@article{scholars20584,
          volume = {32},
         journal = {Journal of the Mechanical Behavior of Materials},
           title = {Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool},
            year = {2023},
            note = {Cited by: 5; All Open Access; Gold Open Access; Green Open Access},
       publisher = {Walter de Gruyter GmbH},
             doi = {10.1515/jmbm-2022-0300},
          number = {1},
          author = {Al-Zubaidi, Salah and Ghani, Jaharah A. and Haron, Che Hassan Che and Al-Tamimi, Adnan Naji Jameel and Mohammed, M. N. and Ruggiero, Alessandro and Sarhan, Samaher M. and Abdullah, Oday I. and Salleh, Mohd Shukor},
        abstract = {Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 {\^I}1/4m was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1-20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO-GSA algorithms. The hybrid BPNN-PSO-GSA model showed its superiority over the BPNN-PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN-PSO-GSA hybrid model was the 3-18-1 structure, which reached the best testing MSE of 3.8 {\~A}? 10-11 against 2.42 {\~A}? 10-5 of the 3-8-1 BPNN-PSO hybrid model. {\^A}{\copyright} 2023 the author(s), published by De Gruyter.},
            issn = {21910243},
             url = {https://www.scopus.com/pages/publications/85177181457?origin=resultslist}
}