Design requirements for different mechanical metamaterials, porous constructions and lattice structures, employed as tissue engineering scaffolds, lead to multi-objective optimizations, due to the complex mechanical features of the biological tissues and structures they should mimic. In some cases, the use of conventional design and simulation methods for designing such tissue engineering scaffolds cannot be applied because of geometrical complexity, manufacturing defects or large aspect ratios leading to numerical mismatches. Artificial intelligence (AI) in general, and machine learning (ML) methods in particular, are already finding applications in tissue engineering and they can prove transformative resources for supporting designers in the field of regenerative medicine. In this study, the use of 3D convolutional neural networks (3D CNNs), trained using digital tomographies obtained from the CAD models, is validated as a powerful resource for predicting the mechanical properties of innovative scaffolds. The presented AI-aided or ML-aided design strategy is believed as an innovative approach in area of tissue engineering scaffolds, and of mechanical metamaterials in general. This strategy may lead to several applications beyond the tissue engineering field, as we analyze in the discussion and future proposals sections of the research study.

Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved.

The article studies the friction coefficient in elastohydrodynamic lubrication (EHL) by means of analytically obtained equations for different contact geometries. The introduction of some simplifications allows for the simultaneous consideration of piezoviscous, pseudoplastic and thermal phenomena, resulting in complete and realistic models, which provide results in a quick and easy manner. The predictive potential of this analytical approach is analyzed by comparing the estimates of friction with full-EHL simulations and experimental data under different operating conditions. The results obtained allow us to discuss the influence of some assumptions taken into account and the scope of applicability of the models, in order to determine their usefulness and limitations.

This study introduces an AI-driven methodology for the personalized design of short femoral stems in total hip arthroplasty, addressing the challenge of stress shielding that compromises long-term implant survival. To improve pre-surgical implant suitability, a dual-input convolutional neural network (dual-CNN) was developed to predict the shielding directly from CT-like cross-sectional images that simultaneously capture femoral anatomy and stem geometry. A digitally generated dataset based on two segmented femurs and 392 stem designs was used for training and validation, while a third unseen femur assessed generalization. The influence of different dataset configurations was analyzed, with the combined dataset yielding the most accurate and robust predictions. The dual-CNN outperformed both single-anatomy models and a previously published random forest approach, reducing mean absolute error by approximately 30% and confirming the benefits of anatomically informed, image-based inputs. These findings demonstrate that the proposed model offers an efficient and scalable alternative to finite element analysis for evaluating stress/strain shielding and optimizing patient-specific short femoral stem designs.

This study investigates the application of non-planar additive manufacturing (NPAM) combined with robotic systems to enhance the production of coronary stents. Traditional additive manufacturing methods typically produce planar layers, which can limit the mechanical properties and surface quality of printed objects. In contrast, NPAM allows for the creation of continuous, curved layers, reducing anisotropic mechanical properties and minimizing stress concentrations. The methodology automates the generation of non-planar, axisymmetric layers, facilitating the fabrication of complex stent geometries that closely mimic the natural anatomy of coronary arteries. By employing advanced motion planning algorithms, robotic arms can precisely deposit materials along these curved trajectories, which may result in smoother surfaces and enhanced structural integrity. The study successfully generated trajectories for the case study and designed the necessary support structures, enabling fabrication on a cylindrical build platform giving a complete perspective of the usage of this methodology.

Desde que se acuñó el término tribología en la década de los sesenta se han logrado numerosos avances que reducen la fricción en máquinas. Aun con estos avances el estudio de la fricción en máquinas es un área activa de investigación, ya que todavía se emplea el 20% de la energía mundial en vencer esta resistencia pasiva. El microtexturizado de superficies ha resultado ser una técnica efectiva para reducir la fricción en contactos mecánicos con condiciones de trabajo que pueden dificultar la formación de una lubricación en película gruesa. En la literatura hay escasos estudios que modelen los fenómenos físicos de este tipo de contactos lubricados con superficies microtexturizadas debido a la dificultad que conlleva la influencia de numerosas variables, como las condiciones de trabajo, la geometría que define el patrón de texturas y su interrelación. Estudios recientes muestran la aplicabilidad de los algoritmos de Inteligencia Artificial (IA) para la predicción de la fricción. Por ello, en este estudio se emplean redes neuronales para determinar el coeficiente de fricción en contactos lubricados microtexturizados. A diferencia de estudios previos en los que se utilizan algoritmos de IA entrenados con datos que no tienen en consideración la incertidumbre de la microfabricación, el algoritmo propuesto se desarrolla empleando datos que se han obtenido midiendo los patrones de texturizado fabricados tanto por fotolitografía y ataque químico como mediante ablación láser. De esta forma, se comprueba que la influencia de la precisión de la fabricación es relevante en la correcta predicción del coeficiente de fricción.