Three-dimensional (3D) printable lead-free piezocomposites offer scalable and environmentally friendly solutions in many engineering applications. Typically, the composite system consists of polymeric matrices reinforced with active polycrystalline particles, and possibly nanoadditives. The presence of interfacial inclusion/matrix damage could not only compromise the structural integrity of the component but also significantly alter its ability to act as functional smart materials. In addition, both the agglomeration of the nanoadditives, such as carbon nanotubes (CNTs), and the degree of polarization could also affect the electromechanical behavior of the composite. In this paper, we develop a computational micromechanical model for the study of the influence of three types of internal defects on the piezoelectric performance of such composites. We investigate the influence of (i) the texture of the active phase, which is linked to the degree of polarization; (ii) the effect of agglomerations of the CNTs; and (iii) the presence of damage in the inclusion/matrix interphase region. Performance is assessed through the macroscopic response in various figures of merit (FOM). This work provides numerical evidence that the effective piezoelectric constants related to normal strain modes are strongly affected by the presence of damage in the interphase. Instead, the piezoelectric constant related to the shear strain remains unchanged by interfacial damage. Near the percolation threshold of the CNTs, piezocomposites exhibit a notable improvement in the piezoelectric response compared to the composite without nanomodification, as noted in previous works. Interestingly, this trend also applies in the presence of interfacial damage, and it is described in this work. The piezoelectric performance also depends on the texture of the active particles. We find, under some simplifications, the optimal orientation for the crystallites, and we find how the texture changes the performance in the presence of damage. Regarding energy harvesting applications, optimal energy conversion efficiency has been observed for polycrystalline inclusions that resembles a highly oriented single crystal and CNT volume fraction of 60% of percolation threshold. This is a quite surprising result since the optimal is usually expected for volume fractions very close to percolation and active inclusions with some orientational dispersion. The optimal CNT volume fraction depends on the presence of interfacial damage. Under perfect interface conditions, the energy conversion efficiency is improved with the presence of CNT agglomerations. Under imperfect interface conditions, there is no enhancement due to the addition of CNT.

This work presents a computational study on the impact of carbon nanotube (CNT) enriched matrix on the performance of 1–3 lead-free piezoelectric periodic composites. Specifically, we investigate a piezoelectric composite system consisting of [Formula: see text] parallel aligned fibers of polycrystalline barium titanate (BaTiO3) embedded in a polydimethylsiloxane (PDMS) matrix doped with multiwalled CNT. The effective properties and several figures of merit have been obtained to evaluate the performance of this composite system as is typically done for these materials used for sensing, actuating, or harvesting applications. The results reveal that, in lead-free BaTiO3/PDMS piezocomposites, the addition of CNTs in the PDMS matrix should be [Formula: see text] “being [Formula: see text] the percolation threshold”, but not higher. In another case, we will only improve the performance of the lead-free piezocomposite for sensing or actuating, but not for energy harvesting applications. This study provides insights into the use of multiwalled CNTs in lead-free piezocomposites and suggests the optimal concentration of CNTs to enhance their performance. The findings have potential implications for the development of new piezoelectric materials and devices for sensing and harvesting applications.

The DIY approach promotes small-scale digital manufacturing for the production of customized, fast moving consumer goods, including powder detergent. In this context, a machine was developed to manufacture a customized detergent according to the needs of the clients indicated on a digital platform connected to the machine. The detergent is produced by a mixing process of the formulation components carried out in a 3D mixer. Analysing the mixing performance of the process is essential to obtain a quality product. In this study, the mixing process of the powder detergent was modelled using the discrete element method. After validating it with experimental test, this model was utilized to study the mixing performance considering the allowable mass fraction range of every formulation component and a mixer speed of 45 rpm, and the dataset generated from this study was employed along with a machine learning algorithm to obtain a model to predict the mixing index. In this sense, twenty-five different combinations of the defined components were simulated and a mixing index of 0.98–0.99 was obtained in a time of 60 s, revealing that all the combinations were completely mixed. In addition, the developed model was validated with results obtained from the DEM model. The model predicts the mixing index in advance and with accuracy.