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Adaptive Control Optimization in Micro-Machining of Hardened Steels

机译：硬化钢微型加工的自适应控制优化

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摘要

Nowadays, the miniaturization of many consumer products is extending the use of micro-machining operations in conventional shop floors with high quality requirements. However, the impact of cutting-tool wear on part dimensions, form and surface integrity is not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear and adapt the cutting conditions accordingly to minimize the production cost ensuring part specifications. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with an evolutionary optimization algorithm; and iv) a CNC interface module. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.
机译：如今，许多消费产品的小型化正在以高质量要求在传统的商店地板中扩展微加工操作。然而，切割工具磨损对部件尺寸，形式和表面完整性的影响并不可忽略不可忽略，并且最低生产成本的部分质量保证是一个具有挑战性的任务。事实上，工业实践通常设置保守的切割参数和早期切割更换政策，以最大限度地减少切削刀具磨损对部件质量的影响。虽然这些实践可能会确保零件完整性，但距离生产成本很远。本文提出了一种自适应控制优化（ACO）系统来估计切削刀具磨损并相应地调整切割条件，以最小化生产成本确保部件规格。 ACO系统基于：i）由测力计组成的监控传感器系统; ii）具有人工神经网络模型的估计模块; iii）具有进化优化算法的优化模块;和iv）CNC接口模块。此外，与保守和高生产策略分别，最终ACO系统的实施报告的生产成本为12.3％和29％。

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