Perspectives on the future of additive manufacturing

Additive manufacturing (AM) has come a long way since its first representation in the form of rapid prototyping techniques developed in the 1980s. These days, we are in the process of moving from the generation of prototypes to the creation of actual functional parts. Metal AM is particularly mature, and is seeing significant industrial utilization. Ceramic and polymer AM are not far behind. There is growing consensus as far as when AM does and does not make sense, its utilization is becoming more and more widespread, and there is a clear acceleration in the pace of research associated with AM. All of this leads to an obvious question – where are we headed? The speaker recently lead a working group tasked with formulating a response to this question. The resulting effort was both eye-opening and though-provoking. In this talk, he will review current perspectives on the future of additive manufacturing, sourced from a thorough review of the recent literature on this topic, including exchanges with key actors in the field, coupled with his own observations related to AM. The goal is to leave the audience with a deeper appreciation of the expressed needs and anticipated impacts associated with the continued development of AM technologies, with references provided for further reading

Phonon-phonon interactions due to non-linear effects in a linear ion trap

We examine in detail the theory of the intrinsic non-linearities in the dynamics of trapped ions due to the Coulomb interaction. In particular the possibility of mode-mode coupling, which can be a source of decoherence in trapped ion quantum computation, or, alternatively, can be exploited for parametric down-conversion of phonons, is discussed and conditions under which such coupling is possible are derived.Comment: 25 pages, 4 figure

On the consistency of sequentially normalized least squares

Peer reviewe

Enhancing the sustainability of high performance fiber composites

Continuous fiber reinforced composites find widespread and increasing use in all manner of structural applications, from sporting goods to aircraft to wind turbine blades. They promise an attractive mix of high stiffness and strength and excellent corrosion resistance coupled with low weight that is difficult to match with more traditional materials. However, the bulk of these materials are based on petroleum-based thermosetting resins that cannot be recycled, calling into question their sustainability. This is of particular note given the rise of wind energy as a critically important form of sustainable power generation and the heavy reliance of this industry on composites in general and continuous fiber reinforced epoxy resins in particular. It is with this in mind that our group has, for the last several years, pursued work on three fronts to address the aforementioned challenges. We have examined the structure-properties relations of a family of high-performing bio-based epoxy resins, and have demonstrated that it is possible to achieve levels of performance similar to those required in the wind energy sector1. We have studied the process rheology of these materials in the context of resin transfer molding (the preferred means of composite formation), developed a new method to quantify the amenability of an arbitrary resin to such processes, and have shown that the bio-based systems possess significant advantages as far as infusion times are concerned2. Finally, inspired by the seminal report of L. Leibler’s group in 20113, we have focused most recently on the ability to rework and recycle both epoxy resins and their composites, adding another dimension to our push for sustainability. This talk with present an overview of efforts in all three areas and provide an update on our most recent efforts. Please click Additional Files below to see the full abstract

MML Probabilistic Principal Component Analysis

Principal component analysis (PCA) is perhaps the most widely method for data dimensionality reduction. A key question in PCA decomposition of data is deciding how many factors to retain. This manuscript describes a new approach to automatically selecting the number of principal components based on the Bayesian minimum message length method of inductive inference. We also derive a new estimate of the isotropic residual variance and demonstrate, via numerical experiments, that it improves on the usual maximum likelihood approach