Self Piercing Riveting for Metal-Polymer Joints

Self-Piercing Riveting (SPR) is a sheet metal joining technique based on the insertion of a rivet into two or more sheets, with no preparatory hole. This process has gained wide diffusion in the automotive industry, due to the increasing use of materials alternative to steel, that are difficult or impossible to join with traditional techniques. In particular, polymeric materials are becoming increasingly used, due to their favorable weight/strength ratio. This paper reports the results of experimental investigations, aimed at identifying the variables affecting the mechanical characteristics of mixed metal-plastic joints. A statistic model for the optimization of the geometrical parameters has been computed. The paper demonstrates that self-piercing riveting appears competitive for metal/polymer junction. The results analyzed in light of statistical techniques show that some geometrical parameters affect joint performance more than others and can therefore be used as independent variables for joint performance optimizatio

Influence of High-Productivity Process Parameters on the Surface Quality and Residual Stress State of AISI 316L Components Produced by Directed Energy Deposition

The production of large components is one of the most powerful applications of laser powder-directed energy deposition (LP-DED) processes. High productivity could be achieved, when focusing on industrial applications, by selecting the proper process parameters. However, it is of crucial importance to understand the strategies that are necessary to increase productivity while maintaining the overall part quality and minimizing the need for post-processing. In this paper, an analysis of the dimensional deviations, surface roughness and subsurface residual stresses of samples produced by LP-DED is described as a function of the applied energy input. The aim of this work is to analyze the effects of high-productivity process parameters on the surface quality and the mechanical characteristics of the samples. The obtained results show that the analyzed process parameters affect the dimensional deviations and the residual stresses, but have a very little influence on surface roughness, which is instead dominated by the presence of unmelted particles

Fasi finali e riutilizzo di età storica nel Nuraghe Cuccurada di Mogoro (OR)

Nel sito archeologico in località Cuccurada, in territorio comunale di Mogoro, le ricerche hanno evidenziato un insediamento pluristratificato, con fasi di occupazione del Neolitico recente (cultura di Ozieri), dell’Eneolitico (cultura di Monte Claro) e delle Età del Bronzo e del Ferro, con un riutilizzo dell’area in epoca romana e medievale. Il complesso è già abbastanza noto in letteratura, ma ancora parzialmente inedito per quanto riguarda i materiali e i dati di scavo. Nel presente contributo si vuole presentare nel dettaglio le diverse fasi di occupazione del sito, soprattutto in relazione ai momenti finali della frequentazione protostorica (Bronzo Finale/I Ferro) ed alla rioccupazione del complesso in età romana e medievale.In the archaeological site of Cuccurada, in the territory of Mogoro, the researches have evidenced a pluristratified settlement, with phases of occupation of the recent Neolithic (culture of Ozieri), of the Eneolithic age (culture of Monte Claro) and of the Bronze and Iron Ages, with a re-use of the area in roman and medieval period. The complex is already enough famous in literature, but still partially unknown regarding the materials and the excavation data. In the present work we want to present in detail the different occupation phases of the site, especially in relationship to the final moments of the proto-historic frequentation (Final Bronze/I Iron Age) and to the last re-use of the complex in Roman and medieval age

Probing the hydrogen melting line at high pressures by dynamic compression

We investigate the capabilities of dynamic compression by intense heavy ion beams to yield information about the high pressure phases of hydrogen. Employing ab initio simulations and experimental data, a new wide range equation of state for hydrogen is constructed. The results show that the melting line up to its maximum as well as the transition from molecular fluids to fully ionized plasmas can be tested with the beam parameters soon to be available. We demonstrate that x-ray scattering can distinguish between phases and dissociation states

Microstructure and Residual Stress Evolution of Laser Powder Bed Fused Inconel 718 under Heat Treatments

The current work aimed to study the influence of various heat treatments on the microstructure, hardness, and residual stresses of Inconel 718 processed by laser powder bed fusion process. The reduction in residual stresses is crucial to avoid the deformation of the component during its removal from the building platform. Among the different heat treatments, 800 °C kept almost unaltered the original microstructure, reducing the residual stresses. Heat treatments at 900, 980, and 1065 °C gradually triggered the melt pool and dendritic structures dissolution, drastically reducing the residual stresses. Heat treatments at 900 and 980 °C involved the formation of δ phases, whereas 1065 °C generated carbides. These heat treatments were also performed on components with narrow internal channels revealing that heat treatments up to 900 °C did not trigger sintering mechanisms allowing to remove the powder from the inner channels

On the quality of unsupported overhangs produced by laser powder bed fusion

One of the main design constraints for additive manufacturing is the definition of downward-facing surfaces, which can lead to problems, like part failing or warping, during construction and poor surface quality. In this paper, a specific index has been defined to represent the surface quality of the downward-facing surfaces induced by the laser powder bed fusion (L-PBF) process. In order to validate the quality index, a design of experiment (DoE) that considers geometric parameters of the overhangs has been defined and carried out, and the quality of resulting surfaces has been evaluated using an optical scanning system. The statistical analysis (ANOVA) has allowed identifying the relationships between significant geometrical parameters and the quality index here proposed

Abrasive fluidized bed finishing to improve the fatigue behaviour of Ti6Al4V parts fabricated by electron beam melting

A study of the abrasive fluidized bed (AFB) finishing process was conducted to quantify the obtainable improvement of the fatigue behaviour of Ti6Al4V parts produced by electron beam melting (EBM). Axial-symmetric EBM samples were rotated at high speed inside a fluidized bed of stainless-steel media. The effects of the treatment time and the rotational speed on morphological features and fatigue life of the EBM samples were investigated. Outcomes showed that the improvement in surface properties induced by the AFB finishing process determined an increase up to 50% in fatigue life and a shift of the S-N curve

A review of heat treatments on improving the quality and residual stresses of the Ti–6Al–4V parts produced by additive manufacturing

Additive manufacturing (AM) can be seen as a disruptive process that builds complex components layer upon layer. Two of its distinct technologies are Selective Laser Melting (SLM) and Electron Beam Melting (EBM), which are powder bed fusion processes that create metallic parts with the aid of a beam source. One of the most studied and manufactured superalloys in metal AM is the Ti–6Al–4V, which can be applied in the aerospace field due to its low density and high melting point, and in the biomedical area owing to its high corrosion resistance and excellent biocompatibility when in contact with tissues or bones of the human body. The research novelty of this work is the aggregation of all kinds of data from the last 20 years of investigation about Ti–6Al–4V parts manufactured via SLM and EBM, namely information related to residual stresses (RS), as well as the influence played by different heat treatments in reducing porosity and increasing mechanical properties. Throughout the report, it can be seen that the expected microstructure of the Ti–6Al–4V alloy is different in both manufacturing processes, mainly due to the distinct cooling rates. However, heat treatments can modify the microstructure, reduce RS, and increase the ductility, fatigue life, and hardness of the components. Furthermore, distinct post-treatments can induce compressive RS on the part’s surface, consequently enhancing the fatigue life

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy