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.

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.

ÖZET 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. ABSTRACTStainless steel is a material that can be used in many areas because it resists corrosion excellently, can be used at low and high temperatures, is easy to shape and has a pleasant aestheticappearance. In this study, a model has been developed using the adaptive network based fuzzy logic inference system (ANFIS) approach based on the surface roughness cutting parameters of 316L stainless steel. Cutting speed, feed, cutting depth and cutting width are selected as cutting parameters. ANFIS modeling was performed using the ANFIS editor of the Matlab 8.5 program. When the experimental values were compared with the predicted values of the developed ANFIS model, it was found that the maximum percentage error value was 9.58 and the average percentage error value was 5.25. The correlation coefficient of the ANFIS model was 0.997. The results showed that ANFIS can be an effective method for estimating surface roughness in 316L stainless steel milling process.

The aim of this study is to develop a novel decision support system, which has never been developed yet, in order to optimize machining parameters. We combine the three distinct methods: experimental design and analysis, fuzzy data envelopment analysis (DEA) and fuzzy analytical hierarchy process (AHP). Firstly, a full factorial experiment including four factors and three levels is carried out. We take into account cutting speed, feed rate, depth of cut and number of cutting tool inserts as factors. The following three outputs are selected: Material Removal Rate, Machining Time and Surface Roughness. Secondly, a total of 23 experiments are determined as efficient decision-making units using fuzzy DEA with super efficiency method. Finally, a fuzzy AHP approach is conducted to rank the efficient experiments among each other. In conclusion, the results show that the Fuzzy DEA-Fuzzy AHP and the Fuzzy DEA with Super Efficiency generate clearly different rankings of experiments and Fuzzy DEA-Fuzzy AHP Approach has outperformed Fuzzy DEA with Super Efficiency Approach. The results highlight the importance of taking into account the expert opinions in the decision-making processes.

Cam Elyaf Takviyeli Polimer (CETP) kompozitler, diğer malzemelere göre daha üstün özelliklere sahip olmasından dolayı birçok mühendislik uygulamalarında kullanılmaktadır. Bu kompozitlerin montajında delme işlemi yaygın olarak uygulanmaktadır. CETP malzemelerin delinmesinde; yüksek delik yüzey kalitesi ile minimum deformasyon ve itme kuvveti için delme parametrelerinin belirlenmesi oldukça önemlidir. Bu yüzden, delme işlemi sırasında oluşan delik yüzeyi hasarını en aza indirmek için optimum delme koşulları belirlenmelidir. Bu çalışmada, delme işleminde itme kuvveti Taguchi Metodu kullanılarak optimize edilmiştir. Ayrıca, itme kuvvetinin tahminine yönelik matematiksel modeller geliştirilmiştir. Delme parametrelerinin itme kuvveti üzerindeki etki oranları varyans analizi ile belirlenmiştir. Varyans analizine göre itme kuvveti üzerindeki en etkili parametrenin ilerleme olduğu görülmüştür. İtme kuvvetini tahmin etmek için Taguchi Metodu, birinci ve ikinci dereceden regresyon modelleri kullanılmıştır. Elde edilen sonuçlar ile deney sonuçları karşılaştırılmıştır. Ayrıca, üç boyutlu grafikler incelendiğinde, % çok duvarlı karbon nanotüp oranı ve kesme hızı arttıkça itme kuvvetinin azaldığını ve ilerleme arttıkça itme kuvvetinin arttığını göstermiştir.