Research on Parametric Model for Surface Processing Prediction of Aero-Engine Blades

This paper presented a method for establishing a blade surface machining prediction model based on a parametric model. The abrasive grain state of the grinding tool was divided into initial wear stage, stable wear stage and sharp wear stage. Based on this, a parametric prediction model of engine blade surface material removal was established. In this paper, the simulation of blade surface machining was carried out. In this work, the blade was divided into several sections according to the direction from the blade root to the blade tip. A certain curve of the outer contour was fitted with a specific arc to reduce the calculation amount. Through a series of simulation calculations, the expressions of the above parametric prediction model were obtained, and several experiments were carried out to verify the feasibility of the prediction model, and the results were analyzed

Recent advances and perspectives for Zn-based batteries: Zn anode and electrolyte

Zn-based batteries have attracted extensive attention due to their high theoretical energy density, safety, abundant resources, environmental friendliness, and low cost. They are a new energy storage and conversion technology with significant development potential and have been widely used in renewable energy and portable electronic devices. Considerable attempts have been devoted to improving the performance of Zn-based batteries. Specifically, battery cycle life and energy efficiency can be improved by electrolyte modification and the construction of highly efficient rechargeable Zn anodes. This review compiles the progress of the research related to Zn anodes and electrolytes, especially in the last five years. This review will introduce fundamental concepts, summarize recent development, and inspire further systematic research for high-performance Zn-based batteries in the future

Research on Abrasive Belt Based Machining Technology for Milling Shaped Aero-Engine Blades

This paper proposes an automatic surface treatment method for CNC milled blades. The surface texture characteristics of the blade after milling were analyzed. The contact area between the contact wheel and the blade surface was analyzed theoretically, and then the contact wheel and the blade edge were analyzed. The contact area between the intake side and the exhaust side was theoretically analyzed, and one micro-element in the contact area was selected, and the simulation analysis under multiple sets of parameters was carried out. In the experimental aspect, this paper has assembled a set of relatively reliable experimental systems, and carried out an in-depth analysis of a certain area of the engine blade obtained from the experiment to verify the feasibility and reliability of the proposed method

Fusion Recalibration Method for Addressing Multiplicative and Additive Effects and Peak Shifts in Analytical Chemistry

Analytical chemistry relies on the qualitative and quantitative analysis of multivariate data obtained from various measurement techniques. However, artifacts such as missing data, noise, multiplicative and additive effects, and peak shifts can adversely affect the accuracy of chemical measurements. To ensure the validity and accuracy of results, it is crucial to preprocess the data and correct for these artifacts. This paper proposes a fusion recalibration algorithm, called Spectral Offset Recalibration (SOR), that combines the Extended Multiplicative Signal Correction (EMSC) and Correlation-Optimized Warping (COW) algorithms to address both multiplicative and additive effects and peak shifts. The algorithm incorporates prior spectroscopic knowledge to down-weight or disregard spectral regions with strong absorption or significant distortion caused by peak alignment algorithms. Experimental validation on wood NIR datasets and simulated datasets demonstrates the effectiveness of the proposed method. The fusion recalibration approach offers a comprehensive solution for accurate analyses and predictions in analytical chemistry by mitigating the impact of artifacts