Multi-Response Optimization in Drilling of MWCNTs Reinforced GFRP Using Grey Relational Analysis

The present work concentrates on the use of Grey Relational Analysis for optimizing the drilling parameters like weight percentage of multi-wall carbon-nanotube (MWCNTs), cutting speed and feed rate on the thrust force and the delamination factor in the drilling of GFRP composites. Full factorial design is utilized for the trial. Analysis of variance (ANOVA) is applied to determine the significance of drilling parameters on multi-response. Considering the multi-response optimization results, which are acquired from the largest Grey Relational Grade (GRG), it is determined that optimal parameters are 1 wt. % MWCNTs, cutting speed 25 m/min, and feed rate 0.10 mm/rev to minimize concurrently thrust force and delamination factor. It is provided that the percentage development in GRG with the multi-response optimization is 50.53%. It is clearly indicated that the quality characteristics are crucially developed using this approach in the drilling of GFRP. According to the results of ANOVA of the GRG, the crucial factor is feed rate. Validation experiment was confirmed by computing the confidence level within the interval width. Eventually, results of validation experiment with the optimum drilling conditions settings have indicated that the proposed model develops overall performance of drilling process

Optimization of machining parameters in face milling using multi-objective Taguchi technique

In this research, the effect of machining parameters on the various surface roughness characteristics (arithmetic average roughness (Ra), root mean square average roughness (Rq) and average maximum height of the profile (Rz)) in the milling of AISI 4140 steel were experimentally investigated. Depth of cut, feed rate, cutting speed and the number of insert were considered as control factors; Ra, Rz and Rq were considered as response factors. Experiments were designed considering Taguchi L9 orthogonal array. Multi signal-to-noise ratio was calculated for the response variables simultaneously. Analysis of variance was conducted to detect the significance of control factors on responses. Moreover, the percent contributions of the control factors on the surface roughness were obtained to be the number of insert (71.89 %), feed (19.74 %), cutting speed (5.08%) and depth of cut (3.29 %). Minimum surface roughness values for Ra, Rz and Rq were obtained at 325 m/min cutting speed, 0.08 mm/rev feed rate, 1 number of insert and 1 mm depth of cut by using multi-objective Taguchi technique

Modeling and optimization of face milling process parameters for AISI 4140 steel

In this study, the effect of cutting parameters such as the depth of cut, feed rate, cutting speed and the number of inserts on surface roughness were investigated in the milling of the AISI 4140 steel. The optimal control factors for surface quality were detected by using the Taguchi technique. Experimental trials were designed according to the Taguchi L18 (2¹x3³) orthogonal array. The statistical effects of control factors on surface roughness have been established by using the analysis of variance (ANOVA). Optimal cutting parameters were obtained by using the S/N ratio values. The ANOVA results showed that the effective factors were the number of inserts and the feed rate on surface roughness. However, the depth of cut and the cutting speed showed an insignificant effect. Additionally, the First-order and Second-order regression analysis were conducted to estimate the performance characteristics of the experiment. The acquired regression equation results matched with the surface roughness measurement results. The optimal performance characteristics were obtained as a 0.5 mm depth of cut, 0.08 mm/rev feed rate, 325 m/min cutting speed and 1 number of inserts by using the Taguchi method. Additionally, the confirmation test results indicated that the Taguchi method was very prosperous in the optimization of the machining parameters to obtain the minimum surface roughness in the milling of the AISI 4140 steel

316L Paslanmaz Çeliklerin Frezeleme işlemindeki Yüzey Pürüzlülüğün ANFIS ile Modellenmesi

Paslanmaz çelikler, mükemmel korozyon direnci, düşük ve yüksek sıcaklıklarda kullanılabilmesi, kolay şekillendirilebilmesi ve iyi estetik görünüme sahip olmasından dolayı birçok alanda kullanılabilen bir malzemedir. Bu çalışmada, 316L paslanmaz çeliğin yüzey pürüzlülüğü kesme parametrelerine bağlı olarak adaptif ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) yaklaşımı kullanılarak bir model geliştirilmiştir. Kesme parametreleri olarak kesme hızı, ilerleme, kesme derinliği ve kesme genişliği seçilmiştir. Matlab 8.5 programının ANFIS editörü kullanılarak ANFIS modellemesi gerçekleştirilmiştir. Geliştirilen ANFIS modelinin tahmin sonuçları ile deneysel sonuçlar karşılaştırıldığında en büyük yüzde hata değerinin 9,58 ve ortalama yüzde hata değerinin 5,25 olduğu tespit edilmiştir. ANFIS modelinin korelasyon katsayısı 0,997 olarak bulunmuştur. Sonuçlar, ANFIS modelinin 316L paslanmaz çeliğin frezeleme işleminde yüzey pürüzlülüğün tahmin edilmesinde etkin bir yöntem olabileceğini göstermiştir