Makine Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/263

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Author: search.filters.author.Akar, SametSubject: search.filters.subject.Ann

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Prediction of the Onset of Shear Localization Based on Machine Learning
    (Cambridge Univ Press, 2023) Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk; Akar, Samet; Ayll, Ece
    Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 7
    A Study on the Μwire-Edm of Ni55.8ti Shape Memory Superalloy: an Experimental Investigation and a Hybrid Ann/Pso Approach for Optimization
    (Springer Heidelberg, 2023) Seyedzavvar, Mirsadegh; Boga, Cem; Akar, Samet
    The unique properties of high hardness, toughness, strain hardening, and development of strain-induced martensite of nickel-titanium superalloys made the micro-wire electro discharge machining (mu wire-EDM) process one of the main practical options to cut such alloys in micro-scale. This paper presents the results of a comprehensive study to address the response variables of Ni55.8Ti superalloy in mu wire-EDM process, including the kerf width (KW), material removal rate (MRR), arithmetic mean surface roughness (R-a) and white layer thickness (WLT). To this aim, the effects of pulse on-time (T-on), pulse off-time (T-off), discharge current (I-d) and servo voltage (SV) as input parameters were investigated using the experiments conducted based on Taguchi L-27 orthogonal array. The results were employed in the analysis of variance (ANOVA) to examine the significance of input parameters and their interactions with the output variables. An optimization approach was adopted based on a hybrid neural network/particle swarm optimization (ANN/PSO) technique. The ANN was employed to achieve the models representing the correlation between the input parameters and output variables of the mu wire-EDM process. The weight and bias factor matrices were obtained by ANN in MATLAB and together with the feed forward/backpropagation model and developed functions based on PSO methodology were used to optimize the input parameters to achieve the minimum quantities of KW, R-a and WLT and the maximum value of MRR, individually and in an accumulative approach. The results represented a maximum accumulative error of nearly 8% that indicated the precision of the developed model and the reliability of the optimization approach. At the optimized level of input parameters obtained through the accumulative optimization approach, the KW, R-a, and WLT remained nearly intact as compared with the levels of responses obtained in the individual optimization approach, while there was a sacrifice in the machining efficiency and reduction in the MRR in the mu wire-EDM process of Nitinol superalloy.