Innovative hip implants should be designed in accordance with biomechanical models of the proximal femur and take into account both body weight and muscle action in order to improve usability and biomimetic performance. This article proposes a finite element analysis of the proximal femur using both cortical and trabecular regions and employing transverse isotropic properties with standardized loads taken from active and young patients. Maximum principal stresses are plotted to show the mechanical behavior of the femur and grouped to evaluate stress shielding. Tsai–Wu and the maximum principal stress fields are useful for finding the areas more prone to failure and analyzing the influence of the stems on femoral mechanics. Other parameters, such as the stem material, absence of neck and osteotomy level, are explained. This paper is expected to provide a guide for designers and surgeons of femoral stems for assessing qualitatively and quantitatively the risks of stress shielding.

Artificial intelligence‐augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state‐of‐the‐art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models to help solve problems in scalability, quality assurance, and material complexity, allowing for real‐time defect detection, digital twin integration, and closed‐loop process control. This roadmap brings together leading contributions from twenty internationally recognized research groups by uniting perspectives from materials science, computer science, robotics, and manufacturing. This work aims to create a cohesive framework for advancing AI2AM as a multidisciplinary science. The ultimate intent of this work is to establish a foundation for coordinated research and innovation in AI‐powered AM and to serve as a strategic entry point for future breakthroughs in autonomous and sustainable production.

This study presents a versatile ontology for describing all kinds of smart or stimuli-responsive 4D printed material systems and structures. The different components of the ontology, namely: initial geometry and shape, shape-morphing principle(s), triggering stimuli, intermediate/final geometry and shape, 4D material and printing or additive manufacturing technology, are enumerated and classified. Accordingly, a codification system for schematically illustrating the actuation cycle of 4D printed material systems and structures, and shape-morphing devices in general, is proposed. The systematic application of the ontology to a relevant set of examples helps to demonstrate its utility and adaptability to many different types of 4D printed objects. It demonstrates that the ontology and codification schemes developed in this research can serve a comprehensive classification tool for the emergent field of 4D printing. It is the first ontology capable of representing the multiple actuation steps of complex 4D printed devices and actuators, in which several metamorphoses may be achievable, due to combinations of different shape-morphing principles and triggering stimuli. To this end, a single line of code is required. A glossary is provided to support its implementation and application. Besides, the usability of the ontology and related codification by a generative artificial intelligence (AI) for supporting engineering design tasks is explored and validated through a set of examples and an industrial use case. This work is expected to provide a universal language to facilitate the communication in the 4D materials and printing field, as well as a synergic generative AI-based methodology for creativity promotion linked to innovative smart 4D printed material systems and structures.

The biomechanical performance of hip prostheses is often suboptimal, which leads to problems such as strain shielding, bone resorption and implant loosening, affecting the long-term viability of these implants for articular repair. Different studies have highlighted the interest of short stems for preserving bone stock and minimizing shielding, hence providing an alternative to conventional hip prostheses with long stems. Such short stems are especially valuable for younger patients, as they may require additional surgical interventions and replacements in the future, for which the preservation of bone stock is fundamental. Arguably, enhanced results may be achieved by combining the benefits of short stems with the possibilities of personalization, which are now empowered by a wise combination of medical images, computer-aided design and engineering resources and automated manufacturing tools. In this study, an innovative design methodology for custom-made short femoral stems is presented. The design process is enhanced through a novel app employing elliptical adjustment for the quasi-automated CAD modeling of personalized short femoral stems. The proposed methodology is validated by completely developing two personalized short femoral stems, which are evaluated by combining in silico studies (finite element method (FEM) simulations), for quantifying their biomechanical performance, and rapid prototyping, for evaluating implantability.