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Adaptive control optimisation system for minimising production cost in hard milling operations

机译:自适应控制优化系统,可将硬铣削生产中的生产成本降至最低

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摘要

This paper proposes an adaptive control with optimisation (ACO) methodology for optimising the production cost subjected to quality constraints in high-performance milling operations of hardened steel (58-62 HRC). Unlike traditional approaches for optimising production cost, this paper deals with optimising the cutting operation considering the current state of the cutting tool. Artificial intelligence techniques for modelling (artificial neural networks) and optimising (genetic algorithms and mesh adaptive direct search algorithms) are applied for this purpose. As a result, the production cost estimation from the proposed approach is 13% lower than the one obtained by the traditional approach with 76% less uncertainty.
机译:本文提出了一种带有优化的自适应控制(ACO)方法,用于在淬硬钢(58-62 HRC)的高性能铣削操作中,根据质量约束优化生产成本。与优化生产成本的传统方法不同,本文考虑了切削刀具的当前状态来优化切削操作。为此,应用了用于建模(人工神经网络)和优化(遗传算法和网格自适应直接搜索算法)的人工智能技术。结果,所提出方法的生产成本估算比传统方法所获得的估算成本低13%,不确定性降低了76%。

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