用于弹药推进执行器的Al 5083 H116合金表面粗糙度和附着力的数学建模和多响应优化

IF 1.7 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Multidiscipline Modeling in Materials and Structures Pub Date : 2023-02-16 DOI:10.1108/mmms-11-2022-0237
H. Gökçe, M. A. Biberci
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引用次数: 0

摘要

目的本研究旨在获得最低的表面粗糙度(Ra)和钻头附着力值(AV),这取决于在Al 5083 H116合金钻孔过程中控制因素(切削速度Vc、进给速率f和钻头D)的变化。低粗糙度值会增加最终零件的疲劳强度,并影响摩擦学性能,如润滑和摩擦。在加工韧性材料时,AV会增加Ra值,并对刀具寿命产生负面影响。设计/方法/方法使用田口L16正交阵列进行钻孔试验。利用灰色关联分析(GRA)、响应面法(RSM)和人工神经网络(ANN)对Ra和AV的实验测量结果进行调整,以生成预测值。扫描电镜检测了钻头尖端的粘附和切屑形态,并用EDX对其进行了分析。发现Ra和AV随着f的增加而增加。Vc影响AV;Ra的86.04%f和AV的54.71%Vc是最有效的控制参数。在使用GRA优化Ra和AV之后,f是最有效的控制因素。Vc:120m/min、f:0.025mm/rev和D2是最佳的。人工神经网络预测准确率分别为Ra99.6%和AV99.8%。用RSM得到了数学模型。f的增加增加了AV,这对Ra有负面影响,而Vc的增加降低了粘附趋势。对于具有最高槽长度的D1钻头,测量到相对较低的Ra,因为这有利于排屑。此外,所获得的数学模型的高相关性表明,这些模型可以安全地使用。独创性/价值本研究的新颖性在于用GRA和ANN确定最佳钻孔参数,以便为弹药翼推进系统的组装钻孔,特别是用Al 5083 H116合金生产的带有铆钉和螺栓的弹药翼推进设备。
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Mathematical modeling and multiresponse optimization to reduce surface roughness and adhesion in Al 5083 H116 alloys used in ammunition propulsion actuators
PurposeThis study aims to obtain the lowest surface roughness (Ra) and drill bit adhesion values (AV) depending on the change in control factors (cutting speed-Vc, feed rate-f and drill bit-D) during drilling of the Al 5083 H116 alloy. Low roughness values increase the fatigue strength of the final part and affect tribological properties such as lubrication and friction. In the machining of ductile materials, the AV increases the Ra value and negatively affects the tool life.Design/methodology/approachDrilling tests were conducted using Taguchi L16 orthogonal array. The experimental measurement findings for Ra and AV were adjusted utilizing the Grey Relational Analysis (GRA), the Response Surface Method (RSM) and Artificial Neural Networks (ANN) to generate prediction values. SEM detected drill-tip adhesions and chip morphology and they were analyzed by EDX.FindingsRa and AV increased as the f increased. Vc affects AV; 86.04% f on Ra and 54.71% Vc on AV were the most effective control parameters. After optimizing Ra and AV using GRA, the f is the most effective control factor. Vc: 120 m/min, f: 0.025 mm/rev and D2 were optimal. ANN predicted with Ra 99.6% and AV 99.8% accurately. Mathematical models are obtained with RSM. The increase in f increased AV, which had a negative effect on Ra, whereas the increase in Vc decreased the adhesion tendency. With the D1 drill bit with the highest flute length, a relatively lower Ra was measured, as it facilitates chip evacuation. In addition, the high correlations of the mathematical models obtained indicate that the models can be used safely.Originality/valueThe novelty of this study is to determine the optimum drilling parameters with GRA and ANN for drilling the necessary holes for the assembly of ammunition wing propulsion systems, especially those produced with Al 5083 H116 alloy, with rivets and bolts.
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来源期刊
CiteScore
3.70
自引率
5.00%
发文量
60
期刊介绍: Multidiscipline Modeling in Materials and Structures is published by Emerald Group Publishing Limited from 2010
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