Adjustment Of Process Parameters Research Articles

Microstructural stability and aging behavior of refractory high entropy alloys at intermediate temperatures

• Phase decomposition behaviors of RHEAs annealed at intermediate temperatures were explored. • The onset decomposition temperature is related to the elemental diffusion rates. • The ending decomposition temperature depends on the elemental melting points. • A new indicator for designing advanced RHEAs for practical application was proposed. Several body-centered-cubic (BCC) refractory high entropy alloys (HEAs), i.e., HfNbTaTiZr, NbTaTiZr, HfNbTiZr and NbTiZr, were annealed at intermediate temperatures for 100 h, and their microstructures and aging behaviors were studied in detail. All these HEAs start to decompose into multiple phases at around 500 °C, but reenter the single-phase region at significantly different temperatures which were determined to be 900, 1000, 1100 and above 1300 °C for HfNbTiZr, NbTiZr, HfNbTaTiZr and NbTaTiZr, respectively. Our analysis indicates that the onset decomposition temperature in these four HEAs is closely related to the elemental diffusion rates while the ending decomposition temperature is strongly dependent on the elemental melting points. Our findings are important not only for understanding phase stability of HEAs in general, but also for adjusting processing parameters to optimize mechanical properties of these HEAs.

Coffee Leaf Tea from El Salvador: On-Site Production Considering Influences of Processing on Chemical Composition.

The production of coffee leaf tea (Coffea arabica) in El Salvador and the influences of processing steps on non-volatile compounds and volatile aroma-active compounds were investigated. The tea was produced according to the process steps of conventional tea (Camellia sinensis) with the available possibilities on the farm. Influencing factors were the leaf type (old, young, yellow, shoots), processing (blending, cutting, rolling, freezing, steaming), drying (sun drying, oven drying, roasting) and fermentation (wild, yeast, Lactobacillus). Subsequently, the samples were analysed for the maximum levels of caffeine, chlorogenic acid, and epigallocatechin gallate permitted by the European Commission. The caffeine content ranged between 0.37–1.33 g/100 g dry mass (DM), the chlorogenic acid was between not detectable and 9.35 g/100 g DM and epigallocatechin gallate could not be detected at all. Furthermore, water content, essential oil, ash content, total polyphenols, total catechins, organic acids, and trigonelline were determined. Gas chromatography—mass spectrometry—olfactometry and calculation of the odour activity values (OAVs) were carried out to determine the main aroma-active compounds, which are β-ionone (honey-like, OAV 132-927), decanal (citrus-like, floral, OAV 14-301), α-ionone (floral, OAV 30-100), (E,Z)-2,6-nonadienal (cucumber-like, OAV 18-256), 2,4-nonadienal (melon-like, OAV 2-18), octanal (fruity, OAV 7-23), (E)-2 nonenal (citrus-like, OAV 1-11), hexanal (grassy, OAV 1-10), and 4-heptenal (green, OAV 1-9). The data obtained in this study may help to adjust process parameters directly to consumer preferences and allow coffee farmers to earn an extra income from this by-product.

Consequences of adjusting cell density and feed frequency on serum-free expansion of thymic regulatory T cells

BackgroundGiven the promising results from phase 1/2 clinical trials of therapy involving regulatory T cells (Tregs), it is critical to develop Treg manufacturing methods that use well-defined reagents. MethodsSeeking to maximize expansion of human thymic Tregs activated with anti-CD3/CD28 antibody-coated beads and cultured in serum-free medium, the authors investigated the effect of adjusting process parameters including cell density and cell concentration, and feeding strategy on Treg yield and quality. ResultsThe authors found that levels of expansion and viability varied with cell density on the day of restimulation. Tregs restimulated at low cell densities (1 × 105 cells/cm2) initially had high growth rates, viability and FOXP3 expression, but these parameters decreased with time and were less stable than those observed in cultures of Tregs restimulated at high cell densities (5 × 105 cells/cm2), which had slower growth rates. High-density expansion was associated with expression of inhibitory molecules and lower intracellular oxygen and extracellular nutrient concentrations as well as extracellular lactate accumulation. Experiments to test the effect of low oxygen revealed that transient exposure to low oxygen levels had little impact on expansion, viability or phenotype. Similarly, blockade of inhibitory molecules had little effect. By contrast, replenishing nutrients by increasing the feeding frequency between 2 days and 4 days after restimulation increased FOXP3, viability and expansion in high-density cultures. ConclusionThese data show the previously undescribed consequences of adjusting cell density on Treg expansion and establish a Good Manufacturing Practice-relevant protocol using non-cell-based activation reagents and serum-free media that supports sustained expansion without loss of viability or phenotype.

Strategies for adjusting process parameters in CAE simulation to meet real injection molding condition of screw positions and cavity pressure curves

Strategies for adjusting process parameters in CAE simulation to meet real injection molding condition of screw positions and cavity pressure curves

Study on the Weldability of Copper-304L Stainless Steel Dissimilar Joint Performed by Robotic Gas Tungsten Arc Welding.

The welding process of dissimilar metals, with distinct chemical, physical, thermal, and structural properties, needs to be studied and treated with special attention. The main objectives of this research were to investigate the weldability of the dissimilar joint made between the 99.95% Cu pipe and the 304L stainless steel plate by robotic Gas Tungsten Arc Welding (GTAW), without filler metal and without preheating of materials, and to find the optimum welding regime. Based on repeated adjustments of the main process parameters—welding speed, oscillation frequency, pulse frequency, main welding current, pulse current, and decrease time of welding current at the process end—it was determined the optimum process and, further, it was possible to carry out joints free of cracks and porosity, with full penetration, proper compactness, and sealing properties, that ensure safety in operating conditions. The microstructure analysis revealed the fusion zone as a multi-element alloy with preponderant participation of Cu that has resulted from mixing the non-ferrous elements and iron. Globular Cu- or Fe-rich compounds were developed during welding, being detected by Scanning Electron Microscope (SEM). Moreover, the Energy Dispersive X-ray Analysis (EDAX) recorded the existence of a narrow double mixing zone formed at the interface between the fusion zone and the 304L stainless steel that contains about 66 wt.% Fe, 18 wt.% Cr, 8 wt.% Cu, and 4 wt.% Ni. Due to the formation of Fe-, Cr-, and Ni-rich compounds, a hardness increase up to 127 HV0.2 was noticed in the fusion zone, in comparison with the copper material, where the average measured microhardness was 82 HV0.2. The optimization of the robotic welding regime was carried out sequentially, by adjusting the parameters values, and, further, by analyzing the effects of welding on the geometry and on the appearance of the weld bead. Finally, employing the optimum welding regime—14 cm/min welding speed, 125 A main current, 100 A pulse current, 2.84 Hz oscillation frequency, and 5 Hz pulse frequency—appropriate dissimilar joints, without imperfections, were achieved.

Finite element simulation of plasticity and fracture for Inconel 718 deposited by laser powder bed fusion – Chances, use and challenges

Laser Powder Bed Fusion (LPBF), is used to produce a component in layers by selective re-melting of a metal powder bed. A powder layer of an alloy is applied to a metal substrate within a process chamber filled with an inert gas. This powder layer is locally heated by a laser to a temperature above the melting point of the used alloy. Only regions of the applied powder which belong to the desired component are treated by the laser beam. After heating the layer is complete, the build platform is lowered by 30–90 µm and a new powder layer is applied. The procedure is repeated until the part is completed. After suitable post processing the part can be used. The relative density of the finished component is close to 100%. Depending on the deposition system the laser spot diameter measures approximately 40–80 µm, therefore the size of the resulting melt region is relatively small while cooling rates and temperature gradients are very high. Hence some resulting material properties of certain laser-melted alloys differ from the properties which can be achieved by conventional production routes. Furthermore, studies show that adjustment of certain process parameters can be used to influence the material condition, e.g., the anisotropy of the mechanical material properties. On the one hand this degree of freedom might offer opportunities to realize designed component properties, but on the other hand the modeling of the material condition and the resulting mechanical properties is challenging. A testing program covering different stress states as well as different material orientations has been performed at COMTES FHT. The tests revealed a significant orthotropy of plasticity and fracture which both are considered in the derived material card for the material model MF GenYld+CrachFEM – a material model developed by MATFEM. A good agreement between physical tests and finite element simulations of the performed tests was found. Chances and challenges in using the full potential of AM deposited materials based on detailed numerical modeling considering material orthotropic behavior are presented here for Inconel 718 and are supported by experimental testing.

Fast-growing cyanobacteria bio-embedded into bacterial cellulose for toxic metal bioremediation

Cyanobacterial biomass and cellulose-based materials have been used separately as green bio-adsorbents for the removal of toxic metals from water. Hybrid materials made of living microbial cells encased in a solid matrix have shown good potential for bioremediation. Here, the fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 was embedded in situ into bacterial cellulose (BC), a robust biopolymer rich in hydroxyl groups with excellent water holding capacity. The living material was obtained by injecting S. elongatus into a Komagataeibacter sucrofermentans culture producing BC. Several types of BC/S. elongatus (BC/SE) materials were developed including small spheroids and flat films with different cyanobacteria loads via simple adjustments of the biosynthesis process parameters. BC/SE spheroids were evaluated for toxic copper removal and exhibited excellent adsorption properties compared to pure BC with a maximum capacity of 156.25 mg g−1. Thus, this simple bio-embedding approach holds promises in the development of living materials for environmental applications.

Rapid and accurate detection of starch content in mixed sorghum by hyperspectral imaging combined with data fusion technology

AbstractSorghum is a type of brewing material. The starch content of different kinds of mixed sorghum affect the quality and yield of liquor. Therefore, an accurate and efficient detection of its starch content is of great significance to obtain high‐quality and high‐yield of liquor. Based on the data of single visible light (Vis), near‐infrared (NIR), and Vis and NIR fusion, this study develops a genetic algorithm optimization BP neural network (GA‐BPNN) model and a particle swarm optimization support vector machine model. It is deduced that the model based on the data from the Vis and NIR fusion has the highest performance in predicting the starch content of mixed sorghum. For the prediction of amylose, the GA‐BPNN model developed after using the Pearson correlation coefficient to select the characteristic wavelength for fusion, has the highest performance (RMSEP = .0298 and = .9948). For the prediction of amylopectin, the GA‐BPNN model developed after fusion of spectral features extracted by principal component analysis, has the highest performance (RMSEP = .0213 and = .9985). In conclusion, the hyperspectral imaging combined with data fusion can rapidly and accurately detect the starch content of mixed sorghum.Practical ApplicationsAs the single raw material of Maotai‐flavor liquor and the main raw material of Luzhou‐flavor liquor, the amylopectin content of sorghum directly affects the quality of liquor. Its amylose content also affects the yield of liquor. Because the different varieties of sorghum have different amylose and amylopectin content, liquor manufacturers often use several mixed varieties of sorghum as a brewing material. In order to ensure the quality and yield of liquor, it is particularly crucial to detect the amylose and amylopectin content variables of blended sorghum. The traditional sorghum starch content determination methods, such as the chemical detection methods and near infrared spectroscopy, have the disadvantages of destructiveness, low efficiency, and low detection accuracy. In the process of liquor brewing, the traditional detection methods cannot guide the timely adjustment of brewing process parameters. The hyperspectral imaging technology is widely used in food material detection, due to its fast and nondestructive advantages. The data fusion technology can fuse data from different sources of the same object to be detected, in order to obtain more comprehensive data for the development of accurate prediction models. This study uses the hyperspectral imaging combined with data fusion in order to quickly and accurately predicts the starch content (amylose, amylopectin) of mixed sorghum, which has a guiding significance for the timely adjustment of process parameters in the brewing process of liquor. In addition, it provides an accurate method for the component detection of other grains.

A novel oppositional binary crow search algorithm with optimal machine learning based postpartum hemorrhage prediction model

Postpartum hemorrhage (PPH) is an obstetric emergency instigated by excessive blood loss which occurs frequently after the delivery. The PPH can result in volume depletion, hypovolemic shock, and anemia. This is particular condition is considered a major cause of maternal deaths around the globe. Presently, physicians utilize visual examination for calculating blood and fluid loss during delivery. Since the classical methods depend on expert knowledge and are inaccurate, automated machine learning based PPH diagnosis models are essential. In regard to this aspect, this study introduces an efficient oppositional binary crow search algorithm (OBCSA) with an optimal stacked auto encoder (OSAE) model, called OBCSA-OSAE for PPH prediction. The goal of the proposed OBCSA-OSAE technique is to detect and classify the presence or absence of PPH. The OBCSA-OSAE technique involves the design of OBCSA based feature selection (FS) methods to elect an optimum feature subset. Additionally, the OSAE based classification model is developed to include an effective parameter adjustment process utilizing Equilibrium Optimizer (EO). The performance validation of the OBCSA-OSAE technique is performed using the benchmark dataset. The experimental values pointed out the benefits of the OBCSA-OSAE approach in recent methods.

Recent Progress on Regulating Strategies for the Strengthening and Toughening of High-Strength Aluminum Alloys.

Due to their high strength, high toughness, and corrosion resistance, high-strength aluminum alloys have attracted great scientific and technological attention in the fields of aerospace, navigation, high-speed railways, and automobiles. However, the fracture toughness and impact toughness of high-strength aluminum alloys decrease when their strength increases. In order to solve the above contradiction, there are currently three main control strategies: adjusting the alloying elements, developing new heat treatment processes, and using different deformation methods. This paper first analyzes the existing problems in the preparation of high-strength aluminum alloys, summarizes the strengthening and toughening mechanisms in high-strength aluminum alloys, and analyzes the feasibility of matching high-strength aluminum alloys in strength and toughness. Then, this paper summarizes the research progress towards adjusting the technology of high-strength aluminum alloys based on theoretical analysis and experimental verification, including the adjustment of process parameters and the resulting mechanical properties, as well as new ideas for research on high-strength aluminum alloys. Finally, the main unsolved problems, challenges, and future research directions for the strengthening and toughening of high-strength aluminum alloys are systematically emphasized. It is expected that this work could provide feasible new ideas for the development of high-strength and high-toughness aluminum alloys with high reliability and long service life.

Probabilistic Digital Twin for Additive Manufacturing Process Design and Control

Abstract This paper proposes a detailed methodology for constructing an additive manufacturing (AM) digital twin for the laser powder bed fusion (LPBF) process. An important aspect of the proposed digital twin is the incorporation of model uncertainty and process variability. A virtual representation of the LPBF process is first constructed using a physics-based model. To enable faster computation required in uncertainty analysis and decision-making, the physics-based model is replaced by a cheaper surrogate model. A two-step surrogate model is proposed when the quantity of interest is not directly observable during manufacturing. The data collected from the monitoring sensors are used for diagnosis (of current part quality) and passed on to the virtual representation for model updating. The model updating consists of Bayesian calibration of the uncertain parameters and the discrepancy term representing the model prediction error. The resulting digital twin is thus tailored for the particular individual part being produced and is used for probabilistic process parameter optimization (initial, before starting the printing) and online, real-time adjustment of the LPBF process parameters, in order to control the porosity in the manufactured part. A robust design optimization formulation is used to minimize the mean and standard deviation of the difference between the target porosity and the predicted porosity. The proposed methodology includes validation of the digital twin in two stages. Validation of the initial model in the digital twin is performed using available data, whereas data collected during manufacturing are used to validate the overall digital twin.

Strong robustness and high accuracy in predicting remaining useful life of supercapacitors

Remaining useful life shows extraordinary function in guiding the timely replacement of supercapacitors that reach the service life limit, which has great significance to the security and stability of the energy storage system. In order to more accurately predict the remaining useful life of supercapacitors so as to ensure the reliability of the whole supercapacitor bank, a temporal convolutional network is used. Among them, a residual block can solve the problems of gradient explosion and gradient disappearance, which are widespread in the recurrent neural network. Early stopping technology is used to avoid overfitting, and the Adam algorithm was used to optimize the process of parameter adjustment of the temporal convolutional network. The stability and accuracy of the model prediction were verified by using the capacity attenuation dataset of supercapacitors under different experimental conditions. Meanwhile, to verify the generalization ability of the model, the datasets of supercapacitors at different working conditions without training are input into the temporal convolutional network model. Simulation shows that the temporal convolutional network model exhibits strong robustness and high accuracy in predicting the remaining useful life of supercapacitors.

Process optimization via confidence region: a case study from micro-injection molding

In industrial research, experiments are designed to determine the optimal factor levels of the process parameters. Typically, experimental data are used to fit empirical models (for example, regression models) to derive one set of optimal conditions that maximize (or minimize) the response. Unfortunately, the optimization rarely provides a Confidence Interval for the location of the optimal solution, even though the optimal solution itself is subjected to variability. From a practitioner's point of view, identifying a region of possible optimal values provides high operational flexibility to adjust process parameters online during production. This paper provides a procedure for computing a confidence region for the optimal point based on experimental data, bootstrapping, and data depth. The procedure is validated using a case study from micro-injection molding, where the part weight is maximized under a constraint of the probability of flash formation. The proposed method considers that the objective function (part weight) and the constraint (probability of flash formation) are estimated from experimental data and subjected to sampling variability.

Virtual model predictive control of the inverter to achieve unbalance compensation for islanded and grid‐tied operations

Voltage unbalance is a frequent problem in the power system, and using renewable energy power generation devices to achieve unbalance compensation is a promising solution compared to adding additional equipment. Most of the existing unbalance compensation schemes for inverters are implemented based on cascaded linear controllers, which have obvious disadvantages of complex structure and cumbersome parameter adjustment process. In order to develop a simpler unbalance compensation scheme, this paper first derives the equivalent sequence network (ESN) model of the unbalanced three-phase power system, and it reveals the important role of transmission line impedance in the unbalance problem. Inspired by the ESN model, an unbalance compensation scheme based on negative sequence virtual impedance (NSVI) is determined. Then the NSVI is embedded in the inverter mathematical model, and the virtual model predictive control (VMPC) of the inverter to achieve unbalance compensation is proposed. Finally, the proposed VMPC method is validated by experiments, and a comparative analysis is carried out with linear control methods. The development process and validation results show that, compared with the unbalance compensation scheme based on cascaded linear controllers, the VMPC method proposed in this paper has a simpler structure and stable performance.

Mathematical modelling of flow field in 3-dimensional additive printing

The distribution of flow field caused by squeegee movement exercises a crucial influence over the transfer of aqueous polyurethane dispersion (PUD) in the 3-dimensional (3D) additive printing for warp knitted vamp. So, the current study's goal is to develop a mathematical model for flow field related to printing parameters, where analytical solution of squeegee deformation is crucial. This paper presents an analytical procedure to deduce the deformation equation of wedge squeegee with variable section based on the semi-inverse method from elastic mechanic. Then a mathematical model of 3D additive printing is obtained using lubrication theory, leading to non-dimensional velocity as well as the integral expression of hydrodynamic pressure that is solved by the Romberg integration algorithm. Further analysis reveals the similarities and differences between flow field studied in this paper and Couette flow. More importantly, the hydrodynamic pressure near the squeegee tip plays a crucial role in PUD transfer. Then, the sensitivity analysis is carried out to quantify the impacts of process parameters on hydrodynamic pressure. Among printing parameters, the printing angle has the greatest influence on hydrodynamic pressure, which is often neglected in traditional engineering studies. The theoretical calculations are compared against the simulations of finite volume method, showing good agreement for the distribution of velocity and pressure. This work paves the way for adjustment of process parameters and mechanism study of PUD transfer.

Controllable hydrophilic titanium surface with micro-protrusion or micro-groove processed by femtosecond laser direct writing

Smart surfaces with switchable wettability have aroused enormous interest, due to their multifunction in chemistry, biology and medicine. In this paper, a controllable hydrophilic titanium surface with either micro-protrusion or micro-groove is processed by femtosecond laser. The relationship between laser processing parameters of defocus value (Df), scanning speed (Vs), power (Po) and the shape/size of structures are systematically studied. Results show that Df and Vs mainly affect the shape of structures, while Vs and Po influence size. The surface structures can change from micro-groove to micro-protrusion, for the first time, by adjusting processing parameters. After laser processing, morphology and chemical composition are analyzed. It is found that the oxygen content in the middle area of micro-protrusion is higher than the recast layer of micro-groove. The formation mechanism can be attributed to the photo-thermal effect and Marangoni convection effect. Furthermore, the surface roughness and contact angle (CA) measurement results show that the surface roughness of micro-groove array is greater than micro-protrusion array, while micro-protrusion array can promote hydrophilicity compared with micro-groove array. Moreover, the micro-protrusion array with high scanning interval (△) can maintain the hydrophilicity better than micro-groove array even after 90 days. The selected area modification of femtosecond laser processing can potentially be applied to improve biological activity in titanium implants.

Numerical analysis on the effect of process parameters on deposition geometry in wire arc additive manufacturing

Here we develop a two-dimensional numerical model of wire and arc additive manufacturing (WAAM) to determine the relationship between process parameters and deposition geometry, and to reveal the influence mechanism of process parameters on deposition geometry. From the predictive results, a higher wire feed rate matched with a higher current could generate a larger and hotter droplet, and thus transfer more thermal and kinetic energy into melt pool, which results in a wider and lower deposited layer with deeper penetration. Moreover, a higher preheat temperature could enlarge melt pool volume and thus enhance heat and mass convection along both axial and radial directions, which gives rise to a wider and higher deposited layer with deeper penetration. These findings offer theoretical guidelines for the acquirement of acceptable deposition shape and optimal deposition quality through adjusting process parameters in fabricating WAAM components.

Critical assessment of methods for measurement of temperature profiles and heat load history in microwave heating processes-A review.

Limitations of microwave processing due to inhomogeneities of power input and energy absorption have been widely described. Over- and underheated product areas influence reproducibility, product quality, and possibly safety. Although a broad range of methods is available for temperature measurement and evaluation of time/temperature effects, none of them is sufficiently able to detect temperature differences and thermally induced effects within the product caused by inhomogeneous heating. The purpose of this review is to critically assess different methods of temperature measurement for their suitability for different microwave applications, namely metallic temperature sensors, thermal imaging, pyrometer measurement, fiber optic sensors, microwave radiometry, magnetic resonance imaging, liquid crystal thermography, thermal paper, and biological and chemical time-temperature indicators. These methods are evaluated according to their advantages and limitations, method characteristics, and potential interference with the electric field. Special attention is given to spatial resolution, accuracy, handling, and purpose of measurement, that is, development work or online production control. Differences of methods and examples of practical application and failure in microwave-assisted food processing are discussed with a special focus on microwave pasteurization and microwave-assisted drying. Based on this assessment, it is suggested that infrared cameras for measuring temperature distribution at the product surface and partially inside the product in combination with a chemical time/temperature indicator (e.g., Maillard reaction, generating heat-induced color variations, depending on local energy absorption) appear to be the most appropriate system for future practical application in microwave food process control, microwave system development, and product design. Reliable detection of inhomogeneous heating is a prerequisite to counteracte inhomogeneity by a targeted adjustment of process and product parameters in microwave applications.

Preparation of TiO[formula omitted] electron transport layer by magnetron sputtering and its effect on the properties of perovskite solar cells

Magnetron sputtering technology benefits from an easy process parameter adjustment strategy, good experimental reproducibility, and high-quality film formation; thus, it is widely used in thin-film electronic devices. In the present work, the thickness and morphology of the TiO2 electron transport layer were controlled by magnetron sputtering time, and based on this technology, planar heterojunction perovskite solar cells were prepared. The results showed that with an increase in the sputtering time, the surface roughness of the TiO2 film decreased, and the electrical homogeneity and square resistance of the film increased. Especially, the film transmittance showed a maximum value of 82.29% at 45 min of deposition, and the so prepared perovskite solar cells exhibited the highest photoelectric conversion efficiency of up to 12.42%.

Review on MRR in Spark Erosion Machining (SEM) Through ANN

EDM is a modern machining technology used to machine hard material pieces that are difficult to produce using traditional machining methods. This article aims to optimize process factors to obtain maximum MRR and high surface integrity in the end cut. Using artificial neural networks, this research proposes a technique for automatically determining and optimizing processing parameters in the EDM sinking process (ANN). The availability of machining data in the industrial tool room survey is the primary issue regarding adjusted process parameters for precision machining. Experiments are conducted to investigate the effect of pulse current, pulse on time, electrode area, and gap voltage on MRR response.