For several decades, the design and manufacture of electrical machines has been considered a technically mature area and, as a result, research and development in the area has been extremely limited, ...even though this is a crucial technology in the application of electrical energy. Electrical machines are used in over 80% of the world’s energy conversion processes—first to create electrical energy, which can be easily transmitted, and second to convert that energy into mechanical form for applications ranging from dishwashers to transportation, and from medical devices to those used for industrial processes. Today, two technologies are changing this. The first is the development of power electronic drives and the second is the introduction of additive manufacturing technology. The latter technology has opened up new areas for innovation and research, and many conventional processes are likely to become obsolete. Considering the overall consumption of electricity by electrical machines, the design freedom granted by the novel production technology gives the opportunity for even more efficient, object-oriented machines to be built, with a lower environmental impact and less raw material consumption. If this technology can be developed to maturity, it would have a significant positive impact on the desired green transition that is being pursued all over the world.
Metal additive manufacturing (AM) technology is maturing. Although currently slower and less reliable than traditional production methods, AM systems shine when producing parts with unconventional ...topologies or in small quantities. Like countless other research communities, the electrical machine (EM) research community has shifted considerable efforts towards integrating AM systems into the EM production cycle to implement more powerful and efficient topology optimized (TO) next-generation EMs. In this paper, the state-of-the-art printing of soft magnetic, hard magnetic, and electrically conductive materials was investigated to evaluate the maturity of each material type for integration into EM construction. The highest maturity was identified for AM pure copper, showing characteristics equivalent to commercial high purity copper. In contrast, AM permanent magnets were the least mature: suffering from low power density and limited magnetization capacities. Printed soft magnetic steels were characterized as halfway in-between: on one side showing equivalent DC magnetic properties to conventional non-oriented steels, but on the other – suffering from high eddy current losses in AC applications. Based on the study's findings, it would appear that the emergence of additively manufactured EMs is only a matter of time. We predict a dramatic increase in the printing of prototype TO components within the next few years, focusing most likely on TO machine windings, heat exchangers, and synchronous rotors.
Additive manufacturing (AM) or 3D printing has opened up new opportunities for researchers in the field of electrical machines, as it allows for more flexibility in design and faster prototyping, ...which can lead to more efficient and cost-effective production. An overview of the primary AM techniques utilized for designing electrical machines is presented in this paper. AM enables the creation of complex and intricate designs that are difficult or impossible to achieve using traditional methods. Topology Optimization (TO) can be used to optimize the design of parts for various purposes such as weight, thermal, material usage and structural performance. This paper primarily concentrates on the most recent studies of the AM and TO of the reluctance machines. The integration of AM with TO can enhance the design and fabrication process of magnetic components in electrical machines by overcoming current manufacturing limitations and enabling the exploration of new design possibilities. The technology of AM and TO both have limitations and challenges which are discussed in this paper. Overall, the paper offers a valuable resource for researchers and practitioners working in the field of AM and TO of electrical machines.
This paper presents current research trends and prospects of utilizing additive manufacturing (AM) techniques to manufacture electrical machines. Modern-day machine applications require extraordinary ...performance parameters such as high power-density, integrated functionalities, improved thermal, mechanical & electromagnetic properties. AM offers a higher degree of design flexibility to achieve these performance parameters, which is impossible to realize through conventional manufacturing techniques. AM has a lot to offer in every aspect of machine fabrication, such that from size/weight reduction to the realization of complex geometric designs. However, some practical limitations of existing AM techniques restrict their utilization in large scale production industry. The introduction of three-dimensional asymmetry in machine design is an aspect that can be exploited most with the prevalent level of research in AM. In order to take one step further towards the enablement of large-scale production of AM-built electrical machines, this paper also discusses some machine types which can best utilize existing developments in the field of AM.
The bio-inspired algorithms are novel, modern, and efficient tools for the design of electrical machines. However, from the mathematical point of view, these problems belong to the most general ...branch of non-linear optimization problems, where these tools cannot guarantee that a global minimum is found. The numerical cost and the accuracy of these algorithms depend on the initialization of their internal parameters, which may themselves be the subject of parameter tuning according to the application. In practice, these optimization problems are even more challenging, because engineers are looking for robust designs, which are not sensitive to the tolerances and the manufacturing uncertainties. These criteria further increase these computationally expensive problems due to the additional evaluations of the goal function. The goal of this paper is to give an overview of the widely used optimization techniques in electrical machinery and to summarize the challenges and open problems in the applications of the robust design optimization and the prospects in the case of the newly emerging technologies.
Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions ...and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.
This paper presents an analytical modelling technique based on modified winding function and co‐energy analysis, for characterisation of synchronous reluctance machines, in conjunction with a hybrid ...algorithm for mapping of the machine's operating point on BH curve and the corresponding performance parameters in non‐linear magnetic condition. The model successfully provides the mean torque and the torque profiles of a synchronous reluctance motor as a function of input current and load‐angle in the magnetic linear and non‐linear conditions. The obtained results of this proposed model are compared with finite‐element‐analysis (FEA) based commercial design software Simcentre‐MAGNET. The comparison proves this model's capability to estimate the machine's performance parameters with considerable accuracy from an already established design analysis technique, FEA. The proposed modelling technique requires only a fraction of simulation runtime when compared to other design analysis techniques. Considering the accuracy of this modelling technique and the least requirement of computation resources and simulation time, this model can be used as an initial iterative‐design tool for mass personalised, additively manufactured electrical machines where rapidness of the iterative design analysis tool is of paramount importance.
Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, ...necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.
Additively manufactured soft magnetic Fe-3.7%w.t.Si toroidal samples with solid and novel partitioned cross-sectional geometries are characterized through magnetic measurements. This study focuses on ...the effect of air gaps and annealing temperature on AC core losses at the 50 Hz frequency. In addition, DC electromagnetic material properties are presented, showing comparable results to conventional and other 3D-printed, high-grade, soft magnetic materials. The magnetization of 1.5 T was achieved at 1800 A/m, exhibiting a maximum relative permeability of 28,900 and hysteresis losses of 0.61 (1 T) and 1.7 (1.5 T) W/kg. A clear trend of total core loss reduction at 50 Hz was observed in relation to the segregation of the specimen cross-sectional topology. The lowest 50 Hz total core losses were measured for the toroidal specimen with four internal air gaps annealed at 1200 °C, exhibiting a total core loss of 1.2 (1 T) and 5.5 (1.5 T) W/kg. This is equal to an 860% total core loss reduction at 1 T and a 510% loss reduction at 1.5 T magnetization compared to solid bulk-printed material. Based on the findings, the advantages and disadvantages of printed air-gapped material internal structures are discussed in detail.
A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and ...available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.
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