首页|期刊导航|中国机械工程|C60微量润滑铣削GH4169切削力预测及工艺参数优化

C60微量润滑铣削GH4169切削力预测及工艺参数优化OA

Prediction of Cutting Forces and Processing Parameter Optimization in C60 Minimum Quantity Lubrication Milling of GH4169

中文摘要英文摘要

镍基高温合金在航空航天等领域应用广泛,但加工时刀具磨损严重、切削力大且效率低,传统润滑技术难以改善切削性能.针对上述问题,引入C60纳米流体切削液改善切削区润滑条件,降低刀-工界面摩擦并抑制热积累.构建考虑纳米流体摩擦特性和冷却效果的切削力建模方法,结合斜角切削理论、镜像热源法与Johnson-Cook本构方程计算切削力.铣削实验结果显示,该模型能量化纳米流体润滑下摩擦与冷却的协同作用,预测切削力平均误差为6.73%,纳米流体润滑下切削合力峰值较常规切削液降低19.6%.进一步提出基于Pareto最优解的粒子群优化算法多目标优化策略,构建加工效率与切削力的多目标评价体系,优化工艺参数后,切削合力减小了 5.7%,加工效率提高了 36.33%.

Nickel-based superalloys were widely used in aerospace and other fields.However,signifi-cant challenges were in machining,including severe tool wear,poor surface integrity,and low processing efficiency,which could not be effectively mitigated by conventional lubrication techniques.To address these issues,C60 nanofluid cutting fluid was introduced to enhance lubrication at the tool-workpiece inter-faces,thereby reducing friction and suppressing heat accumulation.A novel cutting force modeling ap-proach was developed,incorporating the tribological properties and cooling effects of nanofluids.The model integrated oblique cutting theory,mirror heat source method,and Johnson-Cook constitutive equa-tion to calculate cutting forces.Experimental results demonstrate that the proposed model accurately quanti-fies the synergistic effects of friction reduction and cooling enhancement under nanofluid lubrication,achiev-ing an average prediction error of 6.73%for cutting forces.Notably,the cutting force peak is reduced by 19.6%under nanofluid lubrication compared to conventional cutting fluids.Furthermore,a multi-objective optimization strategy was proposed based on Pareto optimality and PSO.A comprehensive evalu-ation system was established considering machining efficiency and cutting forces.Optimization results show that the cutting force decreases by 5.7%,while machining efficiency increases by 36.33%after parameter optimization.

潘志榕;孙浩;姚斌;蔡志钦;蓝启鑫;张金辉

厦门大学航空航天学院,厦门,361005中国航发哈尔滨东安发动机有限公司,哈尔滨,150060厦门大学航空航天学院,厦门,361005厦门大学航空航天学院,厦门,361005厦门大学航空航天学院,厦门,361005厦门大学航空航天学院,厦门,361005

矿业与冶金

C60纳米流体切削力预测粒子群优化

C60nanofluidcutting force predictionparticle swarm optimization(PSO)

《中国机械工程》 2026 (7)

1562-1571,10

国家自然科学基金(52475070)中国航空发动机集团产学研合作项目(TC240Y010)

10.3969/j.issn.1004-132X.2026.07.004

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