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The purpose of this paper is to compare the influence of relative density (RD) and strain rates on failure mechanism and specific energy absorption (SEA) of polyamide lattices ranging from bending to stretch-dominated structures using selective laser sintering (SLS).

Three bending and two stretch-dominated unit cells were selected based on the Maxwell stability criterion. Lattices were designed with three RD and fabricated by SLS technique using PA12 material. Quasi-static compression tests with three strain rates were carried out using Taguchi's L9 experiments. The lattice compressive behaviour was verified with the Gibson–Ashby analytical model.

It has been observed that RD and strain rates played a vital role in lattice compressive properties by controlling failure mechanisms, resulting in distinct post-yielding responses as fluctuating and stable hardening in the plateau region. Analysis of variance (ANOVA) displayed the significant impact of RD and emphasised dissimilar influences of strain rate that vary to cell topology. Bending-dominated lattices showed better compressive properties than stretch-dominated lattices. The interesting observation is that stretch-dominated lattices with over-stiff topology exhibited less compressive properties contrary to the Maxwell stability criterion, whereas strain rate has less influence on the SEA of face-centered and body-centered cubic unit cells with vertical and horizontal struts (FBCCXYZ).

This comparative study is expected to provide new prospects for designing end-user parts that undergo various impact conditions like automotive bumpers and evolving techniques like hybrid and functionally graded lattices.

To the best of the authors' knowledge, this is the first work that relates the strain rate with compressive properties and also highlights the lattice behaviour transformation from ductile to brittle while the increase of RD and strain rate analytically using the Gibson–Ashby analytical model.

The positive correlation between students’ internship and employability is well documented. However, there is no consensus on its relationship with academic performance. Previous studies investigated the Bachelor level: in the UK, the so-called sandwich placement is positively correlated; in the US, the results are mixed. This study aims to expand the scientific literature and focuses on Politecnico di Torino, Italy: the cases of the Industrial and Management Engineering Bachelor (BA) and Master of Science (MS) degree programs are examined.

The academic performance of 2,279 BA students and 2,560 MS students graduated between 2016 and 2021 is examined. Ordinary least square models are employed to test the relationship between grades and internship, controlling for ex ante academic scores, gender, age and geographical origin.

The results show a small robust negative correlation between internship and grades, especially at the bachelor level, where potentially a self-selection occurs. At the master level, the negative relationship is significant but very small and might be partially determined by the marks of the exams substituting the internship.

This study contributes to the empirical research on the relationship between students’ grade and internship and suggests the presence of complementarities in several ways: the Italian context, characterized by an optional internship is examined, both at the BA and MS level. Universities that intend to organize internship as overlapping with lectures and exams should be cautious, especially when considering the BA level students, whose maturity and skills are less developed than MS ones.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.