Among the different families of shape memory alloys (SMA), the Fe-Mn-Si-Cr-Ni alloys have attracted a renewed interest because of its low cost, high corrosion resistance and high recovery strength during the shape memory effect, and the new technologies of additive manufacturing offer unforeseen possibilities for this family of SMA. In the present work, the reversible gamma - epsilon martensitic transformation (MT), responsible for the shape memory effect, is studied in two Fe-Mn-Si-Cr-Ni alloys with high (20.2 wt%) and low (15.8 wt%) Mn content, produced by the conventional route of casting and rolling, in comparison with the MT in another similar alloy, with intermediate Mn content (19.4 wt%), which was produced by gas atomization and additive manufacturing through laser metal deposition. The forward and reverse gamma - epsilon MT is studied by mechanical spectroscopy through the internal friction spectra and the dynamic modulus variation, together with a parallel microstructural characterization including in-situ observation of the gamma - epsilon MT during cooling and heating at the scanning electron microscope. The evolution of the transformed fraction of epsilon martensite, evaluated through the integral area of the internal friction peak, was followed along thermal cycling in all three alloys. Both, the internal friction and the electron microscopy studies show that the epsilon martensite amount increases very fast during the first few cycles, and then decreases with a tendency towards its stabilization for many tens of cycles. The results show that the gamma - epsilon MT is more stable on cycling in the additive manufactured sample than in the conventionally processed samples, opening new avenues for designing shape memory steels to be specifically processed through additive manufacturing.

Shin pads are part of the mandatory equipment footballers must wear so as to prevent lesions. Most players wear commercially available shin pads made from a variety of common materials (polypropylene or polyethylene) and high-resistance materials (glass fibre, carbon fibre or Kevlar) using traditional manufacturing techniques. Additive manufacturing was used years ago to deliver customised rigid shin pads, but they did not offer any significant advantage in terms of materials or design compared to traditional shin pads. This project analyses a novel approach to the design and manufacture of shin pads for football players that combines existing digitisation tools, lattice structures and a multi-material additive manufacturing device. A total of 24 different additive manufacturing geometries were evaluated using a customised rig where a 1-kg impactor was released from several heights (100-400 mm). The impact acceleration, the transmitted force to the leg and penetration were calculated. Results were compared against two commercially available shin pads. Results show that two of the additive manufacturing specimens tested at the highest drop height had lower impact accelerations than commercial shin pads. They had an acceleration reduction between 42% and 68% with respect to the commercial shin pads. Regarding the penetration, the improvement achieved with additive manufacturing specimens ranged from 13% to 32%, while the attenuation and the contact times were similar.

Formula Student is an international competition governed by the Society of Automotive Engineers (SAE) which challenges university students to design and build a racing car that will subsequently be compared against other cars from universities around the world on homologated racing circuits by non-professional drivers. This study focuses on the design, analysis and manufacturing process of a new oil sump for a Formula Student car - which involves combining a main ABS-plastic core created by an additive manufacturing (AM) printing process and a manual lay-up process with carbon fibre - in order to reduce the sloshing effect due to the movement of the oil during racing. The new oil sump and the original sump were modelled with computer-aided design (CAD) software and five computational fluid dynamics (CFD) simulations were performed to compare the sloshing effect in both designs in three driving scenarios: acceleration, braking and changing direction. The simulations showed that acceleration is not a critical situation since the new internal design of the sump was capable of delaying the immersion time of the oil pick-up pipe from 0.75 seconds to 2 seconds during braking and from 0.4 seconds to 0.8 seconds during lateral acceleration. The new design was physically manufactured and subsequently integrated into an internal combustion engine for testing for 45 minutes. During this test, the engine was started and put at 9600 RPM, so the oil worked under realistic temperature condi

In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method is hybrid as the prediction relies both on actually performed motions for reference (following a data-based approach) and on the definition of appropriate performance measures (following a knowledge-based approach). The prediction is carried out through the definition of a constrained non-linear optimization problem, in which the objective function is composed of a weighted combination of data-based and knowledge-based contributions. The weights of each contribution are varied in order to generate a battery of hybrid predictions, which range from purely data-based to purely knowledge-based. The results of the predictions are analyzed and compared against actually performed motions both qualitatively and quantitatively, using a measure of realism defined as the distance of the predicted motions from the mean of the actually performed motions. The method is applied to clutch pedal depression motions and the comparison between the different approaches favors the hybrid solution, which seems to combine the strengths of both data- and knowledge-based approaches, enhancing the realism of the predicted motion.

In this paper, we present a novel method to predict human motion, seeking to combine the advantages of both data-based and knowledge-based motion prediction methods. Our method relies on a database of captured motions for reference and introduces knowledge in the prediction in the form of a motion control law, which is followed while resembling the actually performed reference motion. The prediction is carried out by solving an optimization problem in which the following conditions are imposed to the motion: must fulfill the goals of the task; resemble the reference motion selected from the database; follow a knowledge-based dynamic motion control law; and ensure the dynamic equilibrium of the human model, considering its interactions with the environment. In this work, we apply the proposed method to a database of clutch pedal depression motions, and we present the results for three predictions. The method is validated by comparing the results of the prediction to motions actually performed in similar conditions. The predicted motions closely resemble the motions in the validation database and no significant differences have been noted either in the motion's kinematics or in the motion's dynamics.

In this paper, we present an optimization method for solving the nonlinear constrained optimization problem arising from a motion reconstruction problem formulated with natural coordinates. A motion reconstruction problem consists in a kinematic analysis of a rigid multibody system whose motion is usually overdetermined by an excess of data. The method has been applied to the analysis of human motion which is a typical case of an overdetermined kinematic problem as a large number of markers are usually placed on a subject to capture its movement. The efficiency of the method has been tested both with computer-simulated and real experimental data using models that include open and closed kinematic loops.