Energy modelling for mechanical machining processes is essential for energy labelling of machined products and as a foundation for selecting optimum cutting conditions that meet economic objectives while reducing energy demand and CO 2 footprint. Electrical energy demand in machining can be modelled in two parts: 'Basic Energy' demand by the machine tool and 'Tip Energy' for actual material removal. A significant amount of research and energy evaluation is based on the use of average specific energy values and ignores the impact of machining conditions. In this comprehensive study, the evaluation of specific tip energy is undertaken, and the effect of chip thickness, tool wear, nose radius and cutting environment is quantified. This work is an essential guide for the application of models to estimate energy demand in practical machining processes. It is of significant importance to improve accuracy in energy-centric modelling of machining processes for sustainable manufacture and resource efficiency.
The optimisation of the laser additive manufacturing (AM) process is a challenging task when a new material is considered. Compared to the selection of other process parameters such as laser power, scanning speed and hatch spacing, optimisation of powder layer thickness is much more time-consuming and costly because a new run is normally needed when the layer thickness value is changed. In practice, the layer thickness is fixed to a value that is slightly higher than the average particle size. This paper introduces a systematic approach to layer thickness optimisation based on a theoretical model of the interactions between the particles, the wiper and the build plate during the powder deposition. The focus is on a systematic theoretical and experimental investigation of the effect of powder layer thickness on various powder bed characteristics during single-layer and multi-layer powder deposition. The theoretical model was tested experimentally using Hastelloy X (HX) with an average particle size of 34.4 µm. The experimental results validated the simulation model, which predicted a uniform powder bed deposition when employing a 40 µm layer thickness value. Lower (30 µm) and higher (50 µm) layer thickness values resulted in large voids and short-feed defects, respectively. The subsequent optimisation of the scanning speed and hatch spacing parameters was executed using a 40 µm layer thickness. The optimum process parameters were then used to examine the microstructure and tensile performance of the as-fabricated HX. This study provides an improved understanding of the powder deposition process and offers insights into the selection of suitable powder layer thicknesses in laser AM.
Polymer nanocomposites filled with carbon nanotubes are observed to present an onset of the insulator-to-conductor transition through previous experimental studies. In this work, numerical simulations based on Monte Carlo method are performed to investigate the percolation threshold. The conductive fillers are modeled as a three dimensional (3D) network of identical units dispersed in the polymer matrix. However, the distribution of the fibers is not uniform due to the existence of the emulsion particles. The effects of the aspect ratio and fiber length on the critical volume fraction are studied. Linearization is made to the logarithm of simulation results. The calculated critical volume fraction is used in the power-law function to predict the electrical conductivity of the polymer composites. The results from the homogeneous model (without emulsion particles) and the model containing emulsion particles are compared. The effects of the size and the geometrical variation of the emulsion particles are evaluated.
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