Prediction of Mechanical Properties for Carbon fiber/PLA Composite Lattice Structures Using Mathematical and ANFIS Models.

This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations.

Compression Performance and Failure Analysis of 3D-Printed Carbon Fiber/PLA Composite TPMS Lattice Structures.

Triply periodic minimum surface (TPMS)-based lattice structures have gained interest for their outstanding capacity to absorb energy, their high load-bearing capacity, and their high surface-to-volume ratio. This study considered three TPMS cell topologies, including Diamond, Gyroid, and Primitive. The FDM process was used to print the lattice structures with two materials: pure polylactic acid (PLA) and carbon fiber-reinforced PLA (PLA + CF). The influence of carbon fiber (CF) incorporation, unit cell type (topologies) and size, and relative density (RD) on mechanical properties and failure patterns were explored comprehensively under uniaxial compression testing. The results demonstrate a change in the compressive modulus (0.09 to 0.47 GPa), compressive strength (2.98 to 13.89 MPa), and specific energy absorption (SEA) (0.14 MJ/m3/g to 0.58 MJ/m3/g) due to the influence of CF incorporation, cell type and size, and RD. Results indicate that the Diamond structure outperformed both Primitive and Gyroid structures in terms of compressive modulus and strength, and SEA. All the CF-based TPMS structures showed a higher compressive modulus. Compressive strength and energy absorption capacity were both slightly enhanced in most PLA + CF-based Diamond structures. On the contrary, Gyroid and Primitive structures showed better performance for pure PLA-based structures in terms of compression strength and specific absorption energy.

A Novel Flushing Mechanism to Minimize Roughness and Dimensional Errors during Wire Electric Discharge Machining of Complex Profiles on Inconel 718.

One of the sustainability goals in the aeronautical industry includes developing cost-effective, high-performance engine components possessing complex curved geometries with excellent dimensional precision and surface quality. In this regard, several developments in wire electric discharge machining have been reported, but the influence of flushing attributes is not thoroughly investigated and is thus studied herein. The influence of four process variables, namely servo voltage, flushing pressure, nozzle diameter, and nozzle-workpiece distance, were analyzed on Inconel 718 in relation to geometrical errors (angular and radial deviations), spark gap formation, and arithmetic roughness. In this regard, thorough statistical and microscopical analyses are employed with mono- and multi-objective process optimization. The grey relational analysis affirms the reduction in the process's limitations, validated through confirmatory experimentation results as 0.109 mm spark gap, 0.956% angular deviation, 3.49% radial deviation, and 2.2 µm surface roughness. The novel flushing mechanism improved the spark gap by 1.92%, reducing angular and radial deviations by 8.24% and 29.11%, respectively.

EDM of Ti-6Al-4V under Nano-Graphene Mixed Dielectric: A Detailed Investigation on Axial and Radial Dimensional Overcuts.

Ti-6Al-4V is considered a challenging material in terms of accurate machining. Therefore, electric discharge machining (EDM) is commonly engaged, but its low cutting rate depreciates its use. This issue is resolved if graphene nanoparticles are mixed in the dielectric. However, the control over the sparking phenomenon reduces because of the dispersion of graphene particles. Subsequently, the machined profile's geometric accuracy is compromised. Furthermore, the presence of nanographene induces different sparks along axial and radial cutting orientations. This aspect has not been comprehensively examined yet and dedicatedly targeted in this study to improve the quality of EDM process for Ti-6Al-4V. A total of 18 experiments were conducted under Taguchi's L18 design considering six parameters namely, electrode type, polarity, flushing time, spark voltage, pulse time ratio, and discharge current. The aluminum electrode proved to be the best choice to reduce the errors in both the cutting orientations. Despite the other parametric settings, negative tool polarity yields lower values of axial (ADE) and radial errors (RDE). The developed optimal settings ensure 4.4- and 6.3-times reduction in RDE and ADE, respectively. In comparison to kerosene, graphene-based dielectric yields 10.2% and 19.4% reduction in RDE and ADE, respectively.

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.

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture.

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.