Process Parameters Research Articles

Determining process parameters for optimum weld quality in submerged arc welding process of mild steel using a hybrid Fuzzy-MABAC approach

Determining process parameters for optimum weld quality in submerged arc welding process of mild steel using a hybrid Fuzzy-MABAC approach

Prediction of the finished tablet coating variability in pan coaters by coupling CFD-DEM and Monte Carlo simulations: Method development and validation

We present a numerical approach to predict the long-term coating variability of tablets in pan coaters by combining coupled computational fluid dynamics-discrete element method (CFD-DEM) simulations with a Monte-Carlo method. In CFD-DEM simulations, the coater geometry, operating conditions, air flow and exact tablet shapes are directly simulated to obtain statistical distributions of tablet coating mass gain and circulation time through the spray zone. These short-term distributions (∼102 s) serve as input to a Monte Carlo (MC) method to efficiently predict the coefficient of variation in pan coaters for timescales relevant to commercial manufacture (∼104 s). These predictions achieved reasonable agreement with experimental coating thickness measurements via both the manual gauge and optical coherence tomography (OCT) from coating trials on L.B. Bohle film coaters at different scales. This method enables the application of digital tools as efficient alternatives to resource-intensive experimental trials in determining the appropriate coating process parameters.

Modified Gravity in the Presence of Matter Creation: Scenario for the Late Universe

We consider a dynamic scenario for characterizing the late Universe evolution, aiming to mitigate the Hubble tension. Specifically, we consider a metric f(R) gravity in the Jordan frame which is implemented to the dynamics of a flat isotropic Universe. This cosmological model incorporates a matter creation process, due to the time variation of the cosmological gravitational field. We model particle creation by representing the isotropic Universe (specifically, a given fiducial volume) as an open thermodynamic system. The resulting dynamical model involves four unknowns: the Hubble parameter, the non-minimally coupled scalar field, its potential, and the energy density of the matter component. We impose suitable conditions to derive a closed system for these functions of the redshift. In this model, the vacuum energy density of the present Universe is determined by the scalar field potential, in line with the modified gravity scenario. Hence, we construct a viable model, determining the form of the f(R) theory a posteriori and appropriately constraining the phenomenological parameters of the matter creation process to eliminate tachyon modes. Finally, by analyzing the allowed parameter space, we demonstrate that the Planck evolution of the Hubble parameter can be reconciled with the late Universe dynamics, thus alleviating the Hubble tension.

Nano-, micro- and macro-indentation tests of thermal spray WC-Ni coatings with lamellar microstructure at different particle deposition temperatures

The mechanical properties of WC-Ni coatings by HVOF thermal spraying were comprehensively evaluated by indentation tests at a wide load range of 10−1-103 N, i.e. nano-, micro- and macro-indentations. The correlation between process parameters and coating microstructure and mechanical properties was characterized via particle deposition temperature. The coating microstructure changes in reduced porosity, enhanced WC phase decomposition and better splats flattening were correlated to the particle deposition temperature rising. A porosity divergence between coating surface and cross-section was found as a quantitative anisotropy indicator to coating intrinsic lamellar microstructure formed by the splats flattening and piling up. Considering the lamellar microstructure, indentation responses on coating surface and cross-section were compared for hardness and elastic modulus evaluation below 3 N. The surface hardness is higher than that of cross-section, and both increased correlatively to the particle deposition temperature rising except for the almost constant surface hardness below 1 N. An analogous particle temperature-dependent behavior was also manifested for the elastic modulus by nano-indentation below 1 N. Interestingly, the elastic modulus by coating surface micro-indentation at 30 N presented identical particle temperature dependence to that by coating cross-section nano-indentation, both consistently representing the overall coating elastic modulus change trend verified by non-destructive ultrasonic test. Indentation fracture toughness on coating surface under 1.96 kN and on coating cross-section under 49 N was comparatively evaluated, and the values had a reverse trend of dependence on the particle deposition temperature. The particle-deposition-temperature dependent mechanical properties were interpreted by the coating intra-splats and inter-splats defects reduction with enhanced carbide-metal bonding essentially determined by the melting state of the metal binder phase, providing an insight to utilize indentation tests for characterizing thermal spray coatings.

Molten Salt Electrodeposition: Review

Molten salt electrodeposition is the process of producing impressively dense deposits of refractory metals using the electrolysis of molten salts. However, predicting which electrochemical parameters and setup will best control different kinds of deposition (density, homogeneity, etc.) is an ongoing challenge, due to our limited understanding of the properties and mechanisms that drive molten salt electrodeposition. Because these advancements have been made rapidly and in different arenas, it is worth taking the time to stop and assess the progress of the field as a whole. These advancements have increasing relevance for the energy sector, the development of space materials and engineering applications. In this review, we assess four critical facets of this field: (1) how the current understanding of process variables enhances the electrodeposition of various molten salts and the quality of the resulting product; (2) how the electrochemical setup and the process parameters (e.g., cell reactions) are known to impact the electrodeposition of different metal coatings and refractory-metal coatings; (3) the benefits and drawbacks of non-aqueous molten salt electrodeposition, and (4) promising future avenues of research. The aim of this work is to enhance our understanding of the many procedures and variables that have been developed to date. The expectation is that this review will act as a stimulant, motivating scientists to delve further into the investigation of refractory-metal alloys by utilizing molten salt electrodeposition.

Surface morphology of 3C–SiC layers grown on 4H–SiC substrates using TCS as silicon precursor

The hetero-epitaxial growth of 3C–SiC layers on on-axis 4H–SiC substrates has been demonstrated in a hot-wall CVD reactor using trichlorosilane and ethylene as precursors. The additional chlorine is supplied by hydrogen chloride for variation of Cl/Si ratio. The influence of temperature, C/Si ratio and Cl/Si ratio process parameters on the morphology is studied. Double-position-boundaries free 3C–SiC epitaxial layers have been successfully grown at the optimized condition, which is at a temperature of 1340 °C, with C/Si = 0.6 and Cl/Si = 6 using a carbon-rich pretreatment. Low temperature near bandgap photoluminescence shows a good optical property of the obtained 3C–SiC epitaxial layers. Compared to the standard chemistry, a higher growth rate of 12 μm/h could be achieved by utilizing the chlorinated precursors. This study provides a feasible way to grow double-position-boundaries free 3C–SiC epitaxial layers using TCS as silicon precursor.

Synthesis of antibacterial fluorescent carbon dots and green coal-like hydrochar from tea Industry byproducts via hydrothermal carbonization

This study introduces an experimental framework employing hydrothermal carbonization (HTC) for the valorisation of factory tea waste as a solid fuel. Employing a centre composite design and response surface methodology, the investigation systematically assesses the influence of two critical process parameters, temperature (180–220 °C) and time (0–240 min), on higher heating value (HHV), hydrochar yield, and energy yield. The study identifies an optimized HTC condition (220°C, 55 min) resulting in the recorded HHV of 22.26±1.10 Mj/Kg. Combustion characteristics and kinetics of the factory tea waste-derived hydrochar (TW-hydrochar) is analysed through TGA and DTG, complemented by structural elucidation via FTIR and FE-SEM. Concurrently, the study reports the observation of carbon dots (TW-CDs) generation during TW-hydrochar production, characterized comprehensively through UV–visible spectroscopy, TEM, XRD, XPS, Zeta potential, and fluorescence methods. Further, antimicrobial properties of TW-CDs are rigorously investigated against Bacillus subtilis and Escherichia coli, manifesting pronounced antibacterial activity against B. subtilis and E. coli as evidenced by a 12 mm and 11 mm inhibition zone, respectively. This research contributes to the scientific understanding of tea waste valorization through HTC, providing insights into cleaner fuel options and the emergence of carbon dots with potential antibacterial applications.

A multiobjective optimization framework based on FEA, ANN, and NSGA-II to optimize the process parameters of tube-to-tubesheet joint

This study presents a multiobjective optimization framework that integrates Artificial Neural Network (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for the optimization of rolling process parameters of tube-to-tubesheet joint (TTT-joint). During the rolling process, both beneficial contact pressure and detrimental tensile residual stress are generated within the joint. The primary objective of this framework is to minimize the tensile residual stress while maximizing the contact pressure in the TTT-joint. To achieve this, a backpropagation ANN model is trained to rapidly estimate the residual stress and contact pressure for various sets of rolling process parameters. For training purposes, a series of nonlinear elastoplastic finite element (FE) simulations are performed to generate the input database for the neural network. A detailed parametric study is performed based on the axisymmetric FE model of the TTT-joint. The trained neural network is then incorporated into the NSGA-II optimization algorithm to find the fitness function and optimized process parameters. The contact pressure and residual stress predicted by the proposed ANN-NSGA-II framework are validated by finite element analysis (FEA) using the optimized parameters. The present analysis established that the proposed methodology can be applied in practical engineering problems to obtain the process parameters that yield the maximum contact pressure and minimum tensile residual stress in the TTT-joint.

Failure analysis of titanium honeycomb sandwich structures subjected to three-point bending in high-temperature condition

In this study, the failure process and failure mechanism of Ti65 titanium sandwich structure subjected to three-point bending under high temperature are explored by the combination method of experimental analysis and numerical prediction. Test results show that the failure modes change and a 50% decrease in the peak compressive load occurs under high temperature. A self-developed parametric finite element model is deployed to recreate the structural details of sandwich honeycomb structures and exhibit the failure process of the titanium sandwich structure under three-point bending. The predicted failure process and critical parameters have excellent consistency with test results.

Online detection method for magnetic suspension concentration based on machine vision

Abstract With the intelligent development of magnetic particle inspection, the quality of magnetic indications formed at cracks is closely related to the accuracy of magnetic particle inspection image analysis results. The concentration of magnetic suspension is a key process parameter affecting the quality of magnetic indication formation. Hence, this study presents an online detection method based on machine vision for measuring magnetic suspension concentration. The method initially enhances the contrast of images of the pear-shaped measuring tube containing magnetic suspension and then extracts scale lines through feature analysis and morphological processing. A method for extracting the magnetic particle sedimentation area of magnetic suspension based on a dual-threshold segmentation algorithm is proposed. The contour filtering algorithm and pixel calibration method are used to obtain the magnetic particle concentration of the non-estimation and estimation areas based on scale line extraction, ultimately forming an online accurate detection method for magnetic suspension concentration values. Experiments were conducted to validate the method against different concentrations, turbidity levels, tilting angles of the pear-shaped measuring tube, and ambient brightness. The results show that the error in magnetic suspension concentration detection based on this method is within ±3%. This has certain reference value for the stable control of magnetic suspension concentration and for enhancing the reliability of intelligent decision-making results in magnetic particle inspection.

Effect of hot air-assisted radio frequency rotation heating system on improving heating uniformity of dried black fungus (Auricularia auricula)

A large-capacity radio frequency (RF) rotation heating system was employed in this study to heat dried black fungus, and hot air was used for assistance treatment. The effects of electrode gap, rotation rate and hot air on the uniformity of RF heating of dried black fungus were explored. The optimal heating process parameters were obtained, and the sterilization effect was validated. The results showed that the rotation system could significantly enhance the RF heating uniformity of black fungus (P > 0.05). The optimal heating process for heating a large-capacity (480 g) black fungus was to utilize RF heating assisted by 70 °C hot air with an electrode gap of 175 mm and a rotation rate of 60 rpm, followed by 30 min of heat preservation at 70 °C hot air with a rotation rate of 40 rpm. After the entire sterilization treatment, the heating uniformity index λ of black fungus was reduced to 0.027 ± 0.004, the total plate count decreased by 3 log CFU/g, and the moisture content dropped to below 13 %. The ideal RF heating uniformity and sterilization effect of black fungus were obtained in this study through the simultaneous use of hot air and rotation, providing a theoretical foundation for the application of RF technology in the processing of black fungus.

Efficient depolymerization of kraft lignin using zinc chloride based (molten) salts

Lignin is an attractive feed for biofuels and biobased chemicals. We here report the conversion of kraft lignin (Lignoboost) to liquids by hydropyrolysis using a zinc based (molten) salt in a semi-batch reactor. m-Cresol was used to dissolve and inject the lignin into the heated and pressurized reactor containing the molten salt. The liquid yield and fraction of organics in the liquid was optimized by varying the process parameters (hydropyrolysis temperature, pressure, hydrogen gas flow rate and salt to lignin ratio). The highest liquid yield of around 78 wt% was obtained using small amounts of salts (2.5 wt% of salts based on lignin input, 350 °C, 30 bar, 33 wt% lignin in m-cresol, hydrogen flow rate of 160 mL/min). A larger excess of salts gave a lower liquid yield, implying that catalytic amounts of salts are preferred. In addition, small amounts of salts are essential for depolymerization and the formation of lower molecular weight components, as was evident by comparing experiments with and without salts. The liquid product was shown to consist of low molecular weight phenolics like dimethyl- and ethyl-cresol, methyl guaiacol and oligomers (GC-MS, 2DGC-FID, NMR and GPC analysis). The product oil may have potential for biofuel production when coupled with a catalytic hydrotreatment step to obtain hydrocarbons or, after downstream processing and catalytic conversions, to important biobased chemicals like green phenol.

Coaxial Electrohydrodynamic Bioprinting of Pre-vascularized Cell-laden Constructs for Tissue Engineering.

Recapitulating the vascular networks that maintain the delivery of nutrition, oxygen, and byproducts for the living cells within the three-dimensional (3D) tissue constructs is a challenging issue in the tissue-engineering area. Here, a novel coaxial electrohydrodynamic (EHD) bioprinting strategy is presented to fabricate thick pre-vascularized cell-laden constructs. The alginate and collagen/calcium chloride solution were utilized as the outer-layer and inner-layer bioink, respectively, in the coaxial printing nozzle to produce the core-sheath hydrogel filaments. The effect of process parameters (the feeding rate of alginate and collagen and the moving speed of the printing stage) on the size of core and sheath lines within the printed filaments was investigated. The core-sheath filaments were printed in the predefined pattern to fabricate lattice hydrogel with perfusable lumen structures. Endothelialized lumen structures were fabricated by culturing the core-sheath filaments with endothelial cells laden in the core collagen hydrogel. Multilayer core-sheath filaments were successfully printed into 3D porous hydrogel constructs with a thickness of more than 3 mm. Finally, 3D pre-vascularized cardiac constructs were successfully generated, indicating the efficacy of our strategy to engineer living tissues with complex vascular structures.

Processing correlations of laser direct energy deposited Inconel718 based on multi-field numerical simulation

Laser direct energy deposited (LDED) Inconel718 always have problems of non-uniform temperature distribution because of the complex flow of molten pool and layer-by-layer fabrication characterization which directly affected the microstructure and mechanical properties of materials. In this work, Inconel718 revealed a refined grains and a weave structure exhibiting excellent mechanical properties which were superior to those of the conventional casting process were successfully fabricated by adopt a high-precision and intelligent coaxial powder feeding system during the LDED processing. Moreover, based on multi-field numerical simulation method, a two-phase flow model and a transient thermal mechanical deformation model were established to simulate the LDED processing, while takes into account the evaporated mass loss of the inconel718 alloy. The morphology prediction of laser directed energy deposition by multi-physics numerical simulation, the relationship among the laser process parameters, i.e., the laser power, scanning speed and the amount of powder feeding, the temperature filed and the microstructure and mechanical properties of Inconel718 alloy during LDED were investigated and established. The simulated results consistent well with the experimental results. Results shown that the relative density of sample was sensitive to the laser power, the precision and stability of the powder feeding rate. With increasing laser power, the grain size of Inconel718 is refined, and the microhardness and tensile properties of the materials increased. The optimized LDED processing parameters were determined. Evaluating the mechanical properties of the materials LDEDed at a laser power of 600W and a scanning speed of 8.5mms-1 exhibited a high tensile strength of 944±5MPa and a good elongation of 29±0.5%. This work suggests that the processing parameters can be optimized to prepare densified materials with excellent mechanical properties.

Model performance evaluation of build time using geometric shape complexity and process parameters in material extrusion

Additive Manufacturing (AM) is gaining widespread interest and recognition in both academia and industry due to its numerous advantages over traditional manufacturing processes. The AM process offers two significant benefits: mass customization and the ability to achieve higher shape complexity at no cost. The present study introduces a methodology for quantifying the shape complexity of feature-based parametric models that AM can produce. This method is exclusively a quantitative approach based on the “number of inputs” required to define the feature or integral sketch. Furthermore, an Artificial Neural Network (ANN) based predictive model for estimating the AM build time has been developed and validated. Supervised Machine Learning (ML) is employed to predict printing time by considering significant design and manufacturing parameters that affect the overall process time. A performance evaluation is conducted between the developed ANN model and a statistical Multiple Linear Regression (MLR) model. The validation Computer Aided Design (CAD) samples are chosen to evaluate the ANN and MLR model's performance to predict the build time. It is observed that the ANN model outperforms the MLR model in estimating the build time of complex geometries. Regarding the efficiency of models, the ANN model achieved an R square value of 0.880 and a lower prediction error of 15.543%. However, the MLR model showed some sensitivity to high complexity and large volume parts when predicting printing time, with a R square value of 0.96. In contrast, the ANN model is more precise in forecasting FDM build time for complex geometries and large volume parts.

Enhancing machining efficiency in hybrid metal matrix composites through EDM parameter optimization via grey relational analysis

In summary, this study utilized stir casting to develop three distinct aluminium hybrid composites incorporating boron carbide and alumina. These composites were identified as Al7075 + 12%B4C + 6%Al2O3, Al7075 + 6%B4C + 6%Al2O3, and Al7075 + 6%B4C + 12%Al2O3. The novelty of this research lies in the unique combination of materials and their proportions, offering enhanced properties yet unexplored in this specific configuration. The research focused on machining these composite materials using EDM and employed a Taguchi L27 orthogonal array design to plan the machining tests. Key process parameters considered were gap voltage (V), duty cycle (%), current (A), and different percentages of reinforcement content. This aspect adds a significant novel dimension to the study, as the impact of EDM machining parameters on hybrid composites with this particular composition has not been extensively explored previously. The study applied Grey Relational Analysis to evaluate the impact of EDM machining process parameters on critical output responses, specifically material removal rate (MRR) and tool wear rate (TWR). SEM images revealed redeposited molten material, craters, and surface cracks, emphasizing the importance of controlling discharge energy and current levels for desired surface quality. An optimal configuration was identified peak current 2 A, pulse duration 100 μs, duty factor 20%, Composition 2 for improved surface attributes. These qualitative and quantitative insights inform process optimization strategies for EDM machining of aluminium hybrid composites.

The effect of different process parameters on the flowability of modified magnesium-coal based solid waste carbon fixation backfill slurry rich in dicalcium silicate

The effect of different process parameters on the flowability of modified magnesium-coal based solid waste carbon fixation backfill slurry rich in dicalcium silicate

Influence of 3D printed porous aluminum anode structure on electrochemical performance

Aluminium-air (Al-air) batteries have been considered as one of the most promising next-generation energy storage devices. In this study, we investigated the effect of structural changes in the main body of porous aluminium anode on the electrochemical performance under the constraints of the 3D printing process using both simulation and experimental methods. By analysing the simulation results under the constraint of 3D printing process, we found that the variation of the base fillet radius affects the dimensional deviation of the porous aluminium anode. As the radius of the fillet increases, the degree of warpage deformation gradually decreases. For this reason, we experimentally verified the self-corrosion rate, electrochemical and discharge properties of aluminium anodes under the same process parameters. The test results are consistent with the simulation analysis. With the increase of fillet radius, the self-corrosion rate of anode gradually decreases and the electrochemical activity gradually increases. Meanwhile, the discharge voltage was as low as 1.55 V in the periphery without rounded corners, and increased by 1.3 % and 3.2 % when the periphery radius was 1 mm and 2 mm, respectively. This research result provides a new method for other researchers to further improve the overall performance of aluminium-air batteries.

Effect of the Building Orientation on Additively Manufactured Copper Alloy: Hydraulic Performance of Different Surface Roughness Channels

The additive manufacturing capabilities open new frontiers in the design of complex geometries in many different application fields, especially thermal science, in which multi-functional, efficient, compact components with internal cooling or heating channels are becoming more and more requested. Among the possible additive manufacturing technologies, the laser powder bed fusion process has recently been proven to manufacture high-conductivity metals, with good mechanical and thermal properties (i.e. pure copper and copper alloys), attracting the attention of the heat transfer community. However, depending on the material, design, and process parameters, the surface roughness of the components can remarkably change and become a critical issue in cooling applications. Currently, the surface texture characteristics and the final channel size cannot be accurately estimated a-priori, and thus the initial hypothesis of the current design methods may lead to unpredictable and undesired results. This work experimentally and numerically explores the effects of the building orientation on the fluid dynamic behaviour in CuCrZr alloy channels, which is considered one of the most promising additive manufacturing materials for thermal applications. By coupling the experimental hydraulic results with surface characterization and X-ray computer tomography dimensional measurements of the channels, a novel simplified methodology to properly correlate the pressure drop to the surface texture of a single wall is proposed and numerically validated.

An artificial intelligence approach to model and optimize biodiesel production from waste cooking oil using life cycle assessment and market dynamics analysis

Biodiesel has emerged as a viable alternative to fuel, offering a more sustainable and environmentally friendly energy option. The current study explores the modeling and optimization of biodiesel production from waste cooking oil using artificial intelligence and genetic algorithms. The study focuses on enhancing five process parameters: methanol-to-oil molar ratio, catalyst concentration, reaction temperature, reaction time, and stirring speed. The optimization of these parameters is complemented by a life cycle assessment to reduce environmental impact. The approach considers biodiesel yield, high heating value, and energy consumption as output variables, thereby advancing sustainable biodiesel production. The findings indicated that, under optimal conditions (methanol-to-oil ratio of 1:6.9, stirring rate of 500 rpm, reaction duration of 20 s, reaction temperature of 30 °C and catalyst concentration of 1), the transesterification process achieved the maximum biodiesel yield of 97.76 %. The optimization reached a low environmental impact in the production of biodiesel in an efficient way. Additionally, SWOT analysis helps to develop strategic methods that can enhance efficiency and increase competitiveness. The research suggests that, by optimizing the chemical process in biodiesel production, it is possible to achieve a high yield and high heating value of the biofuel, along with feasible environmental mitigation strategies.