Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Abstract:The future of biomaterial design will rely on temporary implant materials that degrade while tissues grow, releasing no toxic species during degradation and no residue after full regeneration of the targeted anatomic site. In this aspect, Mg and its alloys are receiving increasing attention because they allow both mechanical strength and biodegradability. Yet their use as biomedical implants is limited due to their poor corrosion resistance and the consequential mechanical integrity problems leading to corrosion assisted cracking. This review provides the reader with an overview of current biomaterials, their stringent mechanical and chemical requirements and the potential of Mg alloys to fulfil them. We provide insight into corrosion mechanisms of Mg and its alloys, the fundamentals and established models behind stress corrosion cracking and corrosion fatigue. We explain Mgs unique negative differential effect and approaches to describe it. Finally, we go into depth on corrosion improvements, reviewing literature on high purity Mg, on the effect of alloying elements and their tolerance levels, as well as research on surface treatments that allow to tune degradation kinetics. Bridging fundamentals aspects with current research activities in the field, this review intends to give a substantial overview for all interested readers; potential and current researchers and practitioners of the future not yet familiar with this promising material.

Zn(O,S) buffer layer electronic configuration is determined by its composition and thickness, tunable through atomic layer deposition. The Zn K and L-edges in the X-ray absorption near edge structure verify ionicity and covalency changes with S content. A high intensity shoulder in the Zn K-edge indicates strong Zn 4s hybridized states and a preferred c-axis orientation. 2–3 nm thick films with low S content show a subdued shoulder showing less contribution from Zn 4s hybridization. A lower energy shift with film thickness suggests a decreasing bandgap. Further, ZnSO4 forms at substrate interfaces, which may be detrimental for device performance.

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Magnesium and its alloys have recently attracted great attention as potential materials for the manufacture of biodegradable implants. Unfortunately, their inadequate resistance to the simultaneous action of corrosion and mechanical stresses in the human body have hampered their use as implant materials. This work aims at evaluating the Stress Corrosion Cracking (SCC) susceptibility of the AZ31 Mg alloy after being machined under cryogenic cooling. The SCC behaviour was evaluated by means of Slow Strain Rate Tests (SSRTs) in Simulated Body Fluid (SBF) at 37 °C. Prior to testing, a full characterization of the machined surface integrity, including microstructural observations, residual stress, nano-hardness measurements and surface texture analysis was carried out together with the assessment of the corrosion properties through potentiodynamic polarization curves. In addition, the morphology of the fracture surfaces after SSRTs was analysed by means of 3D optical profiler and Scanning Electron Microscopy (SEM). The improved corrosion resistance due to the increased extension of the nano-surface layer and to the compressive residual stresses represents the reason of the reduced SCC susceptibility of cryogenically machined AZ31 samples as compared to dry machined ones.

Ti-6Al-4V has been extensively used in structural applications in various engineering fields, from naval to automotive and from aerospace to biomedical. Structural applications are characterized by geometrical discontinuities such as notches, which are widely known to harmfully affect their tensile strength. In recent years, many attempts have been done to define solid criteria with which to reliably predict the tensile strength of materials. Among these criteria, two local approaches are worth mentioning due to the accuracy of their predictions, i.e., the strain energy density (SED) approach and the theory of critical distance (TCD) method. In this manuscript, the robustness of these two methods in predicting the tensile behavior of notched Ti-6Al-4V specimens has been compared. To this aim, two very dissimilar notch geometries have been tested, i.e., semi-circular and blunt V-notch with a notch root radius equal to 1 mm, and the experimental results have been compared with those predicted by the two models. The experimental values have been estimated with low discrepancies by either the SED approach and the TCD method, but the former results in better predictions. The deviations for the SED are in fact lower than 1.3%, while the TCD provides predictions with errors almost up to 8.5%. Finally, the weaknesses and the strengths of the two models have been reported.