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Fused filament fabrication (FFF), an economic additive manufacturing (AM) method, is largely used for the fabrication of customized components (of medical, engineering, architectural, toy, artistic, etc. industries). However, the poor mechanical and surface properties are critical barriers limiting the growth of FFF. Therefore, a novel heat treatment approach has been utilized to improve the overall performance of printed parts. The parts were made with acrylonitrile-butadiene-styrene (ABS) with three infill densities (20, 60, and 100%) and annealing was carried out by changing the levels of temperature (105, 115, and 125 °C) and time duration (20, 25, and 30 min). The experimental design was conducted by Taguchi orthogonal array while the optimization was conducted using Taguchi S/N approach. The investigated responses were surface roughness, hardness, dimensional accuracy, tensile strength, flexural strength, and impact strength. Moreover, the reliability of the mechanical properties, with higher error (α > 5%), was verified by using the Weibull statistic to determine the survival rate of annealed FFF parts for functional applications. The adopted annealing approach was found to improve the physical and mechanical properties. The SEM analysis of fractured specimens revealed the type of failure (ductile or brittle). In recapitulation, the annealing process improved the quality characteristics of FFF parts.

Recently, the pulsed current tungsten arc welding process (PC-TAW) has cemented their potential in various sorts of industrial application such as automobile, aerospace, and structural joining. However, the involvement of multiple process parameters in PC-GTAW process usually makes the process cumbersome to understand; and thereby, it is difficult to develop the mathematical model. Here, in this scientific work, the major efforts have been made to optimize multiple parameters for selected output responses through the use of evolutionary computational approaches. For this purpose, the particle swarm optimization (PSO), simulated annealing (SA) algorithm, and hybrid PSO-SA (HPSOSA) techniques have been employed and compared in terms of the quality responses for input parameters. From the soft computing modeling results, it has been observed that the HPSOSA improved the process performance and has revealed the global optimal solution within minimum interval of time. The developed models were statistically significant at 95% confidence interval. The experimental and mathematical outcomes for the welded specimens are duly supported with microscopic analyses.

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Throughout service, damage can arise in the structure of buildings; hence, their dynamic testing becomes essential to verify that such buildings possess sufficient strength to withstand disturbances, particularly in the event of an earthquake. Dynamic testing, being uneconomical, requires proof of concept; for this, a model of a structure can be dynamically tested, and the results are used to update its finite element model. This can be used for damage detection in the prototype and aids in predicting its behavior during an earthquake. In this instance, a wireless MEMS accelerometer was used, which can measure the vibration signals emanating from the building and transfer these signals to a remote workstation. The base of the structure is excited using a shaking table to induce an earthquake-like situation. Four natural frequencies have been considered and six different types of damage conditions have been identified in this work. For each damage condition, the experimental responses are measured and the finite element model is updated using the Berman and Nagy method. It is seen that the updated models can predict the dynamic responses of the building accurately. Thus, depending on these responses, the damage condition can be identified by using the updated finite element models.

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines.

Electrical discharge machining (EDM) has recently been shown to be one of the most successful unconventional machining methods for meeting the requirements of today’s manufacturing sector by producing complicated curved geometries in a broad variety of contemporary engineering materials. The machining efficiency of an EDM process during hexagonal hole formation on pearlitic Spheroidal Graphite (SG) iron 450/12 grade material was examined in this study utilizing peak current (I), pulse-on time (Ton), and inter-electrode gap (IEG) as input parameters. The responses, on the other hand, were the material removal rate (MRR) and overcut. During the experimental trials, the peak current ranged from 32 to 44 A, the pulse-on duration ranged from 30–120 s, and the inter-electrode gap ranged from 0.011 to 0.014 mm. Grey relational analysis (GRA) was interwoven with a fuzzy logic method to optimize the multi-objective technique that was explored in this EDM process. The effect of changing EDM process parameter values on responses was further investigated and statistically analyzed. Additionally, a response graph and response table were produced to determine the best parametric setting based on the calculated grey-fuzzy reasoning grade (GFRG). Furthermore, predictor regression models for response characteristics and GFRG were constructed, and a confirmation test was performed using randomly chosen input parameters to validate the generated models.

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

The United Nations (UN) 2030 agenda on sustainable development goals (SDGs) encourages us to implement sustainable infrastructure and services for confronting challenges such as large energy consumption, solid waste generation, depletion of water resources and emission of greenhouse gases in the construction industry. Therefore, to overcome challenges and establishing sustainable construction, there is a requirement to integrate information technology with innovative manufacturing processes and materials science. Moreover, the wide implementation of three-dimensional printing (3DP) technology in constructing monuments, artistic objects, and residential buildings has gained attention. The integration of the Internet of Things (IoT), cloud manufacturing (CM), and 3DP allows us to digitalize the construction for providing reliable and digitalized features to the users. In this review article, we discuss the opportunities and challenges of implementing the IoT, CM, and 3D printing (3DP) technologies in building constructions for achieving sustainability. The recent convergence research of cloud development and 3D printing (3DP) are being explored in the article by categorizing them into multiple sections including 3D printing resource access technology, 3D printing cloud platform (3D–PCP) service architectures, 3D printing service optimized configuration technology, 3D printing service evaluation technology, and 3D service control and monitoring technology. This paper also examines and analyzes the limitations of existing research and, moreover, the article provides key recommendations such as automation with robotics, predictive analytics in 3DP, eco-friendly 3DP, and 5G technology-based IoT-based CM for future enhancements.

In the present research, a composite layer of TiO2-TiC-NbO-NbC was coated on the Ti-64 alloy using two different methods (i.e., the electric discharge coating (EDC) and electric discharge machining processes) while the Nb powder were mixed in dielectric fluid. The effect produced on the machined surfaces by both processes was reported. The influence of Nb-concentration along with the EDC key parameters (Ip and Ton) on the coated surface integrity such as surface topography, micro-cracks, coating layer thickness, coating deposition, micro-hardness has been evaluated as well. It has been noticed that in the EDC process the high peak current and high Nb-powder concentration allow improvement in the material migration, and a crack-free thick layer (215 μm) on the workpiece surface is deposited. The presence of various oxides and carbides on the coated surface further enhanced the mechanical properties, especially, the wear resistance, corrosion resistance and bioactivity. The surface hardness of the coated layer is increased from 365 HV to 1465 HV. Furthermore, the coated layer reveals a higher adhesion strength (~118 N), which permits to enhance the wear resistance of the Ti-64 alloy. This proposed technology allows modification of the mechanical properties and surface characteristics according to an orthopedic implant’s requirements.