Optimal Parameters Research Articles

Classification-Based Parameter Optimization Approach of the Turning Process

The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

Photovoltaic Integrated Shading Devices in the Retrofitting of Existing Buildings on Chinese Campuses Within a Regional Context

Retrofitting existing buildings to be more energy-efficient is a tremendous contribution to the sustainability of society. The application of photovoltaic integrated shading devices (PVSDs) accords with this ambition by blocking out unwanted radiant heat gain and generating clean electricity. The deployment of PVSDs needs sensible design strategies to optimize the production of renewable energy while retaining the aesthetic quality of the built-up environment, especially in historic campuses. The concept was tested in a case study of buildings in South China University of Technology (SCUT) using Ladybug 1.4.0 and PVsyst 7.2, utilizing the existing “Xia’s shading” design method in historical environments and optimizing the design from the perspective of photovoltaic performance. Firstly, the photovoltaic (PV) panels were integrated as architectural components, and the parameters were incorporated into a mathematical equation based on “Xia’s shading” design method. This was followed by the assessment of the solar energy harvesting potential based on simulated annual solar irradiation values. Lastly, the PV panels’ solar irradiation potential under these different parameters was shown in figures to identify the optimum parameters combination for PVSD applications. The proposed methodology could evolve as a design tool and thus further assist in promoting the large-scale adoption of PVSDs in retrofit projects.

Efficient and sustainable approach of heavy metal adsorption from wastewater using algal biomass of Hypnea valentiae (Turner) Montagne, 1841

The biosorption of heavy metals especially, Cd2+, Pb2+, and Zn2+ from wastewater were evaluated using the biomass of macroalgae, Hypnea valentiae found in coastal waters of Rameshwaram, Tamil Nadu, India. The probable functional groups involved in the biosorption process was analyzed by Fourier transform infrared spectroscopy. The specific surface area, pore volume and pore diameter of prepared biosorbent was determined by Brunauer–Emmett–Teller (BET) analysis and it was found to be 59.993 m2 g−1, 0.292 cc g−1 and 1.429 nm, respectively. The scanning electron microscopy images after the biosorption revealed that the heavy metals have been adsorbed on to the surface of biosorbent. The optimization study was performed using Box–Behnken design and the optimum parameters used were initial concentration of metal ions (10–500 mg L−1), biomass dose (5–25 g L−1), contact time (10–120 min), temperature (25–45 °C), agitation speed (60–120 rpm) and pH (2–14). The heavy metal uptake (biosorption) was assessed by isotherm models including Dubinin–Radushkevich (D–R), Freundlich and Langmuir models. The maximum sorption of metal ions was observed in Langmuir isotherm model (R L 0.1773 and K F 2.2030 L mg−1) with an R 2 value close to 0.99 clearly indicating the chemical mode of metal ion adsorption. Also, the sorption of Cd2+, Pb2+, and Zn2+ on to the surface of algal biomass followed pseudo-second-order kinetics with a rate constant, k2 being 9.627 × 10−3 g (mg min) −1 and qe being 93.98496 mg g−1, strongly supporting the chemisorption mechanism. The computed thermodynamic parameters (ΔG°, ΔH, and ΔS) demonstrated an endothermic, spontaneous biosorption that was feasible. The maximum removal efficiency was achieved for Pb (92.86%) followed by Cd (91.48%) and Zn (87.32%). The biosorbent can be reused and regenerated up to four cycles efficiently. There was a significant reduction in the biological oxygen demand, chemical oxygen demand, total dissolved solids, and pH of wastewater after treatment with the biosorbent. From this study, it is assessed that the biomass from red algae can be utilized for the biosorption of heavy metals from polluted water which is cost-effective and eco-friendly.

Investigation of cutting forces of glass sphere reinforced polymer composites

AbstractIn this study, the effects of reinforcement ratio, drill bit material, and drilling process parameters on the cutting forces generated during the drilling of glass sphere‐reinforced polypropylene composites were investigated experimentally. Test specimens were produced by conventional injection‐moulding of glass sphere reinforced polypropylene composite materials at 5 %, 10 % and 20 % wt. ratios. High speed steel, titanium‐coated high‐speed steel, and carbide drill bits with 4 mm diameter were preferred for drilling. In addition, the drilling process was carried out on a 3‐axis computer numeric control milling machine. Three different feed rates of 0.05 mm/rev, 0.10 mm/rev, and 0.15 mm/rev and cutting speeds of 12 m⋅min−1, 16 m⋅min−1, and 20 m⋅min−1 were determined for the drilling process. In addition, Taguchi experimental optimization method, analysis of variance and scanning electron microscopy were used to investigate the morphology of the drilled surface and the wear mechanisms occurring on the drill bit due to the drilling process. The test findings showed that the maximum torque value was 54.64 N⋅mm and the maximum thrust force was 100.43 N. The optimum test parameter for cutting forces was observed as C1D3FR1CS3. Drill parameter had an effect of 40.96 % on thrust force and 36.11 % on torque.

Bio-sorption of methylene blue using Datura stramonium leaves as adsorbent

Present study was accomplished to prospect the viability of using the Datura stramonium leaves powder (DS) as an adsorbent to remove the methylene blue from aqueous solution. The physico-chemical characteristics of the studied adsorbent were examined. The optimum parameters such as contact time, particle size, absorbent dose, initial methylene blue concentration, and pH were investigated by performing batch experiments models. The kinetics and the isotherms adsorption were evaluated by varying the initial concentration and using the optimum parameters. The optimum of contact time is 30min, with a removal capacity of 89.60 %. The optimal adsorbent concentration to reach the maximum removal of methylene blue (89.54 %) is 18 g/L. An initial methylene blue concentration of 50 ppm is ideal to reach the maximum capacity of removal (92.72 %). The optimum particle size is 80 mm. The kinetics of the adsorption process are in accordance with the pseudo-second order model. Experimental values of the adsorption capacity are close proximity to the optimum values predicted by the pseudo-second order model. Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Harkin-Jura and Hasley isotherms were applied to represent the data obtained from the adsorption studies. The highest R2 values were related to Freundlich, Dubinin-Radushkevich and Hasley isotherm models.

Dual-mode, signal-amplified DNA biosensor for label-free, reliable assay of gestational diabetes mellitus-related miRNA (miR-135a)

miR-135a is highly expressed in patients with gestational diabetes mellitus, and its target genes are also involved in insulin signaling pathway, so it is one biomarker for gestational diabetes mellitus. Herein we designed a dual-mode DNA biosensor for reliable assay of miR-135a based on the fluorescence and colorimetric signals. Several experiments were carried out to demonstrate the assay feasibility and mechanism for this dual-mode DNA biosensor. With optimum parameters, this proposed dual-mode biosensor has been realized sensitive and quantitative assay of miR-135a. For the fluorescence and colorimetric signals, the working ranges are 0.56-61 and 8.3-74nM, while limits of detection are 0.18 and 3.7nM respectively. This dual-mode strategy allows two independent signals for miR-135a assay, so it can verify each other to show more accurate results with good fidelity. Furthermore, there is a good selectivity in the biosensor for target miR-135a over other nucleotide variants, as well as good anti-interference ability in complex samples. This dual-mode DNA biosensor provides a new approach for miR-135a assay and miRNA expression profiling in gestational diabetes mellitus.

Effects of freeze–thaw on the detachment capacity of soils with different textures on the Loess Plateau, China

Soil detachment capacity (Dc) is a crucial indicator for quantifying erosion intensity. However, the combined actions of freeze–thaw and water flow complicate the erosion process, leaving the variation mechanism of Dc under this condition systematically unexplored. This study examined five loess soils from a seasonal freeze–thaw area. The mechanism driving changes in Dc was quantified through freeze–thaw simulation combined with flow scouring tests, and a Dc prediction model was established. The results revealed that the shear strength (τm), cohesion (Coh), and internal friction angle (φ) in silt loam were higher than in sandy loam. After freeze–thaw cycles (FTC), τm, Coh, and φ of the five loess soils decreased by 1.02–1.37, 1.07–9.15, and 0.92–1.05 times, respectively. As FTC increased, τm and Coh gradually stabilized, while φ showed minimal fluctuation, indicating that FTC had a cumulative effect on the deterioration of soil mechanical properties. During FTC, Dc in Wuzhong sandy loam was the largest, being 1.14–3.24 times greater than in other soils, suggesting a significant main effect of soil type on Dc variation, with a contribution rate of 19.27 %. Dc eventually stabilized with increasing, indicating a critical FTC of around 10 for its impact on Dc. Compared to unfrozen soils, Dc increased by 33.69 %–102.40 % under the combined effects of freeze–thaw and water flow, clarifying that FTC aggravated soil instability. Effective stream power was the optimum hydraulic parameter, contributing the most to Dc (45.94 %). FTC (6.41 %) and initial soil moisture content (8.59 %) were less influential, as FTC initially degraded soil properties, and then the combined action with water flow intensified soil damage, causing the role of freeze–thaw factors to be obscured by other variables. A Dc prediction model using a general flow intensity index estimated well Dc, with both R2 and NSE at 0.94. Model performance comparison emphasized the need for validation when extending the application range beyond development conditions. These findings provide new insights into the detachment mechanisms of different textured soils under compound freeze–thaw and hydrodynamic influence in freeze–thaw region.

Study on heat transfer and flow of molten pool during the deposition process of laser wire additive manufacturing

In the laser wire additive manufacturing process, the molten pool acts as the critical link between the wire and the deposited part. The heat transfer and flow behavior within the molten pool predominantly determine the quality of the final deposition. Under laser power ranging from 2400 to 3000 W, traverse speeds between 0.01 and 0.04 m/s, and wire feeding speeds from 0.03 to 0.08 m/s, three distinct flow states—single-swirl, double-swirl, and no-swirl—were observed with increasing heat input. Under the optimum process parameters, the molten pool with stable temperature distribution and orderly flow was obtained. In multilayer deposition, the implementation of a laser decay strategy mitigates steep temperature gradients, diminishes the Marangoni effect within the molten pool, and effectively reduces both heat accumulation and lateral flow. Consequently, the flow mode transitions from no-swirl to swirl, and the maximum flow velocity decreases by 40%.

Novel versatile 5-aminoisophthalic acid modified chitosan nanofiber pads for adsorption toward Congo red, methyl orange, crystal violet, and methylene blue

To structure novel nanofiber pads for organic dye removal from dye-polluted water, we previously prepared CSP nanofiber pads by chitosan (CS) and polyethylene oxide (PEO) via electrospinning, followed by preparation of CSRP nanofiber pads through partial PEO removal from CSP, and the subsequent fabrication of CS-A nanofiber pads via grafting 5-aminoisophthalic acid (5-AIPA) to CSRP. FTIR, XRD, and elemental analyses confirmed the successful synthesis of CS-A nanofiber pads (crosslinking and crystalline degrees of 36.64 % and 4.57 %). CS-A nanofiber pads had porous nanofiber structures (diameter and pore size of 153.46 and 3.82 nm), high specific surface area (102.06 m2/g), great thermal stability (total mass loss of 50.2 %), excellent hydrophilicity (water contact angle of 12.0°), superior swelling performance (2331 %), good pH stability (pH of 3.0–11.0), and 6.8 of pHpzc, evidenced by SEM, Brunauer-Emmett-Teller (BET), thermogravimetric (TG), differential thermogravimetric (DTG), wettability, swelling, and solid addition tests. Optimum adsorption parameters (e.g., pH, dosage, and initial concentration) were selected by batches of tests on adsorption toward four organic dyes. CS-A’s surfaces were heterogeneous, and the adsorption belonged to spontaneous endothermic chemical adsorption, confirmed by kinetic and isothermal models and thermodynamic results. CS-A nanofiber pads were of great reusability and easy to be separated. Adsorption mechanisms involved electrostatic attractions, hydrogen bondings, π-stacking interactions, and pore fillings. Dynamic adsorption toward Congo red showed the Thomas model well fitted the breakthrough curves (R2 > 0.9971), showing the maximum adsorption capacity of 949.15 mg/g. Totally, CS-A nanofiber pads were effective adsorbents suitable for organic dye-containing wastewater purification.

Quick-Delivery Mold Fabricated via Stereolithography to Enhance Manufacturing Efficiency

The ever-growing demand for reducing costs and decreasing the time to market in today’s plastics industry makes rapid tooling and rapid prototyping highly researched areas. Stereolithography (SLA)-manufactured injection mold inserts make it possible to produce prototype parts rapidly and cost-effectively. To utilize SLA in the injection molding industry, two steps have to be considered. The first is to identify suitable SLA process and post-thermal curing process parameters to enhance the mechanical and thermal characteristics. The second is to verify the applicability of SLA-manufactured molds for use in the injection molding industry. IA comprehensive study was performed to find the optimum process parameters for an SLA mold with excellent mechanical and thermal properties and to verify the applicability of the mold. First of all, the mechanical and thermal properties of samples manufactured based on various laser powers and heat treatment at different temperatures were analyzed with a tensile test, DSC, and TMA according to the degree of cure. On the basis of the results from those tests, an SLA mold was designed and fabricated with the optimum mechanical and thermal properties. In addition, the SLA mold was assembled into an injection machine, and an injection molding test was conducted. The SLA mold endured during the injection cycle, and 500 shots were successfully injected without damaging the mold, which resulted in reaching the quick-delivery mold standard. Finally, we demonstrate that SLA is an effective technology to produce molds for use in the injection molding industry.

Removal of Dibenzofuran from aqueous solution by sugarcane bagasse-based biochar

Dibenzofuran (DBF) has been considered an environmental risk due to its high toxicity and risks to human health and ecosystems. Among wastewater treatment technologies, the adsorption process has emerged as a potential solution to remove organic pollutants efficiently, including dibenzofuran, in wastewater. The study aims to investigate the feasibility of sugarcane bagasse-based biochar for DBF removal through adsorption. Biochar characteristics showed a high specific surface area of up to 498.97 m2/g and abundant functional groups on the material surface, resulting in high removal performance of DBF with average adsorption efficiency and adsorption capacity reaching maximum values of 98.43% and 96.77 mg/g, respectively. The optimum parameters were suggested for DBF removal: pyrolysis temperature of 700oC, contact time of 50 min, biochar dosage of 0.5 g/L, and DBF concentration of 40 mg/L. Furthermore, the results of adsorption kinetics and adsorption isotherms indicated that the adsorption process benefits DBF removal. Pseudo-second-order model and Langmuir model can describe the DBF removal process due to the best fit to experimental data (R2 > 0.98). Based on these findings, sugarcane bagasse-based biochar could be utilized efficiently to remove DBF from wastewater.

Phosphate pre-concentration from wastewater by a continuous flow bench-scale forward osmosis membrane: A circular economy approach on nutrient recovery

Phosphate is a vital element necessary for the growth and development of plants, making it a crucial element in agricultural fertilizers. Extracting phosphate from wastewater for recovery aligns with the principles of a circular economy by closing the loop on nutrient cycles. Instead of viewing wastewater as a waste product, treating it as a resource for recovering valuable nutrients like phosphate promotes a more sustainable and efficient use of resources. In this study, the phosphate from synthetic wastewater was pre-concentrated by a continuous flow forward osmosis membrane system to facilitate effective precipitation. The pre-concentration feasibility of forward osmosis was studied by changing the draw concentration, feed concentration, feed pH, and flow rate of the feed solution. With the optimum operational parameters in batch mode such as pH of 7, the draw solute concentration of 0.4 M MgSO4, the feed and draw flowrate of 0.7 L/min and 0.3 L/min, the phosphate concentration was increased from 30 mg/L to 525 mg/L after 60 min, the average osmotic flux and reverse solute flux was 2.7 L/m2/h and 0.43 g/m2/h, respectively. As operated with continuous mode, the final phosphate concentration was increased four times, the average osmotic flux was higher with 8.1 L/m2/h and the reverse solute flux was 0.86 g/m2/h, which could improve the efficiency of calcium phosphate crystallization in precipitation/ crystallization reactors. This integrated system, which includes forward osmosis as a pretreatment method followed by a precipitation process, is technically possible for application on a larger scale as a circular economy approach.

Profiling The Growth Conditions and Persistent Organic Pollutants (POPS) Tolerance of Phenoliferia glacialis USM-PSY62

Antarctica is characterized by extreme cold, isolated, and unique ecosystems. Nevertheless, Antarctica harbors diverse species of microorganisms, particularly in its ice-covered lakes and subglacial environments. These microorganisms have special adaptations to extreme cold and low-nutrient conditions. Some extremophiles, like psychrophiles can thrive in these harsh environments. Phenoliferia glacialis USM-PSY62, previously identified as Rhodotorula sp. USM-PSY62 is a psychrophilic yeast isolated from the ice brine of Antarctica. However, there is very little information on this psychrophilic yeast. This study aims to characterize the P. glacialis USM-PSY62 through the identification of the optimum growth parameters in different media (Yeast Peptone Dextrose, YPD & Yeast Malt, YM), temperature (4°C, 15°C, 20°C) and pH (6, 7, 8, 9) as well as their ability in carbon assimilation and extracellular enzyme production. It has an optimal growth in YPD compared to YM broth media. P. glacialis USM-PSY62 grows optimally at 15°C and pH 7.0. This Antarctic yeast enters the stationary phase on day six of incubation under optimum conditions. It appeared mainly as elongated-shape and oval-shaped with budding formation and was found to produce extracellular enzymes such as protease and amylase in the presence of 2% glucose concentration in YM media. P. glacialis USM-PSY62 also can assimilate various types of carbon sources including raffinose, arabinose, and maltose. Interestingly, the psychrophilic yeast presented growth in media supplemented with Persistent Organic Pollutants (POPs) such as dichlorophenyldichloroethylene (DDE) and polychlorinated biphenyl (PCB). These preliminary findings suggest that P. glacialis USM-PSY62 has tremendous potential for bioremediation application in polluted cold regions, as well as deepening our knowledge of its optimal growth conditions.

Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.

Investigation into micro slotting of Cu-based shape memory alloy via µ-ECM using ethylene glycol mixed aqueous NaCl electrolyte

ABSTRACT Micro-Electrochemical Machining (µECM) presents significant promise as a future micromachining process offering higher machining rates, improved precision and the ability to work with a wide range of materials. Cu-based shape memory alloys (SMAs) have exceptional properties that make them quite difficult to machine using traditional techniques. In this investigation a new electrolyte solution consisting of ethylene glycol (EG) and NaCl has been explored while attempting µECM of Cu-SMA. The Cu-based shape memory alloy used consists primarily of Cu-53.19%, Zn-41.50%, and 5.2% other elements. Four different parameter combinations selected based on pilot experiments were used for the main experimentation conducted in four sub-sets. The influence of micromachining parameters including machining voltage, electrolyte concentration and micro-tool feed rate on µECM characteristics such as material removal rate (MRR) and surface roughness (SR) during micro-slot formation has been investigated. With the identified optimum parameters, a high-quality micro-slot was successfully machined on Cu-based shape memory alloys achieving a material removal rate of 0.323 mg/min and surface roughness of 0.384 μm. The investigation demonstrated that an ethylene glycol electrolyte containing NaCl is more suitable for µECM of micro-slots offering superior surface integrity and shape accuracy compared to a water-based electrolyte.

Biodegradable hydroxylase polyols production from epoxidized palm oleic acid

Recently, the trend of using renewable sources has great attention in the production of polyol chemicals via the epoxidation method. Palm-based polyols produced from the epoxidation and hydroxylation methods usually have low hydroxyl values (below 200 mg KOH/g). This study aimed to synthesize the hydroxylase polyols with a higher hydroxyl value from the renewable source palm oil. The epoxidized palm oleic acid was synthesized using the in situ performic acid, which is obtained from the reaction of formic acid and hydrogen peroxide. The Taguchi method was executed to optimize the epoxidation process for the maximum production of epoxidized oleic acid. Then, optimized epoxidized palm oleic acid was hydroxylated to produce polyols. In addition, the effect of process parameters on the polyol yields was also studied. At the optimum process parameters, the response of relative conversion to oxirane (RCO) with the determination of oxirane oxygen content (OOC) was found at the maximum value of 82%. According to the results, the maximum hydroxyl value produced was 339 mg KOH/g. A kinetic study revealed that the oxirane cleavage by methanol is a second-order reaction whereas the formation of epoxidized palm oleic acid is a first-order reaction.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

Reaction kinetics for catalytic hydrogenation of quinoline to decahydroquinoline as liquid organic hydrogen carrier

The catalytic hydrogenation of quinoline to decahydroquinoline (DHQ) formation is studied for various parameters such as temperature, hydrogen pressure, reaction time, solvents, reactant-to-solvent ratio, and catalyst loading over 5%Pd loaded on alumina (Pd/Al2O3) to obtain optimal reaction conditions. The hydrogen uptake of quinoline in the liquid phase reaction using isopropylalcohol (IPA) is studied in an autoclave reactor. The optimum reaction parameters of 50 bar H2 pressure and 175oC with reactant to solvent ratio of 1:9 for reaction time of 5 h are observed. The effect of IPA solvent showed that hydrogen uptake of 6.91 wt% with complete hydrogenation of quinoline and DHQ yield of more than 99% is observed. The reaction kinetic model is developed for a simplified reaction mechanism and is simulated using the Markov Chain Monte Carlo (MCMC) simulation tool to predict the rate constants and the experimental observations are validated with the model predictions. The activation energy for quinoline hydrogenation to py-THQ formation is estimated to be 136.57 kJ/mol. It is envisaged that quinoline hydrogenation to pz-THQ is the rate-limiting step in the DHQ formation.

Preparation and excellent mechanical properties of nanocrystalline GW103K Mg alloy by HDDR technology

Grain size of GW103K Mg alloy powder were markedly refined to about 45 nm from 20 μm by an optimized hydrogenation-disproportionation-desorption-recombination (HDDR) process. By changing temperature, hydrogen pressure and time, the optimum hydrogenation conditions are explored by orthogonal design. Then the complete dehydrogenation process was determined by using single factor control variables. Furtherly, the HDDRed powder was spark plasma sintering (SPS) sintered and hot extruded to prepare GW103K alloy bulk. XRD, OM, scanning electron microscopy (SEM), and transmission electron microscope (TEM) were used to analyze the phase transformation and microstructure evolution, based on which the grain refining mechanism during the HDDR were discussed. The optimum HDDR parameters for preparing nanocrystalline Mg alloy powders are hydriding at 550 °C under 5 MPa hydrogen pressure for 24 h and dehydriding at 550 °C for 8 h in vacuum. The nanocrystalline GW103 alloy presents a notable dimensional stability and the grain size of the alloy bulk is 43 nm after SPS and hot extrusion. As a result, the nanocrystalline GW103K specimens exhibit excellent mechanical properties compared to the coarse-grained counterparts. Especially, the nanocrystalline GW103K alloy has a tensile elongation of 21.1% which is much more than those of the as-cast and as-extrusion alloys.

Effect of high pressure-low plasticity burnishing on the mechanical and microstructural behaviour of copper foil interlayered FSW aluminium alloys

Abstract This study delves into the impact of high pressure-low plasticity burnishing (HP-LPB) on the mechanical and microstructural behaviour of friction stir welding (FSW) joints, specifically involving AA5052 and AA6082 aluminium alloys. Notably, copper foil (CF) is introduced as a novel element in the HP-LPB process to enhance the weld strength. The experiments were conducted with the tool rotational speeds (TRS) of 1000, 1100, and 1200 RPM, each paired with the welding speeds (WS) of 20, 25, and 30 mm min−1, and tool tilt angle (TTA) of 0°, 1°, and 2°. Mechanical behaviour is assessed through tensile testing along with microhardness measurements, revealing the advantages of HP-LPB with CF in enhancing joint strength and toughness. The microstructural analysis reveals the dissolution of precipitates, highlighting the influential role of copper foil in the improvement of joint efficiency. From the weld without a CF interlayer, a maximum tensile strength of 188 MPa was achieved at a TRS of 1200 RPM, WS of 25 mm min−1 and TTA of 2°. The post-processed FSW sample interlayered with copper foil, exhibited an improvement in joint efficiency by 87% at the optimum process parameter. This research demonstrates that the use of copper foil interlayers combined with HP-LPB treatment can substantially enhance the mechanical properties and structural integrity of FSW aluminum alloys, offering a valuable solution for advanced industrial applications.