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dc:title "HTML Summary of #20618 \n\nInvestigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool\n\n";
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bibo: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 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."^^xsd:string;
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bibo:issue "1";
bibo:status ,
;
bibo:volume "32";
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dct:date "2023";
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dct:title "Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool"^^xsd:string;
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foaf:name "Walter de Gruyter GmbH"^^xsd:string;
rdf:type foaf:Organization .
foaf:familyName "Abdullah"^^xsd:string;
foaf:givenName "Oday I."^^xsd:string;
foaf:name "Oday I. Abdullah"^^xsd:string;
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foaf:familyName "Mohammed"^^xsd:string;
foaf:givenName "M.N."^^xsd:string;
foaf:name "M.N. Mohammed"^^xsd:string;
rdf:type foaf:Person .
foaf:familyName "Ghani"^^xsd:string;
foaf:givenName "Jaharah A."^^xsd:string;
foaf:name "Jaharah A. Ghani"^^xsd:string;
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foaf:familyName "Haron"^^xsd:string;
foaf:givenName "Che Hassan Che"^^xsd:string;
foaf:name "Che Hassan Che Haron"^^xsd:string;
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foaf:familyName "Ruggiero"^^xsd:string;
foaf:givenName "Alessandro"^^xsd:string;
foaf:name "Alessandro Ruggiero"^^xsd:string;
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foaf:familyName "Salleh"^^xsd:string;
foaf:givenName "Mohd Shukor"^^xsd:string;
foaf:name "Mohd Shukor Salleh"^^xsd:string;
rdf:type foaf:Person .
foaf:familyName "Al-Tamimi"^^xsd:string;
foaf:givenName "Adnan Naji Jameel"^^xsd:string;
foaf:name "Adnan Naji Jameel Al-Tamimi"^^xsd:string;
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foaf:familyName "Sarhan"^^xsd:string;
foaf:givenName "Samaher M."^^xsd:string;
foaf:name "Samaher M. Sarhan"^^xsd:string;
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foaf:familyName "Al-Zubaidi"^^xsd:string;
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foaf:name "Salah Al-Zubaidi"^^xsd:string;
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bibo:issn "21910243";
foaf:name "Journal of the Mechanical Behavior of Materials"^^xsd:string;
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