The fourth dimension in 4D printing refers to the ability of materials to alter its form after they are produced, thereby providing additional functional capabilities and performance driven applications. Stimuli materials provide this capability through the use of shape memory polymers. For this research, the property of programming the determined shape is achieved through controlled heat under laboratory conditions. This paper shows the potential to process and experiment with thermoplastic polyurethane as a shape memory material. Taking a step further, we ascertain the properties of this material through extrusion-based additive manufacturing processes and produce parts for testing. The results show that the characteristics of the 3D printed parts successfully retain the property of the shape memory and the recovery force allows this to be utilised as a mechanical actuator. The recovery stress has been recorded to be between 0.45 MPa and 0.61 MPa (at feed rate 990 mm/min).The maximum level of recovery stress is similar to the same material being processed through conventional compression moulding. Lastly, we designed and produced a coil as an actuator to demonstrate that the same material can be extended to other applications.

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The use of natural fibres allows reducing environmental impact, due to their natural renewable origin and the lower energy needed for their production and processing. This work presents the mechanical characterization of a newly developed technical textile, with banana fibre treated by enzymes, comparing experimental results with numerical simulation based on the definition of the unit cell at micromechanical level. The experimental test shows that the composite with the fabric of banana fibre presents worse mechanical behaviour than the one with commercial flax fibre. The presence of wool, necessary for producing the yarn, reduces the mechanical properties of the banana textile. The numerical simulation had an acceptable error compared with the experimental results, with a global average error of 9%, showing that the predictive modelling based on the multiscale method is suitable for the design process of this kind of composite.

This paper compares the mechanical properties of different natural fiber composites produced by rotational molding as a way of waste valorization from campaigns to control invasive plant species in Macaronesia. Rotomolded parts produced with polymeric matrices (polyethylene) and filled with up to 20% by weight of cellulosic fibers obtained from Arundo donax L., Pennisetum setaceum, and Ricinus communis plants were characterized in terms of tensile, flexural, and impact strength. It was found that the sieving of natural fibers allowed for their introduction in higher loadings, from 10 (for un-sieved material) to 20%; fiber size greatly affected the mechanical properties of the final parts, although some combinations were proven not to reduce the mechanical properties of the neat resin. This study is a first approach to the valorization of residues obtained from periodic campaigns of the control of invasive species performed by public authorities, usually at the local level. It is important to highlight that the main objective of this research did not focus on economically profitable activity; instead, it was focused on the reduction of wastes to be disposed from ecosystem maintenance actions and the investment of potential income into preservation policies.

4D printing is a technology that combines the capabilities of 3D printing with materials that can transform its geometry after being produced (e.g. Shape Memory Polymers). These advanced materials allow shape change by applying different stimulus such as heating. A 4D printed part will usually have 2 different shapes: a programmed shape (before the stimulus is applied), and the original shape (which is recovered once the stimulus has been applied). Lightweight parametric optimization techniques are used to find the best combination of design variables to reduce weight and lower manufacturing costs. However, current optimization techniques available in commercial 3D CAD software are not prepared for optimization of multiple shapes. The fundamental research question is how to optimize a design that will have different shapes with different boundary conditions and requirements. This paper presents a new lightweight parametric optimization method to solve this limitation. The method combines the Latin Hypercube design of experiments, Kriging metamodel and specifically designed genetic algorithms. The optimization strategy was implemented and automated using a CAD software. This method recognizes both shapes of the part as a single design and allows the lightweight parametric optimization to retain the minimum mechanical properties for both shapes.