Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites.

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

Sustainable Microfabrication Enhancement of Graphene Nanoplatelet-Reinforced Biomedical Alumina Ceramic Matrix Nanocomposites.

Studies about adding graphene reinforcement to improve the microfabrication performance of alumina (Al2O3) ceramic materials are still too rare and incomplete to satisfy sustainable manufacturing requirements. Therefore, this study aims to develop a detailed understanding of the effect of graphene reinforcement to enhance the laser micromachining performance of Al2O3-based nanocomposites. To achieve this, high-density Al2O3 nanocomposite specimens were fabricated with 0 wt.%, 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% graphene nanoplatelets (GNPs) using a high-frequency induction heating process. The specimens were subjected to laser micromachining. Afterward, the effects of the GNP contents on the ablation depth/width, surface morphology, surface roughness, and material removal rate were studied. The results indicate that the micro-fabrication performance of the nanocomposites was significantly affected by the GNP content. All nanocomposites exhibited improvement in the ablation depth and material removal rate compared to the base Al2O3 (0 wt.% GNP). For instance, at a higher scanning speed, the ablation depth was increased by a factor of 10 times for the GNP-reinforced specimens compared to the base Al2O3 nanocomposites. In addition, the MRRs were increased by 2134%, 2391%, 2915%, and 2427% for the 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% GNP/Al2O3 nanocomposites, respectively, compared to the base Al2O3 specimens. Likewise, the surface roughness and surface morphology were considerably improved for all GNP/Al2O3 nanocomposite specimens compared to the base Al2O3. This is because the GNP reinforcement reduced the ablation threshold and increased the material removal efficiency by increasing the optical absorbance and thermal conductivity and reducing the grain size of the Al2O3 nanocomposites. Among the GNP/Al2O3 nanocomposites, the 0.5 wt.% and 1 wt.% GNP specimens showed superior performance with minimum defects in most laser micromachining conditions. Overall, the results show that the GNP-reinforced Al2O3 nanocomposites can be machined with high quality and a high production rate using a basic fiber laser system (20 Watts) with very low power consumption. This study shows huge potential for adding graphene to alumina ceramic-based materials to improve their machinability.

Common Indications for Referral to the Healthcare system for COVID-19 recovered patients versus Qatar Biobank study population: A descriptive analysis.

BACKGROUND AND AIM OF THE WORK: Qatar Biobank (QBB) is actively acquiring data on the range of short- and long-term health impacts associated with COVID-19. This is performed through the COVID-19 biorepository National project. In this report, we describe the most common indications for the referral to Qatar's healthcare system of COVID-19 biorepository participants in comparison with the Qatar Biobank (QBB) general population study. Methods Patients with a laboratory diagnosis of COVID-19, who were Qatar residents that could communicate in Arabic, English, Hindi and Urdu were eligible to participate in the COVID-19 biorepository project. Biological samples of Consented participants were collected on a weekly basis until recovery, and then monthly for a year. Participants were also offered a bone density scan three months after recovery and non-contrast MRI brain and whole-body scan six months after recovery. Number of participants requiring referral for medical follow up after recovery for any abnormal clinically significant findings were recorded and statistically compared to general population referred participants Results: The majority of referrals for the general population study was for osteopenia versus diabetes for the COVID-19 biorepository project Conclusion Descriptive analysis of the referral data of the COVID-19 participants and QBB general population (not previously affected by the virus) shows a clear difference between the two populations' reasons for referrals. Diabetes for COVID 19 recovered participants versus osteopenia for general population Keywords: COVID19, Reason for Referrals, Diabetes, Qatar biobank.

Investigations on the Effect of Layers' Thickness and Orientations in the Machining of Additively Manufactured Stainless Steel 316L.

Laser-powder bed fusion (L-PBF) process is a family of modern technologies, in which functional, complex (3D) parts are formed by selectively melting the metallic powders layer-by-layer based on fusion. The machining of L-PBF parts for improving their quality is a difficult task. This is because different component orientations (L-PBF-layer orientations) produce different quality of machined surface even though the same cutting parameters are applied. In this paper, stainless steel grade SS 316L parts from L-PBF were subjected to the finishing (milling) process to study the effect of part orientations. Furthermore, an attempt is made to suppress the part orientation effect by changing the layer thickness (LT) of the parts during the L-PBF process. L-PBF parts were fabricated with four different layer thicknesses of 30, 60, 80 and 100 µm to see the effect of the LT on the finish milling process. The results showed that the layer thickness of 60 µm has significantly suppressed the part orientation effect as compared to the other three-layer thicknesses of 30, 80 and 100 µm. The milling results showed that the three-layer thickness including 30, 80 and 100 µm presented up to a 34% difference in surface roughness among different part orientations while using the same milling parameters. In contrast, the layer thickness of 60 µm showed uniform surface roughness for the three-part orientations having a variation of 5-17%. Similarly, the three-layer thicknesses 30, 80 and 100 µm showed up to a 25%, 34% and 56% difference of axial force (Fa), feed force (Ff) and radial force (Fr), respectively. On the other hand, the part produced with layer thickness 60 µm showed up to 11%, 25% and 28% difference in cutting force components Fa, Ff and Fr, respectively. The three-layer thicknesses 30, 80 and 100 µm in micro-hardness were found to vary by up to 14.7% for the three-part orientation. Negligible micro-hardness differences of 1.7% were revealed by the parts with LT 60 µm across different part orientations as compared to 6.5-14% variations for the parts with layer thickness of 30, 80 and 100 µm. Moreover, the parts with LT 60 µm showed uniform and superior surface morphology and reduced edge chipping across all the part orientations. This study revealed that the effect of part orientation during milling becomes minimum and improved machined surface integrity is achieved if the L-PBF parts are fabricated with a layer thickness of 60 µm.

Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach.

The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.

Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques.

Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.