Cell walls of extruded pea snacks: Morphological and mechanical characterisation and finite element modelling

Pulses extruded foods can be envisaged asall solid foams with voids and walls, the latter being considered as a dense starch/protein composite. Pea flour (PF) and blends of pea starch and pea protein isolate (PPI) with different protein contents (0.5-88% dry basis) were extruded to obtain models of dense starch-protein composites. Their morphology was revealed by CLSM microscopy, and their mechanical properties were investigated using a three-point bending test complemented by Finite Element Method (FEM) modelling. Composite morphology revealed protein aggregates dispersed in the starch matrix. It was described by a starch-protein interface index Ii computed from the measured total area and perimeter of protein aggregates. The mechanical test showed that the extruded PF and PPI ruptured in the elastic domain, while the extruded starch-PPI (SP) blends ruptured in the plasticity domain. The mechanical properties of pea composites were weakened by increasing the particle volume fractions, including proteins and fibres, probably due to the poor adhesion between starch and the other constituents. The mechanical behaviour of pea composites did not accurately follow simple mixing laws because of their morphological heterogeneity. Modelling results show that the elastoplastic constitutive model using the Voce plasticity model satisfactorily described the hardening behaviour of SP blend composites. Reasonable agreement (2-10%) was found between the experimental and modelling approaches for most materials. The computed Young's modulus (1.3-2.5 GPa) and saturation flow stress (20-45 MPa) increased with increasing Ii (0.7-3.1), reflecting the increase of interfacial stiffening with the increase of contact area between starch and proteins. FEM modelling allowed to identify the mechanical effect of structural heterogeneities.

Modelling the Process of Fused Deposition Modelling and the Effect of Temperature on the Mechanical, Roughness, and Porosity Properties of Resulting Composite Products

Advances in the additive manufacturing (AM) processes have opened up the possibilities of widely using them in various structural sectors. Since 1980s this technology has been in permanent mutations. The ramification of the AM technology makes it difficult to obtain a clear impression of its potentialities. Predicting and controlling the mechanical characteristics of printed products is crucial for their final practical use. This study mainly aims to characterize the impact of printing parameters on the characteristics of printed articles and to evaluate their significance.

Morphology and mechanical behaviour of pea-based starch-protein composites obtained by extrusion

Starch-legume protein composites were obtained by extrusion of pea flour and pea starch-protein blend at various specific mechanical energies (100-2000 kJ/kg) and a temperature low enough to avoid expansion. The morphology of these composites displayed protein aggregates dispersed in a starch matrix, revealed by microscopy. Image analysis was used to determine the median width of protein aggregates (D50), their total perimeter and surface, from which a protein/starch interface index (Ii) was derived. The mechanical properties of composites were determined by a three-point bending test. The pea flour composites had a higher interface index Ii (1.8-3.1) with lower median particle width D50 (8-18 μm) and a more brittle behaviour than the blend composites that had a lower Ii (1-1.1) and higher D50 (22-31 μm). For both materials, rupture stress and strain were negatively correlated with Ii. This result suggested that there was a poor interfacial adhesion between the pea starch and proteins.

Virtual design of electrospun-like gelatin scaffolds: the effect of three-dimensional fibre orientation on elasticity behaviour

Remarkable mechanical performance of biological tissues is explained by a hierarchical fibrous structure. Designing materials that have similar properties is challenging because of the need to assess complex deformation mechanisms. In order to shed more light on architectural possibilities of biopolymer fibrous networks, we propose a numerical study that relates the fibre arrangement to the elastic modulus of a gelatin scaffold obtained using electrospinning. The adopted approach is based on the virtual designing of scaffolds using all possible combinations of Euler angles that define fibre orientations including preferable alignment. The generated networks are converted into a finite element model and the predicted elastic behaviour is examined. Predictions show that the fibre alignment achieved experimentally in biopolymer fibrous networks is for most of the fibres exhibiting an orthotropic behaviour. Some particular combinations of Euler angles allow transverse isotropic architectures while only limited cases are isotropic. A large sensitivity of Young's moduli to Euler angles is achieved describing multiple scenarios of independent anisotropic behaviours. An anisotropy ratio of the elastic behaviour is suggested based on a suitable combination of elastic moduli. Such a ratio exhibits a wide variation depending on individual and coupled effects of Euler angles. The finite element model predicts 2D, 3D and 4D maps representing all possible configurations of fibre alignment and their consequences on elastic behaviour. The predicted fibre orientation representing the observed anisotropic behaviour of electrospun gelatin networks demonstrates unbalanced contributions of in-plane and out-of plane fibres for a large range of processing conditions.

Plasma spray process modelling using artificial neural networks: Application to Al2O3-TiO2 (13% by weight) ceramic coating structure

Thermal spraying is a versatile technique of coating manufacturing implementing large variety of materials and processes. The manufacture control is constrained by the understanding of the physical phenomena occurring during the spraying. It is however penalized by the large number of processing parameters (up to 50), their interdependencies, their correlations with the coating attributes and the stability of the process. Numerous statistical, heuristic or physical models intended to response to these constrains, very often partially because considering some aspects of the process. This work aims at considering a more global approach based on a powerful statistical methodology using artificial intelligence. Following this approach, the physical phenomena are encoded in a structure called Artificial Neural Network (ANN). An application of the ANN methodology is discussed in the case of the APS spray process. Some processing parameters categories are related to some coating properties for alumina-titania (13% by weight) ceramic coatings. ANN optimization is presented and discussed. Predicted results show globally a well agreement with the experimental values. Some conclusions point out the advantages of the ANN on the conventional methods, such as the design of experiments, used usually to recognize the controlling factors in a process.