Multiobjective Optimization of Tool Geometric Parameters Using Genetic Algorithm

Tool geometric parameters have a huge impact on tool wear. Up to now, there are only a few researches on tool geometric parameters and optimization, and the single objective function of parameter optimization used by researchers during high-speed machining (HSM) mainly is the minimum cutting force. However, the elevated cutting temperature also greatly affects tool wear due to the numerous cutting heat generation. Thus, to reduce tool wear, it is the most fundamental approach to taking into account the comprehensive control of the cutting force and cutting temperature because they are the two most important physical quantities in metal cutting processes. This work proposes a new optimization idea of the cutting-tool’s multi geometric parameters (three main parameters: rake angle, clearance angle, and cutting edge radius) with two objective functions (the cutting force and the temperature). Based on the response surface method (RSM), we have established the modified functional relation models of the influence of tool geometric parameters on the cutting force and temperature according to the finite element simulation results in high-speed cutting of Ti6Al4V. Then the models are solved by using a genetic algorithm, and the optimal tool geometric parameters values that can concurrently control the two objectives in their minimum values are obtained. The advantages lie in the strategy of the separate models of the cutting force and cutting temperature owing to their different dimensions and the solution of the models through giving the cutting force and cutting temperature different weight coefficients. The optimal results are verified by experiments, which shows that the optimal tool geometric parameters are very effective and vital for ensuring both the cutting force and the cutting temperature not too high. This work is of great significance to the cutting tool design theory and its manufacturing for reducing tool wear

A new method for the synthesis of dry diazonium nitrates in nonaqueous condition

1531-1534Amines react with PEG-NO2 adduct in nonaqueous condition to give dry arenediazonium nitrates in high purity and high yield

Structural and Electronic Engineering of Co-doped Ni3C Nanoparticles Encapsulated in Ultrathin Carbon Layers for Hydrogen Evolution Reaction

With resurgent interest in green hydrogen as a key element in the transition to a renewable-energy economy, developing efficient, earth-abundant, and low-cost catalysts for hydrogen evolution reaction (HER) is becoming increasingly important but is still very challenging. Herein, we report the synthesis of Co-doped Ni3C nanoparticles encapsulated in ultrathin carbon layers (CNCC) by in-situ thermal decomposition of organic-inorganic hybrid as high-performance HER electrocatalysts. Experimental and density functional theory studies evidence that the substantial high-index (113) surfaces in synergy with a few atomic carbon layers contribute significantly to the activity and stability, while the electronic structure of Ni3C is optimized through tuning the Co content to enhance the intrinsic kinetics for HER. The CNCC exhibits excellent HER activities with overpotentials at 10 mA cm−2 (η10) of 102 and 69 mV and Tafel slopes of 74 and 43 mV dec−1 in respective neutral and alkaline media along with a superior stability without noticeable decay up to 100 h. More importantly, the CNCC outperforms the benchmark Pt/C catalyst under high current density (>38 mA cm−2) in an alkaline electrolyte, showing great potential for practical hydrogen production. © 2023 Wiley-VCH GmbH.FALS