform computed tomography (CT) and $\mathsf{\mu }$CT datasets into architected lattice implants, as well as how these constructs are fabricated and numerically, mechanically, biologically, and clinically verified. We outline imaging pipelines, including Digital Imaging and Communications in Medicine (DICOM) acquisition, segmentation, contralateral mirroring, and Hounsfield Units (HU)–density–elasticity mapping, and show how these choices impact finite element (FE) models and print-ready geometries. Next, lattice design strategies and mixed-material concepts are compared and linked to specific additive manufacturing routes in metals, polymers, and bioceramics, such as laser powder bed fusion (LPBF), electron beam melting (EBM), fused deposition modeling (FDM), material jetting, and extrusion-based bioprinting. Methodological overviews of linear–elastic models and homogenized finite element (FE) models, along with bench-top mechanical tests, in vitro cell assays, in vivo animal studies, and early clinical series, are utilized to categorize the studies into four pathways: simulation (S), mechanical (E_mech), biological (E_bio), and validation (V). Based on the reviewed literature, we establish a general workflow for CT implants. We identify common gaps in the process, observe insufficient reporting of imaging and modeling details, note a lack of data on fatigue and remodeling, and recognize the limited size of clinical cohorts. Additionally, we provide practical recommendations for developing more standardized and scalable planning pipelines. Part 1 of this two-part review studied defect patterns, anatomical location, and fixation strategies for patient-specific lattice implants used in femoral and tibial segmental reconstruction, with emphasis on how defect morphology and subregional anatomy influence construct selection and mechanical behavior. It established a defect- and fixation-centered review that provides the clinical and anatomical context for the workflow and validation analysis presented in Part 2.