Optimization of process variables in drilling of carbon fiber reinforced plastics using various multi criteria decision making approaches

Carbon fiber-reinforced polymers are one of the lightweight materials used in structural design due to their exceptional mechanical performances. The drilling operation is indispensable as it facilitates the assembling of various manufactured components. However, drilling of fibrous laminates is deemed difficult in comparison to the traditional metals because of the anisotropic and non-homogeneous nature. The present work addresses the parametric effect on the drilled hole delamination and further reduces it with an optimal combination of parameters for multi-objectives using different multi-criterion decision-making techniques. Initially, the response surface-based regression model of delamination as a function of three static inputs has been developed, further revised with induced thrust as well as mean torque for the improvisation of the prediction capability. Finally, for the overall improvement, a decision-making model has been used that includes grey relation analysis, technique for order performance by similarity to ideal solution, and VIšekriterijumsko Kompromisno Rangiranje method. The delamination was found to be minimum at a low drill point angle (100°), high spindle rotation (2150 min⁻¹ ), and low feed rate (0.025 mm/rev) due to reduced thrust force. The mean absolute prediction error was significantly improved considering root mean square torque rather than axial thrust with process variables.     

Extreme learning machine and bayesian optimization-driven intelligent framework for IoMT cyber-attack detection

The Journal of Supercomputing - The Internet of Medical Things (IoMT) is a bionetwork of allied medical devices, sensors, wearable biosensor devices, etc. It is gradually reforming the healthcare...     

Optimizing process parameters for joint strength efficiency improvement in friction stir welding of polycarbonate sheets

The present work addresses optimization of these friction stir welding process variables to maximize joint strength efficiency of welded polycarbonate sheets by using particle swarm optimization algorithm over response surface method based regression model. Initially, parametric influence on weld quality characteristics namely weld bead profile, bead geometry with associated microstructure along with micro-hardness deviation through the weld centerline and stress-strain behavior of the weld have been studied in detail as per full factorial design of experiments by using three different tool pin profiles such as cylindrical, square and triangular. The center point experiment i. e. tool rotational speed of 1800 min⁻¹ and welding speed of 20 mm/min, and square tool pin profile were found to be the optimum combination with a maximum joint strength efficiency of 60.06 %. The regression model of weld ultimate tensile strength was developed by using response surface methodology which was found to be significant. Therefore, this model was further used for parametric optimization by using both response surface methodology and intelligent particle swarm optimization approaches. A slight improvement in joint strength efficiency with better optimization capability was found by using particle swarm optimization technique as compared to response surface methodology.     

Oxygen Barrier of Multiwalled Carbon Nanotube/Polymethyl Methacrylate Nanocomposites Prepared by in situ Method

Multiwalled carbon nanotubes (MWCNTs)/poly(methyl methacrylate) (PMMA) nanocomposites were prepared by ultrasonic assisted emulsifier free emulsion polymerization technique with variable concentration of functionalized carbon nanotubes. MWCNTs were functionalized with H 2 SO 4 and HNO 3 with continuing sonication and polished by H 2 O 2 . The appearance of Fourier transform infrared absorption bands in the PMMA/MWCNT nanocomposites showed that the functionalized MWCNT interacted chemically with PMMA macromolecules. The surface morphology of functionalized MWCNT and PMMA/MWCNT nanocomposites were studied by scanning electron microscopy. The dispersion of MWCNT in PMMA matrix was evidenced by high resolution transmission electron microscopy. The oxygen permeability of PMMA/MWCNT nanocomposites gradually decreased with increasing MWCNT concentrations.     

Influence of curing time on the chloride penetration resistance of concrete containing rice husk ash: A technical and economical feasibility study

This study investigates the influence of the curing time on the chloride penetration behavior of concrete produced with different concentrations of rice husk ash. Compressive strength and chloride penetration at 91 days were assessed according to ASTM C1202. Concentrations of 10%, 20% and 30% of rice husk ash were used and the results were compared with a reference mix with 100% Portland cement and with two other binary mixes with 35% fly ash and 50% ground blast furnace slag. Increases in rice husk ash content produced lower Coulomb charge values. Longer curing times reduced Coulomb charges values for all mixes investigated. However, the extent of the effect of curing times on compressive strength and chloride penetration in concrete is related to the type of mineral addition, the concentration of the substitutions used, the w/b ratio and the curing time used. This behavior points at an optimal curing period for each type of binder to meet specific technical and economical criteria, namely durability and compressive strength specifications for the structure.     

Tribological properties of MoSi2–MoS2 coatings coupling with SAE 52100 steel under reciprocating sliding

Tribological properties of MoSi₂–MoS₂ coatings coupling with SAE52100 steel were tested under reciprocating sliding. Effects of normal load, sliding speed and MoS₂ content on the coatings tribological properties were studied. Worn surfaces of the coatings were analyzed by scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The friction coefficient of the coatings was approximately 0.45 and a little lower than that of the monolithic MoSi_2. The friction did not vary with the sliding time, sliding speed and load. Coating with 12 wt.% MoS₂ had the lowest friction. Wear rate of the coatings increased with the sliding speed and normal load and was higher than that of the monolithic MoSi₂. Wear rate of the coatings did not vary with MoS₂ content. Worn surface of the coatings and the coupling steel ball was covered by a SiO₂ and MoO₃ wear debris layer. Wear mechanism of the coatings was microfracture.     

Inhibited grain growth in hydroxyapatite–graphene nanocomposites during high temperature treatment and their enhanced mechanical properties

Nanostructured hydroxyapatite (HA)–graphene nanosheet (GN) composites have been fabricated by spark plasma sintering consolidation. Nanostructual evolution of the bioceramic-based composites during further high temperature heat treatment is characterized and enhanced mechanical strength is assessed. GN keeps intact after the treatment and its presence at HA grain boundaries effectively inhibits HA grain growth by impeding interconnection of individual HA grains. Microstructural characterization discloses strong coherent interfaces between GN and the (300) plane of HA crystals. This particular matching state in the composites agrees well with the competitive theoretical pull-out energy for single graphene sheet being departed from HA matrix. The toughening regimes that operate in HA–GN composites at high temperatures give clear insight into potential applications of GN for ceramic matrix composites.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Evaluation and Characterization of Mechanical, Electrical, and Thermal Properties of Polymer Nanocomposites Reinforced with Different Allotropes of Carbon

The present work aims to present the results based on the processing of nanocomposites, which consist of matrix materials like epoxy and filler materials such as conjugated nanomaterials/allotropes of carbon, namely, carbon black, graphite, and multiwalled carbon nanotube (MWCNT) used for targeted applications. To improve the physical and chemical properties and to facilitate a better interfacial interface between the polymer and nanotube, functional MWCNT is used during the preparation of the composite. The prime objective of the study is to establish the thermal, mechanical, and electrical properties of nanocomposites using experimental methods. It has been observed from the experimental results that carbon nanotube (CNT) based composite exhibits higher mechanical (tensile and hardness) and thermal properties as compared with the others. The electrical properties are found to be better in a graphite-based composite. Although CNT has superior mechanical and thermal properties, the exorbitant price limits its use. Hence, the allotropes of carbon may be used judiciously considering both the cost and property requirements of the targeted application. The work also studies the dispersion state of nanofibers through scanning electron microscopy (SEM).