Ultrasonic-assisted Electrical Discharge Machining (UV-EDM) has emerged as a promising technique for improving machining efficiency and electrode life, particularly when processing hard conductive materials. This study presents a hybrid multi-criteria optimization framework that integrates Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Attributive Border Approximation Area Comparison (MABAC) method to optimize two conflicting performance measures: Material Removal Rate (MRR) and Electrode Wear Rate (EWR).

This study investigates ultrasonic vibration-assisted (UV) CNC milling of hardened 90CrSi steel cylindrical surfaces, with emphasis on ultrasonic horn design, experimental analysis, and multi-objective optimization of machining parameters, addressing the need for an integrated framework combining system design, experimental validation, and multi-objective optimization. A quarter-wavelength ultrasonic horn was designed and experimentally validated to operate at a frequency of 20 kHz. By adjusting the horn–workpiece system, stable vibration amplitudes were achieved to enable effective ultrasonic-assisted milling of cylindrical surfaces. Milling experiments based on a Box–Behnken design were conducted to examine the effects of vibration amplitude, cutting speed, feed rate, and radial depth of cut on material removal rate (MRR) and surface roughness (Ra). Surrogate models using response surface methodology (RSM) and Gaussian process regression (GPR) were developed to predict machining performance. A GPR-assisted NSGA-II algorithm was then applied to simultaneously maximize MRR and minimize Ra, resulting in a well-defined Pareto front that reveals the trade-off between machining productivity and surface quality.

This study investigates the modeling and single-objective optimization of surface roughness (Ra) and material removal rate (MRR) in electrical discharge machining (EDM) of external cylindrical surfaces of hardened 90CrSi tool steel. The machining process is enhanced using ultrasonic vibration assistance and graphite electrodes to improve surface integrity and productivity. Gaussian Process Regression (GPR) and Response Surface Methodology (RSM) were employed to construct predictive models for Ra and MRR based on key process parameters, including vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), peak current (IP), and servo voltage (SV). The GPR model provided superior predictive performance for surface roughness, while RSM was more effective in modeling MRR. Optimization results showed that the minimum Ra of 1.6216 µm was achieved at A = 2.7743 µm, Ton = 8.0000 µs, Toff = 11.8294 µs, IP = 8.1723 A, and SV = 4.7936 V. Meanwhile, the maximum MRR of 12.1989 g/h was obtained at A = 3.5339 µm, Ton = 16.0000 µs, Toff = 8.0000 µs, IP = 15.0000 A, and SV = 4.0000 V. The findings provide valuable insights into parameter selection for improving EDM performance on external cylindrical surfaces of high-hardness steels.

Accurately determining the temperature during the printing process is crucial for additive manufacturing. Both excessively low and excessively high temperatures affect the quality of the printed product. To ensure product quality, the temperature during the printing process must be higher than the melting temperature of the printing material. This study determines the influence of printing process parameters on the temperature distribution during the printing of Ti6Al4V powder material using the Selective Laser Melting (SLM) method.

The most significant thing for the current team of scientists is not only specific mechanisms or policies, but also the strategic trust of the Party, the State, and the National Assembly.

Self-aligning elastic diaphragms are core components in the hydrostatic load balancing system of thrust bearings in large-capacity hydropower plants. During operation, these parts must withstand extremely high axial loads and hydraulic pressure, requiring their manufacturing materials to possess superior mechanical properties, high fatigue strength, and resistance to corrosion in hydraulic oil environments. The research focuses on analyzing and selecting materials; optimizing sheet metal forming technology using a multi-stage deep drawing process for austenitic stainless steel SUS 304; and simultaneously establishing a rigorous on-site static load testing procedure to validate product performance and reliability. The research results not only solve the problem of localizing a strategic component, reducing dependence on imported equipment, but also open up prospects for applying this technology to other hydropower plants, contributing to energy security and enhancing domestic precision mechanical engineering capabilities.

The paper presents a study on the current status of emissions and technical solutions aimed at upgrading flue gas treatment systems at thermal power plants belonging to Vietnam Electricity (EVN).

In the current period, to meet the country's socio-economic development program, many thermal power plants have been, are being, and will be newly invested and constructed.

In recent years, thanks to the promotion of scientific research, technology application and transfer, and innovation, the National Research Institute of Mechanical Engineering has become a leading scientific research and development organization in Vietnam in the fields of mechanical engineering, automation...

The scientific purpose of this study is to optimize 3 main parameters: hammer mass (m), hammer drop height (h) and filter chamber inlet dust concentration (η) of the Electrostatic precipitator (ESP), to meet the dust removal acceleration (a) and the hammer rapping force (F), ensure the working life span of the discharge electrode frames and the collecting electrode plates. This issue is evaluated as a multi-objective problem.