With the increasing requirements for accuracy and integrity of machining under severe application environment, electrochemical discharge machining (ECDM) has evolved continuously for fabricating micro-holes. The method can be categorized into two types based on whether the material being machined is electrically conductive or non-conductive. Most research to date has been focused on non-conductive materials, with numerous introductory review articles. However, despite a growing number of studies of machining conductive materials, there is a lack of systematic analyses and summaries. Therefore, the purpose of this paper is to fill an important gap in the literature by presenting a comprehensive review of the research and development of ECDM technology for processing conductive materials, especially micro-holes. First, the characteristics of this method are summarized. Second, the development of this method and the mechanism of discharge are compared and analyzed. Third, a discussion is given on how machining performance is affected by parameters such as solution conductivity, electrical parameters, tool electrode structure, and workpiece material. Also, to enhance the machining quality, some auxiliary ECDM measures are presented. Finally, future prospects and trends of ECDM are identified.
With the rapid development of computer technology and the improvement of intelligent technologies in electric power engineering, the volume of data has increased exponentially. Data mining technology can be utilized to search information hidden in the huge amounts of data, and then the data can be transformed into useful knowledge to promote the development of electric power technology. In order to be acquainted with the research and application progress of data mining technology in electric power engineering, several major data mining algorithms are introduced in this paper, including ANN (Artificial Neural Network) algorithm, SVM (Support Vector Machine) algorithm, decision tree algorithm, K-means algorithm, NBC (Naive Bayesian Classification) algorithm and Apriori algorithm. And then, the methods of data mining technology in prediction, classification, clustering and association rules analysis are explained in detail in this engineering, which are combined with the electricity price prediction, power load forecasting, fault type identification, system state classification, power generation side association rules, power grid operation data association analysis. At last, this technology in electric power engineering is summarized and an expectation for the future development is provided.
The rapid development of artificial intelligence (AI) technology makes it possible for achieving intelligent forming. It will bring great breakthrough of material forming technology, realizing the unmanned watching, intelligent processing design and intelligent control during forming process. Moreover, it can greatly improve the forming accuracy, mechanical properties, forming efficiency and economic benefits, and promote the continuous emergence of new forming technology. Thus, the intelligent forming technology, integrating AI technology and advanced forming technology, has become an international research focus. This paper reviews the recent developments of intelligent forming technology from four kinds of common forming technology, i.e., intelligent casting, intelligent plastic forming, intelligent welding, and intelligent additive manufacturing. Moreover, the current research issues and future trends of intelligent forming technology are put forward at the end of the paper.
As an auxiliary mechanical device, Air Turbine Starter (ATS) uses compressed air as power source to start and drive the engine. It withstands the impact of high-pressure airflow during operation, which may cause collision between key components. For this reason, it is necessary to investigate the transient dynamics of ATS rotor system. However, different from the traditional dual rotor structure, ATS uses magnetic reduction gear (MRG) as a reduction unit, which involves multiple physical fields such as magnetic field and stress field, bringing challenges to transient dynamics analysis. In this paper, the magnetic interaction forces between various rotors are innovatively simplified into the form of springs, and added to the solution model to achieve the decoupling of multiple physical fields. On this basis, the transient displacement response of MRG-ATS has been analyzed using transient dynamics theory. The results indicate that the transient displacement of the rotor system has obvious characteristics of oscillation attenuation. The study reveals the feasibility of MRG-ATS application under transient shock.
Milling is widely used to machine the structures with low-rigidity in astronautic and aeronautic industries, while chatter vibration, which is a great limitation and a serious problem, is easy to occur in this kind of process due to the weak stiffness of the structures. To solve the machining problems caused by chatter, prediction and suppression are two important methods that are commonly used by researchers and industry engineers. This article reviews the study progresses on the prediction and suppression of the chatter occurring in milling process of the low-rigidity structure. The dynamic model, acquisition of dynamic parameters, and suppression techniques are introduced. Besides, the problems and the outlooks of the future coming research are also given in the conclusions.