<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool"^^ . "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 μm 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 � 10-11 against 2.42 � 10-5 of the 3-8-1 BPNN-PSO hybrid model. © 2023 the author(s), published by De Gruyter."^^ . "2023" . . "32" . "1" . . "Walter de Gruyter GmbH"^^ . . . "Journal of the Mechanical Behavior of Materials"^^ . . . "21910243" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Mohd Shukor"^^ . "Salleh"^^ . "Mohd Shukor Salleh"^^ . . "Salah"^^ . "Al-Zubaidi"^^ . "Salah Al-Zubaidi"^^ . . "Alessandro"^^ . "Ruggiero"^^ . "Alessandro Ruggiero"^^ . . "M.N."^^ . "Mohammed"^^ . "M.N. Mohammed"^^ . . "Jaharah A."^^ . "Ghani"^^ . "Jaharah A. Ghani"^^ . . "Samaher M."^^ . "Sarhan"^^ . "Samaher M. Sarhan"^^ . . "Che Hassan Che"^^ . "Haron"^^ . "Che Hassan Che Haron"^^ . . "Oday I."^^ . "Abdullah"^^ . "Oday I. Abdullah"^^ . . "Adnan Naji Jameel"^^ . "Al-Tamimi"^^ . "Adnan Naji Jameel Al-Tamimi"^^ . . . . . "HTML Summary of #20584 \n\nInvestigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool\n\n" . "text/html" . .