This research investigation has been carried out in Computer Numerical Control (CNC) turning of 40–50 Hardness Rockwell C (HRC) hardened high chromium high carbon steel (HCHCR-D3) specimen for the findings of surface roughness (Ra) and the tool wear. The HCHCR-D3 steel, which has excellent abrasion and wear resistance, is machined with the physical vapor deposition (PVD) coated carbide (CNMG) turning insert nomenclature based on shape, clearance angle, tolerance and type of tool inserts. The coatings preferred are Titanium Nitrate (TiN), Aluminium Chromium Nitrate (AlCrN) and Latuma for the coating thickness of 3–4μm. The varying input parameters of speed and depth of cut under constant feed rate are used as machining parameters for this CNC turning operation. The machined surface characterization and tool wear have been investigated analytically in this manuscript along with the predicted results of effective stresses and temperatures under dynamic cutting conditions in Deform 3D can be related. The outcomes indicate that the depth of cut and the hardening effect (HRC) are the major influencing parameter on surface roughness. Less tool wear and machining time are obtained by the usage of coated CNMG tool insert for high-speed cutting conditions which results in minimization of wear interruption and growth in surface improvements.
Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO 2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)-particle swarm optimization (PSO) algorithm and artificial neural network (ANN)-genetic algorithm (GA) to optimize the associated multi-response characteristics during CO 2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN-GA and ANN-PSO model is an efficient tool for the optimization of process parameters in CO 2 laser cutting of difficult-tocut material-aluminium. From the optimization results, it can be concluded that the proposed ANN-GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.
In developing countries like India there is a huge gap between power generation and demand where load shedding becomes necessary to sustain system stability. Conventional load shedding methods follow "round robin" technique wherein it is almost impossible to shed exact amount of load. And also shedding is carried out regardless of the type of loads connected to a feeder as a result of which available power is not delivered to the consumers in utmost need. This paper demonstrates Intelligent load shedding scheme to provide optimal solution for load relief based on time priority assigned to various loads. The genetic algorithm technique is employed for optimal load shedding solution to minimize the error between load to be shed and load being shed in a smart grid environment. Simulations are carried on a sample system based on a practical feeder data.
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