Field’s metal, a low-melting-point eutectic alloy composed of 51% In, 32.5 Bi% and 16.5% Sn by weight and with a melting temperature of 333 K, is widely used as liquid metal coolant in advanced nuclear reactors and in electro–magneto–hydrodynamic two-phase flow loops. However, its rheological and wetting properties in liquid state make this metal suitable for the formation of droplets and other structures for application in microfabrication. As with other low-melting-point metal alloys, in the presence of air, Field’s metal has an oxide film on its surface, which provides a degree of malleability and stability. In this paper, the viscoelastic properties of Field’s metal oxide skin were studied in a parallel-plate rheometer, while surface tension and solidification and contact angles were determined using drop shape analysis techniques.

In production of energetic materials, the rheological behavior of propellant doughs is a key factor to identify optimum and safe conditions for processing these special compounds. The analysis of their rheological properties during mixing and extrusion stages is needed to clarify the complex interrelations between the different process parameters and the expected performance of these products, providing essential information for the possible increase of system productivity and overall product quality. In this study, the rheological analysis of triple-base propellants and a propellant simulant was carried out by a DHR-2 rotational rheometer of TA Instruments. Furthermore, different testing techniques were evaluated for appropriate characterization of these propellants. According to the results of rheological measurements, these triple-base propellants were found to correspond to shear-thinning materials (pseudo-plastic behavior). Finally, the results obtained from these experimental observations were matched to well-known constitutive empirical models to describe the mathematical relation between viscosity and shear strain rate.

A large number of material and process parameters affect both the part quality and the process performance in pressure die-casting (PDC) processes. The complex relations between most of these variables make PDC process optimisation a difficult issue which has been widely studied for many years. Although there are several analytical and numerical models to optimise certain process parameters, it is difficult to establish a specific operational configuration for PDC machines that ensures the joint optimisation of these variables. Therefore, in this study, some of these optimisation models have been implemented in a Decision Support System (DSS) that allows us to define an operational region that establishes a setup of machine parameters that ensures the manufacture of quality parts. By using this DSS, the user can set the values of the input variables related to the casting material, the die, or the casting machine. Then the corresponding calculations are made by the system and the results are expressed in terms of certain output variables such as the maximum filling time, maximum filling fraction, or the plunger velocity profile among others. The DSS allows the user to estimate the influence between input and output variables and find proper values for the input variables to achieve an optimum operational range. Consequently, improved process performance can be achieved taking into account productivity, part quality, and economic aspects.