doi:10.1515/jmbm-2022-0300

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Publication Date

Sun Jan 01 2023

Journal Name

Journal Of The Mechanical Behavior Of Materials

Volume

32

DOI

10.1515/jmbm-2022-0300

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

Salah

Jaharah A.

Che Hassan Che

Adnan Naji Jameel

M. N.

Alessandro

Samaher M.

Oday I.

Mohd Shukor

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AbstractTitanium 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.

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Publication Date

Fri Dec 01 2023

Journal Name

Chemical Methodologies

Investigations on TiO<inf>2</inf>-NiO@In<inf>2</inf>O<inf>3</inf> Nanocomposite Thin Films (NCTFs) for Gas Sensing: Synthesis, Physical Characterization, and Detection of NO<inf>2</inf> and H<inf>2</inf>S Gas Sensors

M.A.

Fuad Tariq

S.

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Publication Date

Fri Jan 13 2023

Journal Name

Journal Of The Faculty Of Medicine Baghdad

Detection of Chronic Obstructive Pulmonary Disease among Smokers Aged ≥40 years Attending Primary Health Care Centers in Baghdad-AL-Karkh and AL-Resafa: الكشف عن الالكشف عن مرض الانسداد الرئوي المزمن بين المدخنين الذين تزيد أعمارهم عن 40 عامًا يراجعون مراكز الرعاية الصحية الأولية في بغداد - الكرخ و الرصافة

Isra A.

Nada Abdul Wahhab

Faris

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Background: Chronic obstructive pulmonary disease causes permanent morbidity, premature mortality and great burden to the healthcare system. Smoking is it's most common risk factor and Spirometry is for diagnosing COPD and monitoring its progression.

Objectives: Early detection of chronic obstructive pulmonary disease in symptomatic smokers’ ≥ 40years by spirometry.

Methods: A cross sectional study on all symptomatic smokers aged ≥ 40 years attending ten PHCCs in Baghdad Alkarkh and Alrisafa. Those whose FEV1/FVC was <70% on spirometry; after giving bronchodilator, were considered COPD +ve.

Results: Overall, airway obstruction was seen in

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