Modeling of Stress Concentration Factor Using Artificial Neural Networks for a Flat Tension Bar with Opposite V-Shaped Notches

Mehmet Eren; İhsan Toktaş; Murat Tolga Özkan

doi:10.2339/politeknik.1275466

Research Article

Simetrik V Şekilli Plakadaki Gerilme Yığılma Faktörünün Yapay Sinir Ağı ile Modellenmesi

Year 2023, Volume: 26 Issue: 3, 1199 - 1205, 01.10.2023

Mehmet Eren İhsan Toktaş Murat Tolga Özkan

https://doi.org/10.2339/politeknik.1275466

Abstract

Machine parts are exposed to stress accumulation due to geometric differences. Determining the stress accumulation locations is crucial to the design procedures. Studies on stress concentrations have been conducted in the past using a variety of theoretical and experimental methodologies, and distinct interpretations have been offered depending on the geometry of the machine part to be produced. The ability to complete activities with the least amount of effort and in the shortest amount of time has emerged as a result of the new computer technologies and software that have impacted many aspects of our everyday lives. One of these methods is the artificial neural networks (ANN) model, which is a branch of artificial intelligence. It is argued as a thesis in this study that fast and low-cost solutions can be found to problems in the field of solid mechanics by using the ANN model. For this purpose, a model has been developed to determine the SCF value with the ANN model of a plate with symmetrical V-shaped notch. The graphs obtained from previous experimental studies were converted to digital format and the Kt values obtained for the V-shaped notch problem with different parameters were converted into a data file. In this file, the SCF values to be obtained according to the strength upper limit safety factor value of the machine part, depending on the dimensional dimensions and material type required for the design, are calculated numerically in the form of an Excel file. An ANN-based code was created in MATLAB software and a new solution method was presented for parts containing a V-shaped notch.

Keywords

Stress concentration factor (scf), artificial neural network (ann), opposite v-shaped notches, stress-strain analysis, computational methods

Supporting Institution

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Project Number

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Thanks

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