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Research on Cutting Force predictive model of Austempered Ductile Iron based on Particle Swarm Optimization algorithm

机译:基于粒子群优化算法的奥氏体延性铁切削力预测模型研究

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Compared with traditional linear regression algorithm, the Particle Swarm Optimization (PSO) has excellent performance: high precision, rapid convergence, operation and realization easily. This paper introduces the basic principle of PSO, and build the cutting force model of Austempered Ductile Iron with PSO. Results shows that: the mathematical model with PSO had higher predictive precision and stability than the linear regression algorithm and other optimization. PSO is practical in solving complicated prediction model and can avoid constraint of solution effectively. PSO can also be widely used in many aspects such as tool wear, heat in metal cutting with further improving.
机译:与传统的线性回归算法相比,粒子群优化(PSO)具有优异的性能:高精度,迅速收敛,操作和实现。本文介绍了PSO的基本原理,并用PSO构建了奥斯特型球墨铸铁的切割力模型。结果表明:具有PSO的数学模型具有比线性回归算法和其他优化更高的预测精度和稳定性。 PSO在解决复杂的预测模型方面是实用的,可以有效地避免对解决方案的限制。 PSO也可以广泛应用于诸如刀具磨损等许多方面,在金属切割中的热量进一步改善。

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