## Theoretical analysis and mathematical modeling of deformation and stresses of the grooving tool

NEURAL COMPUTING & APPLICATIONS, vol.32, no.14, pp.10481-10500, 2020 (SCI-Expanded)

• Publication Type: Article / Article
• Volume: 32 Issue: 14
• Publication Date: 2020
• Doi Number: 10.1007/s00521-019-04588-w
• Journal Name: NEURAL COMPUTING & APPLICATIONS
• Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
• Page Numbers: pp.10481-10500
• Keywords: Machining, Grooving tool, Cutting parameters, Finite element method, Artificial neural networks
• Gazi University Affiliated: Yes

#### Abstract

Machining operations involving complex multivariate parameters are defined by many machining parameters. Correct selection of these parameters is crucial for an efficient and economical cutting operation. The grooving operation required in many cases is one of the most problematic methods in all metal cutting operations, especially in terms of chip control. This paper covers theoretical analysis and mathematical modeling of deformation and stresses of the grooving tool. Cutting forces affecting the service life of the grooving tool were measured by various cutting experiments. Deformation and stresses of grooving tool caused by cutting forces were analyzed by finite element method using Ansys software. In modeling with artificial neural networks (ANN), grooving insert width, cutting speed, feed rate, radial force and primary cutting force are inputs in the model and deformation and stresses of the grooving tool are outputs. An algorithm, which is a Matlab script file, was developed to determine the optimal combination of neural network parameters such as the normalization method, number of hidden neurons, transfer function and training algorithm. The best-fitting set determined by the algorithm developed for the model was achieved with the Levenberg-Marquardt backpropagation algorithm, logistic sigmoid transfer function, nine hidden neurons and normalization method with a scaling factor. The MSE, R-2, MAPE values of the ANN model are 2.0327 x 10(-6), 0.999992 and 0.379227, respectively. Performance results have shown that the proposed approach can also be used for ANN modeling of machining parameters in other cutting operations other than grooving.