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Optimization of Machining Parameters for Improving Energy Efficiency using Integrated Response Surface Methodology and Genetic Algorithm Approach

机译:用综合响应面方法和遗传算法方法优化提高能效的加工参数

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Machine tools consume enormous amount of energy during machining, build-up to machining, post machining and idling condition to drive motors and auxiliary equipments in the manufacturing system. Reduction of energy consumption during the machining phase is extremely important to improve the environmental performance over the entire life cycle. This paper presents a predictive and optimization model based on integrated response surface methodology and genetic algorithm approach to predict the energy consumption and the corresponding machining parameters during the turning of AISI 1045 steel with a tungsten carbide tool. Experiments using Taguchi design are performed to develop the predictive model. The developed predictive model is used to formulate the objective function for genetic algorithm. The confirmation experiments are performed to validate the developed model and the results are found within 4% error. The statistical significance of the developed model has been tested by the analysis of variance test. This research will be beneficial for a number of manufacturing industries for selection of machine tools on the basis of energy consumption. The reduction of peak load through optimization will results in lowering the energy consumption of the machine tools during non-cutting time.
机译:机床在加工过程中消耗巨大的能量,积聚到加工,后置加工和空转条件,以驱动制造系统中的电动机和辅助设备。在加工阶段期间降低能耗对整个生命周期来改善环境性能非常重要。本文介绍了基于集成响应面方法和遗传算法方法的预测和优化模型,以预测钨碳化物工具在AISI 1045钢转动期间能耗和相应加工参数。使用Taguchi设计进行实验来开发预测模型。开发的预测模型用于制定遗传算法的目标函数。进行确认实验以验证开发的模型,结果在4%误差范围内发现。通过差异试验的分析测试了开发模型的统计学意义。这项研究将有利于许多制造业,以便在能耗的基础上选择机床。通过优化的峰值负荷的降低将导致在非切割时间期间降低机床的能量消耗。

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