Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool

• Published in: Journal of mechanical behaviour of materials Vol. 32; no. 1; pp. 760 - 6
• Main Authors: Al-Zubaidi, Salah, Ghani, Jaharah A., Haron, Che Hassan Che, Al-Tamimi, Adnan Naji Jameel, Mohammed, M. N., Ruggiero, Alessandro, Sarhan, Samaher M., Abdullah, Oday I., Salleh, Mohd Shukor
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 09.11.2023; Walter de Gruyter GmbH
• Subjects: Aerospace industry; Algorithms; Artificial neural networks; Back propagation networks; Cutting parameters; Cutting speed; Cutting tools; End milling; Factorial design; Feed rate; hybrid algorithm; Mathematical models; Neural networks; Neurons; Optimization; Orthogonal arrays; particle swarm–gravitational search algorithms; Surface roughness; Teeth; Thermal conductivity; Ti6Al4V alloy; Titanium alloys; Titanium base alloys; uncoated tool
• ISSN: 2191-0243; 0334-8938; 2191-0243
• DOI: 10.1515/jmbm-2022-0300

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 against 2.42 × 10 of the 3–8–1 BPNN–PSO hybrid model.