Optimal Parameters Research Articles

Removal of Fluoride from Water by using Natural Adsorbent

Abstract: Access to safe drinking water is not only the elementary need of human being but also honored as an mortal right .For overall development of the country , as acceptable nutrition, education, gender equivalency, and particularly the eradication of poverty in developing countries are required , along with that furnishing safe drinkable water is equally important . Fluoride one of the water quality parameters of concern, with redundant( beyond1.5 mg/ l, World Health Organization( WHO) guideline value of what contaminates groundwater coffers in numerous corridors of the world, and renders it not drinkable for mortal consumption, due to affiliated adverse health goods thus, knowledge of its junking, using the stylish fashion with optimum effectiveness demanded . Taking the inflexibility of the problem into consideration, the present paper aims to give a retrospective approach to the use of effective low- cost adsorbents for the junking of fluoride from water . The defluoridation capacity of certain low- cost natural adsorbents like Groundnut shell powder and Soya chunk powder were added in the list and discussed. The effect of pH, adsorbent dose, initial concentration , and contact time also delved . It's set up that the junking of Fluoride ions at optimum parameters like pH, adsorbent dose ,initial concentration and contact time is at 7, 2gm, 5mg/ l, and 100 minutes respectively for groundnut shell powder and 6, 2gm, 5mg/ l, and 100 minutes respectively for Soya chunk powder. For all optimum parameters, the maximum reduction in Fluoride ions observed in the range of 55 to 65% for groundnut shell powder whereas for soya chunk powder it's 40 to 50%

Parametric optimization in drilling of sisal–glass reinforced epoxy composites using Taguchi grey relational analysis method

This research work intends to study the effect of hybridization of glass and sisal fiber, stacking sequence and tensile properties of the composite. The sisal-glass fiber hybrid composites laminates are prepared using reinforced plain woven sisal fabric (unidirectional) and plainwoven glass fabric. In this research study, 27 experiments are conducted as per L27 orthogonal array. Five process parameters are selected and three responses are considered in this work. The drilling of the composite specimen is considered and the drilling process parameters such as speed, feed rate, drill diameter, material thickness, and drill point angle are selected. The responses considered in this work are delamination factor, thrust force, and torque. Taguchi analysis is performed and the response table for means for the responses is determined, and the most influencing parameter in the drilling of the composite specimen is analyzed. The grey relational coefficients are computed and followed with the computation of the grey relational grade. The grey relational grades are calculated for determining the highest contributing parameter in the drilling of the sisal fiber and glass fiber reinforced hybrid composite specimen. The optimum drilling process parameters are ranked and the ranks presented represent the sequence of run resulting in optimum solutions.

Effects of pelletization on biomethane production from wheat straw

ABSTRACT The high lignin content and low bulk density of wheat straw pose challenges for biogas production. To overcome these hurdles, pretreatment and biomass pelletization have emerged as viable options. Thus, this research aims to reduce the recalcitrant nature of WS by employing various thermal pretreatment techniques and identifying the optimum parameters (temperature and time). To prepare pellets, a mixture comprising wheat straw (subjected to the best pretreatment) and cow manure pellets (WCP) at varying substrate and binder (Svs/Bvs) ratios ranging from 0.5 to 2.5 were used. Parameters such as density, water absorption, and drop shattering were evaluated to evaluate the physical characteristics of produced WCP. Additionally, the biomethane yield test of WCP (exhibiting the most favourable physical characteristics) was performed with various total solids (TS) concentrations from 4 to 12%. The WS demonstrated the highest sCOD solubilisation of 9066 mg/L when subjected to a hot air oven pretreatment (90 min at 110°C). The physical qualities of WCP were found to be dependent on the Svs/Bvs ratio (with the optimal ratio being 2.0). It was also observed that a TS content of 6% yielded the highest biomethane production (253.85 mL/g-VSconsumed). In summary, this study’s conclusion waves the path of management of wheat straw and cow dung while simultaneously generating bioenergy.

A Comprehensive Study of Method Optimisation of Re‐Refining Spent Lubricating Oil

ABSTRACTUsed lubricating oil is generated by various machinery after extended operation. It is also referred to as spent mobile oil. Extremely hazardous waste lubricating oil is detrimental to the environment because it produces oxidative products when additives break down. Used lubricating oil is classified as a hazardous waste substance and has a negative impact on the environment. Polychlorinated biphenyls (PCBs), carcinogenic substances and other impurities make lubricating oil poisonous and pose a serious threat to human health and the environment. Re‐refining is considered the preferred technology for resource conservation, waste minimisation and reduced environmental hazards. The present study focuses on optimising the method of re‐refining waste lubricating oil. The effects of various operating parameters such as refining time, refining temperature, solvent‐to‐used oil ratio and flocculant dosage have been extensively studied to maximise the percentage recovery of lubricating oil. Optimum process parameters are (i) a refining time of 80 min, (ii) a refining temperature of 48°C, (iii) solvent‐to‐waste oil ratio of 5:1 (w/w) and (iv) a flocculant dosage of 2 g/kg of solvent; the optimum yield was found to be 75% with the solvent extraction method and 78% with the extraction–flocculation method, respectively. The purity and physico‐chemical properties of the recovered oil were thoroughly analysed using Fourier transform infrared spectroscopy and ASTM standard methods. It was concluded that refined oil can effectively reduce the ongoing oil crisis and create a clean, healthy environment.

Response surface methodology directed modeling of the biosorption of progesterone onto acid activated Moringa oleifera seed biomass: Parameters and mechanisms

In this study, chemically activated fat-free powdered Moringa oleifera seed biomass (MOSB) was synthesized, characterized, and utilized as a cost-effective biosorbent for the abstraction of progesterone (PGT) hormone from synthetic wastewater. Natural PGT is a human steroid hormone from the progestogen family. Synthetic PGT is approved for the regulation of the menstrual cycle, aiding contraception, and is administered as a hormone replacement therapy in menopausal and post-menopausal women. PGT is an endocrine disrupting chemical (EDC) with negative health impacts on biota. The X-ray diffractogram (XRD), Scanning electron microscopy-Energy-dispersive X-ray spectroscopy (SEM-EDS), and Brunauer–Emmet–Teller (BET) analyses displayed a porous, amorphous biosorbent with an elemental composition of 72.5% carbon and 22.5% oxygen and a specific surface area of 210.0 m2 g−1. The process variables including temperature (298–338 K), pH (2–10), contact time (10–180 min), adsorbate concentration (20–500 μg L−1), and adsorbent dosage (0.1–2.0 g) were optimized using response surface methodology (RSM) to obtain the greatest efficacy of MOSB during biosorption of PGT. The optimum parameters for PGT biosorption onto MOSB were: 86.8 min, 500 μg L−1 adsorbate concentration, 298 K, and 0.1 g adsorbent dosage. PGT removal from aqueous solutions was pH-independent. The Langmuir isotherm best fitted the equilibrium data with maximal monolayer biosorption capacity of 135.8 μg g−1. The biosorption rate followed the pseudo-first-order (PFO) kinetic law. The thermodynamic functions (ΔG < 0, ΔH = −9.258 kJ mol−1 and ΔS = +44.16 J mol−1) confirmed that the biosorption of PGT onto MOSB is a spontaneous and exothermic process with increased randomness at the adsorbent surface. The biosorption mechanism was physisorption and was devoid of electrostatic interactions. The findings from this study indicate that MOSB is an inexpensive, low-carbon, and environmentally friendly biosorbent that can effectively scavenge PGT from aqueous solutions.

Sunlight active Zeolite decorated N-doped nickel oxide nanocomposite for the efficient photocatalytic degradation of toxic phenols

Nanotechnology is the most advanced technique in current times, with nanoparticles and nanocomposites playing a vital role in the remediation of toxic contamination. Notably, nanoparticles synthesized using plant extracts exhibit superior stability and biocompatibility when compared to their counterparts produced through conventional chemical and physical methods. The present study focuses on the green synthesis of N-NiO@Zeolite nanocomposite utilizing Camellia sinensis (green tea) plant leaf extract. The green synthesized nanocomposite was employed for the photocatalytic degradation of toxic organic pollutants including, 3-Aminophenol (3-AP) and bisphenol A (BPA). Under solar irradiation, modified nanomaterials demonstrated excellent photocatalytic activities for degrading 3-AP and BPA. The optimum parameters for photocatalytic degradation were found to be pollutant concentrations of 10 mg/L for 3-AP and 4 mg/L for BPA, photocatalyst dosages of 15 mg for 3-AP and 10 mg for BPA, and neutral pH for both pollutants. Within 120 min, the synthesized N-NiO@zeolite nanocomposite achieved degradation efficiencies of 96 % for 3-AP and 94 % for BPA, superior performance on comparing its parental materials, N-NiO (78 % for 3-AP and 75 % for BPA) and NiO (66 % for 3-AP and 65 % for BPA). The degradation kinetics of both pollutants were accurately represented by first-order kinetics, Langmuir-Hinshelwood model and were consistent with the Langmuir adsorption isotherm model. Furthermore, the environmentally friendly N-NiO@Zeolite nanocomposite displayed outstanding durability and reusability, retaining photocatalytic efficiency after 10 cycles of repeated usage, and its stability was validated by PXRD analysis after treatment. This excellent reusability and stability demonstrate the nanocomposite’s potential as a long-term solution for commercial treatment of wastewater applications.

Granulated graphene oxide-activated carbon for adsorptive removal of diclofenac sodium and ibuprofen in a continuous fixed-bed column

The granular graphene oxide-activated carbon (GGA) composite was synthesized with polyvinyl alcohol (PVA) and malonic acid (MA) as chemical crosslinker at different conditions. GGA synthesis was performed at different parameters including binder-to-powder ratio in the range of 0.5–1.2, synthesis time in the range of 2–24 h, and drying temperature in the range of 60–110 °C. Response surface methodology (RSM) was used to optimize the synthesis parameters of the adsorbent synthesis. The optimum parameters for synthesis were achieved at a binder-to-powder ratio of 0.875, a synthesis time of 23.992 h, and a drying temperature of 60.071 °C. XRD, FTIR, FE-SEM, BET and TGA analyses were used to discuss the characteristics of the adsorbent structures. The surface area and mechanical stability were found to be 156.59 m2/g and 95.417 %, respectively. The adsorption behavior of mono-components (diclofenac sodium (DCF)/ibuprofen (IBP)) and bi-components (DCF + IBP) on the GGA was evaluated in a fixed-bed column. The effects of column operating parameters, including bed mass (m: 0.25–0.75 g), flow rate (Q: 5–9 mL/min), and adsorbate concentration for DCF (C: 20–100 mg/L) and IBP (C: 5–15 mg/L) on the breakthrough curves were investigated. The experimental data of fixed-bed column were correlated with breakthrough models such as Thomas, Bohart-Adams, Yoon-Nelson, Yan, Gompertz, and Log-Gompertz via both linear (under different column operating conditions) and nonlinear (for DCF (Co of 60 mg/L; m of 0.5 g and Q of 7 mL/min) and IBP (Co of 10 mg/L; m of 0.5 g and Q of 7 mL/min)) regression analysis. The maximum adsorption capacities calculated by the Thomas model were 110.068 and 34.912 mg/g for DCF and IBP, respectively. In bi-component systems, antagonistic behavior was observed in the adsorption of DCF and IBP. The results showed that the GGA can successfully remove DCF and IBP through a fixed-bed adsorption column.

Determination of selective laser melting process parameters of tungsten carbide powders coated with nickel via electroless plating for improved interface properties

Tungsten carbide powders are commonly used at various industrial applications due to their high hardness. However, these powders may exhibit low binding and processing challenges without a binder. Therefore, various binding methods such as electroless Ni coating are used to hold WC powders together, increase their processability and improve the mechanical properties of the final product. Electroless Ni plating plays an important role in the use of WC powders, especially in processes such as selective laser melting. The structural, tribological, and mechanical properties of parts manufactured by SLM method are significantly influenced by the parameters employed in the process. Hence, optimizing process variables is essential to unlock the full potential of this advanced manufacturing technique. The main objective of this study was to identify the optimum process parameters for the SLM method of WC-Ni composite powders, which were prepared by coating WC powder materials with electroless Ni. Experimental investigations focused on analyzing the laser power, scanning speed, and hatch spacing parameters. The effects of these parameters on the mechanical and tribological properties of WC-Ni composite material were investigated using microhardness tester, tribometer device, optical microscope, 3D optical profilometer and scanning electron microscope. Eventually, it was determined that the manufacturing parameters employed in the SLM method had a significant impact on relative density, microstructure, surface roughness, hardness, and tribological properties. Samples exhibited a relative density of nearly 99%, and the highest observed hardness value was 1962 HV. This article supports a specialized SLM manufacturing process developed specifically to enhance durability and performance across a broad spectrum of industrial applications through interface improvement and the optimization of manufacturing parameters.

A spin-polarized DFT analysis of the physical attributes of vacancy ordered Rb2TcCl6 double perovskite for optoelectronic and spintronics

Due to their versatile properties, vacancy-ordered double perovskites offer a wide range of potential uses due to their versatile properties. We have thoroughly examined the physical characteristics of vacancy-ordered Rb2TcCl6 in its cubic phase by employing the spin-polarized computations. The ground state energy and optimum structural parameters are obtained in order to assess the structural stability, which is further validated using the tolerance factor and formation energy values. The ductile character of Rb2TcCl6 is revealed by a comprehensive evaluation of mechanical properties. Vickers hardness factor suggests that Rb2TcCl6 can withstand against pressure-induced disturbance more strongly. A direct bandgap for spin-up is measured up to 2.33 eV, while for spin-dn it is 2.02 eV. According to the magnetic moments of 2.69 μB, Rb2TcCl6 is an attractive material for spintronic devices. A number of optical parameters are determined. For optoelectronic applications, Rb2TcCl6 is an excellent choice due to its strong polarization in the visible and ultraviolet regions, as shown by its optical characteristics.

Prediction of bubble departing diameter in pool boiling of mixtures by ANN using modified ReLU

In this research, bubble departure diameter in pool boiling have been measured in aqueous amine and ethylene glycol solutions for various concentrations. The experimental data have been compared with major existing predictive correlations. It is shown that the effect and identity of the independent variables on bubble diameter proposed in the previous studies are inconsistent. The predictions of different correlations have on average a deviation of about 40% from the experimental data. This is mainly due to the complicated interactions between bubbles on the heterogeneous boiling medium, which provides a complex condition. This complexity limits any mathematical modelling of the forces acting on the developing bubbles. Particularly in liquid solutions, where mass transfer by back diffusion through micro-sub-layers adds further complexity. In this work, the classical artificial neural network, ANN, with rectified linear unit, ReLU, activating function, AF, has been modified. This modification is based on adding a numerical matrix to each layer to adjust the slope of AF for each neuron independently. The addition of this parameter, together with the adjustment of the bias matrix, makes the activation function more flexible than the classical ReLU. To find the tuning parameters, a genetic algorithm was implemented instead of the back-propagation technique. It is shown that the predictions of the trained ANN with modified ReLU AF agree within an absolute average error of 10%, which is equal to the total uncertainty of the measurements. Prediction of bubble departing diameter in boiling phenomena is a key parameter for accurate design, operation and optimisation in many industrial systems.

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

Evaluation of Electro-Fenton Process for Removal of Amoxicillin from Simulated Wastewater

&lt;p&gt;The purpose of this work is to examine the efficiency of the Electro-Fenton process (Fenton oxidation) for the elimination of amoxicillin from simulated wastewater through response surface methodology (RSM). The effect three operating parameters that were selected for this evaluation were electrolysis time, hydrogen peroxide concentration, and the current density. The Electro-Fenton method was assessed by a set of experiments that were designed using the Box-Behnken model. The results conclude optimum parameters conditions, at 30 min electrolysis time, 30 ppm H2O2, and current density 2 amps by fixing the other two parameters; 20 ppm concentration of amoxicillin and pH of 3. The maximum removal efficiency at these conditions was 98.11%. The mathematical model concluded has a high correlation coefficient (R2= 98.3). Depending on the results, the Electro-Fenton process has proven an excellent method for the elimination of antibiotics from wastewater.&lt;/p&gt;

Oil-recovery performance of a superhydrophobic sponge-covered disc skimmer

Frequent oil spill accidents caused by transportation, storage and usage may lead to severe damage on aquatic and ecological environments. Effective methods for rapid oil recovery are urgently in demand. Polyvinyl chloride, hydrophobic nano-SiO2, expanded graphite were separately applied to polyurethane and melamine sponge to fabricate superhydrophobic sponge material. The selected superhydrophobic sponge was introduced to establish sponge - covered disc skimmer. Oil recovery tests of the device were conducted to determine the optimum parameters. The examined operating conditions encompassed sponge thickness, immersion depth, rotational speed, oil slick thickness, operation time. The results showed that the melamine sponge modified by both polyvinyl chloride and hydrophobic nano-SiO2 exhibits super-hydrophobicity with a water contact angle of 150.3°. The absorption capacity for diesel oil can reach 53.89 g/g. The absorption capacity can still achieve 90 % of its initial capacity even after 500 extrusion-absorption separation tests. The results indicate the superiority of the superhydrophobic sponge covered surface in oil recovery over the standard steel surface regardless of the operating conditions. The recovery rate of the device can still achieve 96.4 % of its initial capacity with 95 % efficiency even after 85 h operation. The results suggest the superhydrophobic sponge - covered disc skimmer may have great application perspectives in oil spill recovery.

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Optimization of stepped hole drilling for consistent aperture in BCF/PEEK and PEEK stacks

To ensure the aperture consistency of the stepped hole in the drilling of braided carbon fiber reinforced polyether ether ketone (BCF/PEEK) and PEEK stacks, a step variation parameter method is proposed to obtain the optimum process parameters in this study. Initially, the lay-up sequence is determined by analyzing diameter differences in drilled sequences of BCF/PEEK and PEEK stacks. Concurrently, hole-wall surface morphology and damage are observed under traditional drilling conditions to define stepped hole phenomena. Subsequently, optimum step position is identified by analyzing diameter differences at various positions with variable parameters. Moreover, the influence weight of drilling parameters on diameter difference is assessed using Taguchi orthogonal array. An optimization process scheme is proposed for minimum diameter difference control. Results suggest spindle speed of 2000 r/min and feed rate of 40 mm/min for BCF/PEEK, and 5000 r/min and 30 mm/min for PEEK are optimum parameters. This method holds promise for achieving consistent apertures in other laminated composites during drilling.

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

Production and Characterization of Activated Carbon Derived from Costus Afer Leaves (C. afer) for the Adsorption of Methylene Blue Dye from an Aqueous Solution

In this work, Costus afer leaves (C. after) was utilized as an agricultural waste material for the synthesis of activated carbon (AC) used for the adsorption of methylene blue dye from its aqueous solution. The raw material was prepared, chemically activated using KHCO3 with an impregnation ratio of 1:3, and later carbonized at a temperature of 700℃ in an inert N2 atmosphere for 1hour to produce activated carbon. The proximate analysis of the biomass revealed the percentage of ash content, moisture content, volatile matter, and fixed carbon present in the waste biomass. The raw and activated carbon produced were characterized using various tests such as: Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), Fourier Transform Infrared-(FTIR) Spectrometry, X-Ray Fluorescence (XRF), and Brunauer-Emmett-Teller (BET). The results revealed that the SBET of the biomass increased from 400m2/g to 600m2/g after activation. Adsorption studies were carried out in batch mode to determine the influencing factors of adsorption with varying adsorbent dosage, contact time, and pH values. The adsorption experiment revealed high adsorption efficiency at an optimum parameter of dosage (0.2g), contact time (10 min), and pH (8) with a 70.58% removal of methylene blue dye. An increase in dosage, contact time, and pH above the optimum parameter leads to a decrease in percentage removal. The adsorption capacity was achieved using adsorption isotherm models like Langmuir and Freundlich. Adsorption of methylene blue validated the Langmuir model with an R2 of 0.999 and a Qm of 11.83 mg/g. These results show that activated carbon prepared from Costus afer leaves constitutes an effective low-cost material for the adsorption of Methylene dye from wastewater.

Compressive failure analysis of composite honeycomb sandwich panels with impact damage and stepped-scarf repairs

Composite honeycomb sandwich panels (CHSPs) have been widely used in the aerospace industry owing to their lightweight and superior mechanical properties. However, these CHSPs are susceptible to impact damage, leading to a significant reduction in compressive strength and potentially jeopardize aircraft safety. It is crucial to investigate repair methods for CHSPs with impact damage. This paper aims to evaluate the repair performance of CHSPs with impact damage through an analysis of their compressive failure behaviors. To accomplish this, both experimental tests and advanced numerical models are employed. The numerical models for the intact, damaged and stepped-scarf repaired CHSPs are established using the progressive failure analysis model, cohesive zone model and sandwich plate theory. A good agreement is observed between the experimental results and numerical predictions of compressive failure behaviors. Moreover, the validated numerical models are successfully utilized for determining optimum repair parameters by parametric analysis of the repaired CHSPs. The establishment of these numerical models offers an accurate and cost-effective evaluation of repair performance for CHSPs with impact damage, highlighting the novelty and main contribution of this paper.

Effects of the processing parameters and laser textures in friction stir spot welding of Al alloy and polyimide joints

In this paper, friction stir spot welding (FSSW) technology was introduced to join 7075 Al alloy to polyimide (PI) sheets. The effects of the FSSW parameters and laser textures on the microstructure and tensile shear load of the Al/PI joint were studied. The optimum FSSW parameters were the rotation speed of 2400 r/min, the plunge depth of 0.4 mm, the dwell time of 20 s, and the plunge speed of 10 mm/min. The tensile shear load of the joint increased owing to the laser textures in the Al alloy surface. Cross-sections showed that the mechanical locking was found between the Al alloy and PI. The XPS results revealed that no chemical bond were formed at the welding interface after laser textures. The joining mechanism of Al 7075 and PI was main the mechanical locking.

A Study on Powder Spreading Quality in Powder Bed Fusion Processes Using Discrete Element Method Simulation

Powder deposition is a very important aspect of PBF-based additive manufacturing processes. Discrete Element Method (DEM) is commonly utilized by researchers to examine the physically complex aspects of powder-spreading methods. This work focuses on vibration-assisted doctor blade powder recoating. The aim of this work is to use experiment-verified DEM simulations in combination with Taguchi Design of Experiments (DoE) to identify optimum spreading parameters based on robust layer quality criteria. The verification of the used powder model is performed via angle of repose and angle of avalanche simulation–experiment cross-checking. Then, four criteria, namely layer thickness deviation, surface coverage ratio, surface root-mean-square roughness and true packing density, are defined. It has been proven that the doctor blade’s translational speed plays the most important role in defining the quality of the deposited layer. The true packing density was found to be unaffected by the spreading parameters. The vertical vibration of the doctor blade recoater was found to have a beneficial effect on the quality of the deposited layer. Ultimately, a weighted mean quality criteria analysis is mapped out. Skewness and kurtosis were proven to function as effective indicators of layer quality, showing a linear relation to the weighted means of the defined quality criteria. The specific weights that optimize this linearity were identified.