Jonathan Brigham Hopkins

Mechanic neural network design aperiodic metamaterials by first learning the desired behaviors via tuning the stiffness of their active beams. Passive beam design with those same stiffness are accordingly organized within the final design.

Digital mechanical signals can propagate reversibly enabling material computation. Simulations and experiments characterize the signal dynamics and energetic landscape.

Objective: Total knee arthroplasty (TKA) is a common and highly successful treatment for knee osteoarthritis. Despite its success, some TKA implants still do not last the remaining lifetime of the patient, due in large part to aseptic loosening of the bone-implant interface, most commonly involving the tibial component. In this manuscript, we present a compliant tibial stem with the potential to increase the lifespan of TKA by accommodating rotation of the tibial tray about the tibia’s long axis without introducing an additional high-cycle-count wear surface. Our objective was to refine the design of this implant to support the loads and displacements associated with common activities of daily living (ADLs), and to validate performance of a physical prototype on the benchtop. Methods: We used finite element analysis to sweep a representative parameter space of reasonably-sized caged hinges, and then to refine the mechanism geometry in the context of in vivo knee joint loads. We fabricated a prototype of the refined mechanism, and evaluated performance of that physical prototype under ADL loads and displacements. Results: The refined mechanism supports walking loads and displacements with a safety factor of 1.47 on the target fatigue stress limit. The maximum reaction moment in the prototype was 1.22 Nm during emulated walking, which represents a reduction of approximately 80% from the in vivo reaction moment within a conventional TKA implant rotating to the same angle. Discussion/Conclusion: Our results demonstrate feasibility of a compliant tibial stem with the potential to decrease failure rates and increase longevity of TKA implants.

Cross-axis flexural pivots (x-pivots) hold promise as frictionless bearings for applications that require moderate angular displacement under large uniaxial tensile loads. Stress analysis through three-dimensional finite element modeling provides a framework for designing x-pivots to withstand expected loads and deformations for a large number of cycles. This type of analysis is especially important in protecting the mechanism against nonlinear stress effects induced by combined bending and tensile loading, which can prove catastrophic in certain loading regimes. Building on our prior research, we focus in this article on new techniques for mitigating the two most impactful nonlinear stress effects: blade buckling and bowstringing. Specifically, we highlight the ways in which design modifications—to local geometry of the variable-thickness blades, geometry of the complete x-pivot mechanism, and the relationship between the x-pivot and the absolute global system—can be leveraged to tailor mechanism performance to the loads and deformations required of a given application. We use finite element analysis to evaluate the effects of these design modifications in the context of different planar and six degrees-of-freedom loading regimes. Our results demonstrate that informed design changes can significantly reduce peak stresses over a fixed loading sequence, which is an important step toward practical implementation of x-pivots in real-world systems.

Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattresses. Such mattresses typically aim to minimize the pressures imparted while alternating between different states of pressure to prevent areas of tissue from being persistently occluded of blood flow. In this work, we built an actuator bed to study AP approaches and learned that AP mattresses should aim to increase—not decrease—peak pressures to a certain extent if such areas are to be minimized for effectively preventing PUs. In addition, we learned that such mattresses should aim to increase the difference between their loading and off-loading pressures. We identified optimal parameters from the study and used them to design an AP mattress made of compliant mechanisms that markedly reduce areas of persistent occlusion by exhibiting relatively high peak pressures that are periodically alternated with substantially lower off-loading pressures. The mattress’s performance was characterized and compared against a standard foam pad in its flat and raised configurations. The load required to actuate the mattress from one of its stable states of pressure to the other was also measured.

This work provides an overview of the progress made in the emerging field of mechanical neural networks (MNNs). Inspired by the mathematical layout of artificial neural networks (ANNs), MNNs consist of a physical network of tunable beams that learn the desired behaviors and properties of their interconnected lattice. As such they constitute artificial-intelligent (AI) mechanical metamaterials, that is, architected materials, that can autonomously acquire new abilities with increased exposure to ambient loading conditions. These lattices have been demonstrated with a variety of tunable beam designs on the macro scale and are currently being fabricated to demonstrate their learning capabilities on the micro scale. This paper reviews the specific advances that have been published in this area to date.

This is a roadmap article with multiple contributors on different aspects of embodying intelligence and computing in the mechanical domain of functional materials and structures. Overall, an IOP roadmap article is a broad, multi-author review with leaders in the field discussing the latest developments, commissioned by the editorial board. The intention here is to cover various topics of adaptive structural and material systems with mechano-intelligence in the overall roadmap, with twelve sections in total. These sections cover topics from materials to devices to systems, such as computational metamaterials, neuromorphic materials, mechanical and material logic, mechanical memory, soft matter computing, physical reservoir computing, wave-based computing, morphological computing, mechanical neural networks, plant-inspired intelligence, pneumatic logic circuits, intelligent robotics, and embodying mechano-intelligence for engineering functionalities via physical computing. In this paper, we view all the sections with equal contributions to the overall roadmap article and thus list the authorship on the front page via alphabetical order of their last names. On the other hand, for each individual section, the authors decide on their own the order of authorship. (Abstract written by Guest Editors Kon-Well Wang (aka K W Wang) and Suyi Li.)