This paper investigates the underlying interplay between the key process parameters of magnetic abrasive finishing (MAF) in improving surface quality. The five process parameters considered were the working gap, rotational speed, feed rate, abrasive amount, and abrasive mesh when MAFed independently with two abrasive particles—SiC and Al₂O₃. A series of experiments were conducted with an in-house built MAF tool. Based on the main effect results, a model predicting roughness reduction was developed. Results show that surface quality improvement and the underlying dominant process parameters seem unique to the abrasive type used. When MAFed with SiC, the abrasive quantity and rotational speed influence the most. On the other hand, when MAFed with Al₂O₃, the trend is different to SiC, i.e., the abrasive mesh size and the working gap are dominant. The prediction model was well validated by independent experiments, indicating its accuracy in estimating and optimizing the process outcome. MAF is a simple process with a complex interplay between parameters. This is very crucial when abrasive type, size, and amount to be used are concerned, which warrants a deeper investigation in terms of underlying dynamics, interactions, and the deformation of abrasive, magnetic, and workpiece materials.

The flight performance and maneuverability of Odonata depends on wing shape and aero-structural characteristics, including airfoil shape, wingspan, and chord. Despite the superficial similarity between Odonata planforms, the frequency with which they are portrayed artistically, and the research interest in their aerodynamics, those features that are stable and those that are labile between species have not been identified. Studies have been done on 2D aerodynamics over corrugated wings; however, there is limited comparative quantified data on the planforms of Odonata wings. This study was undertaken to explore the scale relationships between the geometrical parameters of photogrammetrically reconstructed wings of 75 Odonata species, 66 from Epiprocta, and 9 from Zygoptera. The wing semi-spans captured in the database range from 24 to 85 mm. By carrying out an extensive statistical analysis of data, we show that the geometrical parameters for the suborder Epiprocta (dragonflies) can be classified into scale-dependent and independent parameters using regression analysis. A number of close correlations were found between the wingspan and the size of other structures. We found that amongst the variables considered, the largest independent variations against the forewing span were found in the chord of the hindwing, and that hindwing properties were not reliably predicted by the Odonata family. We suggest that this indicates continuous evolutionary pressure on this structure.

This paper addresses two NP-hard and strongly related problems in production planning of flexible manufacturing system (FMS), part type selection problem and machine loading problem. Various flexibilities such as alternative machines, tools, and production plans are considered. Real coded genetic algorithms (RCGA) that uses an array of real numbers as chromosome representation is developed to handle these flexibilities. Hybridizing with variable neighbourhood search (VNS) is performed to improve the power of the RCGA exploring and exploiting the large search space of the problems. The effectiveness of this hybrid genetic algorithm (HGA) is tested using several test bed problems. The HGA improves the FMS effectiveness by considering two objectives, maximizing system throughput and minimizing system unbalance. The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solution and the hybridization can improve the performance of the RCGA.

Electrical insulation failure is the most common failure mechanism in electrical machines (motors and generators). High temperatures and/or temperature gradients (HTTG) are the main drivers of insulation failure in electrical machines. HTTG combine with and augment other destructive effects from over-voltage, to voltage transients, overload and load variations, poor construction techniques, and thermal cycling. These operating conditions cause insulation damage that leads to electrical insulation failure. The insulation failure process is greatly accelerated by pollutants and moisture absorption. A simple and robust way to reduce HTTG and moisture adsorption is by maintaining constant internal temperatures. The current method to maintain elevated internal temperatures and reduce condensation issues is by internal electrical heating elements. This paper examines the effectiveness of applying thermoelectric coolers (TECs), solid-state heat pumps (Peltier devices), as heaters to raise a motor’s internal temperature by pumping heat into the motor core rather than heating the internal air. TEC technology is relatively new, and the application of TECs to heat a motor’s internal volume has not previously been explored. In this paper, we explore the hypothesis that TECs can pump heat into a motor when out of service, reducing the HTTG by maintaining high winding slot temperatures and eliminating condensation issues. This paper describes a test motor setup with simple resistive heating (traditional method), compared with the application of TECs with heat sinks, heat pipes, and a water circulation heat exchanger, to gauge the capability of TECs to heat the inner core or winding area. In this paper, we demonstrate the full integration of TECs into a motor. The results show that each of the systems incorporating the TECs would effectively pump heat into the core and keep the winding hot, eliminating condensation issues and water ingress due to thermal cycling.

This paper explores the application of thermoelectric cooler/heater (TEC) modules (Peltier heat pumps devices) to control core and winding temperatures, aiming to reduce the effects of thermal cycling and moisture issues that affect the life of electrical machines. Electrical windings in a motor will fail for a variety of reasons, and a major contributor to adverse effects of a motor’s life is humidity. Due to thermal cycling, air containing moisture is drawn into a motor through a variety of access points such as terminal boxes, bearings, end covers and mounting systems. Even spare or replacement motors specially stored in heated spare equipment stores suffer from moisture ingress because of normal daily temperature changes. The better a machine can be kept warm, the less it is affected by moisture and the effects of mechanical stresses from cycling temperatures. A series of experiments were conducted, whereby a TEC was attached to a section of motor core and was set up to pump heat into the core segment. The thermal properties of the core material and the capacity to control winding temperatures along the core in specific locations and over time was measured. The results of this research demonstrate that the temperature of the motor can be tightly controlled, thus enabling the reduction of the effects of moisture, and reducing core and winding temperature differences. This has a positive influence in reducing the thermal stresses, which will result in improved insulation life and machine reliability.

Few would argue that maximising the performance of the many flow networks (FNs) that operate for the benefit of our society and the economy is anything but essential. Through seeking to mitigate the risks posed by different asset failure modes, maintenance is critical to minimising disruptions and maximising resilience. Repairable flow network (RFN) optimisation and reliability centred maintenance (RCM) are both used to support asset related decisions in FNs but independently; meaning, attempts to maximise FN performance using RCM are likely to result in suboptimal outcomes. There is limited work bringing RFN optimisation and RCM together to support evaluating maintenance decisions in the context of holistic network level performance (measured in terms of profitability) and in a way that considers the complex structural and topological relationships that exist between process components, equipment components, and failure modes. Hence, this paper addresses this by developing the complex repairable flow network (CRFN) modelling framework with the goal of ensuring RFN optimisation integrates complex process and equipment (including failure modes) component topologies, such that it operates in alignment with the needs of RCM as part of maximising network flow in terms of gross profitability. This was done through the creation of a novel and transdisciplinary multi-layered network-based approach that integrates information from what were termed the facility, process, maintainable item, and failure mode levels. Furthermore, through running simulation experiments on an example CRFN, it is demonstrated that the CRFN modelling framework can be used to evaluate the impact different maintenance strategies have on maximising network flow in terms of gross profitability.