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A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process
TLDR
An improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed for optimizing the metal mines production process by maximizing economic and resource benefits. Expand
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Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model
TLDR
This paper proposes a multi-objective optimization model of the production process of metal mines considering both economic benefits and resource efficiency. Expand
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A neural network expert system for evaluating the mining conditions of multiple mineral deposits of Gushan Mine
There are usually multiple mineral deposits in one mineral area, so comprehensive exploitation is very important. Based on the interpretation of the working mechanism of neural network expert system,Expand
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Analysis on the effect of phase boundary in meso-cutting of AISI1045 based on the microstructure-level simulation
In this paper, in order to discover the mechanism of chip formation in meso-cutting, an equivalent homogenous material (EHM) model and a microstructure-level model based on the real microstructureExpand
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Comparative investigation on microstructure-based modelling for the orthogonal cutting of AISI1045
With the feed rate decreasing to the dimension of grain size and tool edge radius, cutting process is often carried out in the grain interior and grain boundary. In this paper, the orthogonal cuttingExpand
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Multi-Objective Optimization for Metal Mine Production Technical Indicators with NSGA-II and ANN Algorithms
TLDR
This paper proposes a ‘multi-objective optimization model’ based on a “fast and elitist Non-dominated Sorting Genetic Algorithm” (NSGA-II) and ‘Artificial Neural Networks’ for the optimization of production technical indicators in the entire geology, mining and beneficiation metal mine production processes. Expand