The energy analysis of injection moulding processes is influenced by complex interactions amongst the moulded part, its material, the injection machine, the process parameters and the environmental conditions. The availability of energy usage analyses that comprehend information on specific materials and machine kind is limited. This paper reports a study that estimates and analyses the power usage profile (PUP), the specific energy consumption (SEC), and the energy distribution at an operation level of two different injected parts made of acrylonitrile-butadiene-styrene and produced in a hybrid injection moulding machine. The methodology followed by the authors incorporates an experiment carried out in an industrial facility. A large sample size, and data-acquisition and data post-processing processes to obtain an accurate PUP and SEC are used. A breakdown of the energy consumed by the injection moulding process of the parts studied at an operation level using Sankey diagrams is discussed. The study results are used to identify strategies to reduce the energy consumed by the processes. The methodology employed, and the strategies reported could be used with other plastic parts regardless of the material and machine used. The results reported are new experimental data useful input for theoretical models.

Very commonly, a mechanical workpiece manufactured industrially includes more than one machining operation. Even more, it is a common activity of programmers, who make a decision in this regard every time a milling and drilling operation is performed. This research is focused on better understanding the power behavior for face milling and drilling manufacturing operations, and the methodology followed was the design of experiments (DOEs) with the cutting parameters set in combination with toolpath evaluation available in commercial software, having as main goal to get a predictive power equation validated in two ways, linear or nonlinear, and understanding the energy consumption and the quality surface in face milling and final diameter in drilling. The results show that it is possible to find difference in a power demand of 1.52 kW to 3.9 kW in the same workpiece, depending on the operations (face milling or drilling), cutting parameters, and toolpath chosen. Additionally, the equations modelled showed acceptable values to predict the power, with p values higher than 0.05 which is the significance level for the nonlinear and linear equations with an R square predictive of 98.36. Some conclusions established that optimization of the cutting parameters combined with toolpath strategies can represent an energy consumption optimization higher than 0.21% and the importance to try to find an energy consumption balance when a workpiece has different milling operations.

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The design of efficient and quieter kitchen appliances represents a significant challenge for manufacturers. In domestic refrigerators, the compressor, condenser’s fan and evaporator’s fan are important sources of noise. This paper presents the statistical energy analysis (SEA) carried out to estimate the sound pressure level generated in a domestic refrigerator’s evaporator subsystem. The work focuses on the evaporator’s fan and the subsystem’s mechanical assembly; these constitute the main source of noise and path for vibration transmission. The results of the analysis for a frequency band noise between 100 Hz and 10 kHz are reported. A semi-anechoic chamber was used to validate the SEA analysis presented herein; the authors also set up an experiment using microphones and accelerometers complying with standard ISO-3745, achieving a correlation of up to 96%. The analysis and experimental data were then used to produce a set of design guidelines to help kitchen appliance designers in the configuration of innovative cabinet geometries and the implementation of noise reduction strategies in household refrigerators.