Optimization of machining parameters considering minimum cutting fluid consumption
Optimization of machining parameters considering minimum cutting fluid consumption
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DOI：
10.1016/j.jclepro.2015.06.007
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发表时间：
2015-12
影响因子：
11.1
通讯作者：
Zhigang Jiang;Fan Zhou;Hua Zhang;Yan Wang;J. Sutherland
中科院分区：
文献类型：
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作者：
Zhigang Jiang;Fan Zhou;Hua Zhang;Yan Wang;J. Sutherland
干式或近干式加工通常被认为是减少切削过程对生态影响的有效策略。然而，由于干式或近干式加工的应用限制，通过加工参数优化减少切削液供应提供了一种具有成本效益的替代方案。为此，提出了考虑最小切削液消耗和成本的加工参数优化模型。优化模型将加工成本和切削液消耗作为两个目标，这两个目标受切削深度、进给速度、切削速度和切削液流量四个变量的影响。在模型中，工艺成本包括生产操作成本和刀具成本，而加工过程的切削液消耗，包括可重复使用的切削液和不可重复使用的切削液，即。残留在工件和切屑上的切削液以及扩散到环境中的切削液。采用Matlab 7编写的混合遗传算法求解多目标优化问题。通过实例研究验证了多目标优化模型的有效性，仿真结果表明，与未优化相比，流体消耗降低了17%。这表明所提出的优化方法是有效的，具有很大的应用潜力。
Dry or near dry machining is often regarded as an effective strategy for reducing ecological impacts of the cutting processes. However, due to the application limitations of dry or near dry machining, reduction of cutting fluid supply through machining parameter optimization offers a cost effective alternative. To this end, an optimization model of machining parameters considering minimum cutting fluid consumption and cost is proposed. Process cost and cutting fluid consumption are treated as the two objectives in the optimization model, which are affected by four variables, namely cutting depth, feed rate, cutting speed, and cutting fluid flow. In the model, process cost includes production operation cost and cutting tool cost, whilst cutting fluid consumption by a machining process, which consists of reusable cutting fluid and non-reusable cutting fluid, ie., the remaining cutting fluid deposited on the workpiece and chips as well as that diffused into the environment. The multi-objective optimization problem is solved by a hybrid genetic algorithm programmed in Matlab 7. An illustrative case study was implemented to verify the effectiveness of the multi-objective optimization model, and the simulation results showed 17% reduction of fluid consumption compared to that without optimization. This indicates that the proposed optimization is effective and has great potential to be adopted by industry.