Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM
Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM
复制标题
DOI：
10.1016/j.measurement.2018.01.003
复制
发表时间：
2018-03
期刊：
影响因子：
5.6
通讯作者：
H. Majumder;K. Maity
中科院分区：
文献类型：
--
作者：
H. Majumder;K. Maity
形状记忆合金或智能合金具有独特的记忆原形状的能力，近年来在广泛的商业应用中引起了人们的广泛关注。为了准确预测和比较形状记忆合金镍钛合金表面粗糙度[算术平均粗糙度（Ra）、均方根粗糙度（Rq）和最大峰谷高度（Rz）]和显微硬度（MH）等几个重要的电火花线切割可加工性指标，建立了广义回归神经网络（GRNN）模型。实验以脉冲接通时间（TON）、放电电流(I)、进给丝量（WF）、丝张力（WT）和冲洗压力（FP） 5个关键加工参数作为加工输入。采用网格搜索方法最小化交叉验证误差。所开发的GRNN模型预测的响应误差在±10%以内，表明GRNN是预测线切割响应的有效策略。将模糊逻辑与基于比率分析的多目标优化相结合的多准则决策（MCDM）方法引入到不同相关响应的优化中。参数组合,吨= 12 µ年代,我 = 10 WT = 12 N, WS = 150 mm / s。和FP = 8 Bar，可以得到较好的结果。方差分析证明了该混合模型的有效性。进行了验证试验，验证了最佳工艺组合对线切割响应的改善。FESEM显微照片识别碎片块，微裂纹，麻坑和重铸层在加工表面。
The unique ability of shape memory alloy or smart alloy to memorize its previous form has drawn notable attention recently in a wide range of commercial applications. In efforts to precisely predict and compare few significant WEDM machinability aspects like surface roughness [arithmetic mean roughness (Ra), root mean square roughness (Rq) and maximum peak-to-valley height (Rz)] and micro-hardness (MH) of shape memory alloy nitinol, general regression neural network (GRNN) model was developed. Five critical machining parameter, namely pulse-on time (TON), discharge current (I), wire feed (WF), wire tension (WT) and flushing pressure (FP) were taken as machining input for the experiments. The grid search method was employed to minimize cross-validation error. The developed GRNN model predicted the responses within ±10% error indicating GRNN as a competent strategy to predict WEDM responses. A multi-criteria decision making (MCDM) approach, Fuzzy logic coupled with multi-objective optimization on the basis of ratio analysis (MOORA) is introduced to optimize different correlated responses. The parametric combination, TON= 12 µs, I = 10 A, WT = 12 N, WS = 150 mm/s. and FP = 8 Bar, were found to yield the preferred results. ANOVA test shows the efficiency of this hybrid MCDM model. Confirmation test has been done to validate the optimum process combination which demonstrates the improvement in WEDM responses. FESEM micrographs identifies lump of debris, micro cracks, pockmarks and recast layer in the machined surfaces.