Control Of Process Parameters Research Articles

Analysis and Characterization of Green Sand Mould Parameters for Understanding their Dynamic Behavior

Production of quality castings results from proper control of process parameters involved in casting. This study shows that the values of the selected green sand mold process parameters for an individual mold can be established by modeling and characterizing process parameters. Thus, proving the uniqueness of individual mold. This paper considers sand and melting parameters, which are traditionally assumed to be constant and the same over all the molds. However, this investigation has shown that the parameters are not constant but dynamic and change over time. The investigation establishes that no two molds are the same due to differences in their mold and melting parameters. The time series method has been incorporated into this study. Systematic predictive changes of the selected process parameters were observed, which can be utilized effectively for individual molds. Process parameters were recorded. A variation was found in the values of each parameter in different molds. Values can be obtained by regression analysis. Thus, this paper determines mold health by exclusively predicting the process parameter values of individual molds, which can be effectively correlated with the generation of casting defects.

Advancements in Surface Modification of NiTi Alloys for Orthopedic Implants: Focus on Low-Temperature Glow Discharge Plasma Oxidation Techniques

Nickel–titanium (NiTi) shape memory alloys are promising materials for orthopedic implants due to their unique mechanical properties, including superelasticity and shape memory effect. However, the high nickel content in NiTi alloys raises concerns about biocompatibility and potential cytotoxic effects. This review focuses on the recent advancements in surface modification techniques aimed at enhancing the properties of NiTi alloys for biomedical applications, with particular emphasis on low-temperature glow discharge plasma oxidation methods. The review explores various surface engineering strategies, including oxidation, nitriding, ion implantation, laser treatments, and the deposition of protective coatings. Among these, low-temperature plasma oxidation stands out for its ability to produce uniform, nanocrystalline layers of titanium dioxide (TiO2), titanium nitride (TiN), and nitrogen-doped TiO2 layers, significantly enhancing corrosion resistance, reducing nickel ion release, and promoting osseointegration. Plasma-assisted oxynitriding processes enable the creation of multifunctional coatings with improved mechanical and biological properties. The applications of modified NiTi alloys in orthopedic implants, including spinal fixation devices, joint prostheses, and fracture fixation systems, are also discussed. Despite these promising advancements, challenges remain in achieving large-scale reproducibility, controlling process parameters, and reducing production costs. Future research directions include integrating bioactive and antibacterial coatings, enhancing surface structuring for controlled biological responses, and expanding clinical validation. Addressing these challenges can unlock the full potential of surface-modified NiTi alloys in advanced orthopedic applications for safer, longer-lasting, and more effective medical implants.

Forming and compliance-free operation of low-energy, fast-switching HfOxSy/HfS2 memristors.

We demonstrate low energy, forming and compliance-free operation of a resistive memory obtained by the partial oxidation of a two-dimensional layered van-der-Waals semiconductor: hafnium disulfide (HfS2). Semiconductor-oxide heterostructures are achieved by low temperature (<300 °C) thermal oxidation of HfS2 under dry conditions, carefully controlling process parameters. The resulting HfOxSy/HfS2 heterostructures are integrated between metal contacts, forming vertical crossbar devices. Forming-free, compliance-free resistive switching between non-volatile states is demonstrated by applying voltage pulses and measuring the current response in time. We show non-volatile memory operation with an RON/ROFF of 102, programmable by 80 ns WRITE and ERASE operations. Multiple stable resistance states are achieved by modulating pulse width and amplitude, down to 60 ns, < 20 pJ operation. This demonstrates the capability of these devices for low-energy, fast-switching and multi-state programming. Resistance states were retained without fail at 150 °C over 104 s, showcasing the potential of these devices for long retention times and resilience to ageing. Low-energy resistive switching measurements were repeated under vacuum (8.6 mbar) showing unchanged characteristics and no dependence of the device on surrounding oxygen or water vapour. Using a technology computer-aided design (TCAD) tool, we explore the role of the semiconductor layer in tuning the device conductance and driving gradual resistive switching in 2D HfOx-based devices.

Biocompatible Heterogeneous Packaging and Laser-Assisted Fluid Interface Control for In Situ Sensor in Organ-on-a-Chip.

The development of bionic organ-on-a-chip technology relies heavily on advancements in in situ sensors and biochip packaging. By integrating precise biological and fluid condition sensing with microfluidics and electronic components, long-term dynamic closed-loop culture systems can be achieved. This study aims to develop biocompatible heterogeneous packaging and laser surface modification techniques to enable the encapsulation of electronic components while minimizing their impact on fluid dynamics. Using a kidney-on-a-chip as a case study, a non-toxic packaging process and fluid interface control methods have been successfully developed. Experimentally, miniature pressure sensors and control circuit boards were encapsulated using parylene-C, a biocompatible material, to isolate biochemical fluids from electronic components. Ultraviolet laser processing was employed to fabricate structures on parylene-C. The results demonstrate that through precise control of processing parameters, the wettability of the material can be tuned freely within a contact angle range of 60° to 110°. Morphological observations and MTT assays confirmed that the material and the processing methods do not induce cytotoxicity. This technology will facilitate the packaging of various miniature electronic components and biochips in the future. Furthermore, laser processing enables rapid and precise control of interface conditions across different regions within the chip, demonstrating a high potential for customized mass production of biochips. The proposed innovations provide a solution for in situ sensing in organ-on-a-chip systems and advanced biochip packaging. We believe that the development of this technology is a critical step toward realizing the concept of "organ twin".

Improving productivity of citramalate from CO2 by Synechocystis sp. PCC 6803 through design of experiment

BackgroundCyanobacteria have long been suggested as an industrial chassis for the conversion of carbon dioxide to products as part of a circular bioeconomy. The slow growth, carbon fixation rates, and limits of carbon partitioning between biomass and product in cyanobacteria must be overcome to fully realise this industrial potential. Typically, flux towards heterologous pathways is limited by the availability of core metabolites. Citramalate is produced in a single enzymatic step through the condensation of the central metabolites pyruvate and acetyl-CoA; improvements in citramalate productivity can, therefore, be used as a measure of overcoming this limitation. Furthermore, citramalate is a useful biomaterial precursor and provides a route to renewable methyl methacrylate and poly(methyl methacrylate), which is often traded as Perspex or Plexiglas.ResultsHere, we describe a phenomenon where the concerted optimisation of process parameters significantly increased citramalate production in Synechocystis sp. PCC 6803. Design of experiment principles were used to determine the optima for each parameter and the interplay between multiple parameters. This approach facilitated a ~ 23-fold increase in citramalate titre from initial unoptimised experiments. The process of scale-up from batch cultures to 0.5, 2, and 5 L photobioreactors is described. At the 2-L scale, citramalate titres from carbon dioxide reached 6.35 g/L with space–time yields of 1.59 g/L/day whilst 5-L PBRs yielded 3.96 ± 0.23 g/L with a productivity of 0.99 ± 0.06 g/L/day. We believe the decrease in productivity from 2-L to 5-L scale was likely due to the increased pathlength and shading for light delivery reducing incident light per cell. However, changes in productivity and growth characteristics are not uncommon when scaling up biotechnology processes and have numerous potential causes.ConclusionsThis work demonstrates that the use of a process parameter control regime can ameliorate precursor limitation and enhance citramalate production. Since pyruvate and/or acetyl-CoA give rise to numerous products of biotechnological interest, the workflow presented here could be employed to optimise flux towards other heterologous pathways. Understanding the factors controlling and thus increasing carbon partitioning to product will help progress cyanobacteria as part of a carbon–neutral circular bioeconomy. This is the first study using design of experiment to optimise overall carbon fixation rate and carbon partitioning to product, with the goal of improving the performance of a cyanobacterium as a host for biological carbon capture.

Effect of Elevated Temperature on Microstructure and Mechanical Properties of Hot-Rolled Steel

The mechanical properties and microstructure of hot-rolled steel are critical in determining its performance in industrial applications, particularly when exposed to elevated temperatures. This study examines the effects of varying temperatures and soaking times on these properties through a series of controlled experiments. The primary objective was to optimize the key response parameters, including tensile strength, yield strength, and elongation, by analyzing the influence of temperature and time. A full factorial design approach was used, applying the desirability function theory to explore all possible combinations and identify optimal processing conditions. The experimental results showed that the soaking time played a critical role, significantly influencing the mechanical properties with an impact ratio of 62%. The microstructural analysis displayed that higher temperatures and longer soaking times resulted in the formation of coarser ferrite and pearlite grains, contributing to a decrease in strength and an increase in ductility. The optimum process condition - 650 °C for 60 min - produced the highest values for tensile strength (400.32 MPa), elongation (36.78%) and yield strength (288.52 MPa). The study also highlighted the temperature-dependent nature of the mechanical behavior of hot-rolled steel. While tensile strength and yield strength initially increase with temperature, prolonged exposure, particularly at 600 °C and 750 °C, results in significant grain coarsening and a corresponding degradation of these properties. Conversely, elongation improves at moderate temperatures (150 °C to 300 °C) but decreases with prolonged exposure, especially at higher temperatures. These findings underscore the importance of precise control of thermal processing parameters to optimize the mechanical properties of hot-rolled steel. The findings offer significant insights that can be leveraged to optimize material performance in industrial applications, where thermal exposure is a critical consideration.

Study on the mechanism of liquid-phase regulated preparation of battery-grade iron phosphate

LiFePO4 batteries play a crucial role in energy storage and electric vehicles, with their precursor, FePO4, directly determining the electrochemical performance of LiFePO4. The key to preparing high-quality FePO4 is the precise regulation of crystal morphology. This study investigates the inter-ionic interaction of Fe3+ in a complex phosphate system to form monoclinic FePO4 with high crystallinity by precisely controlling process parameters such as pH and reaction temperature. The optimized process parameters are as follows: during the leaching stage, a P/Fe feeding ratio of 3:1 and a reaction temperature of 90 °C; during the oxidation stage, a 140 % excess of H2O2 and a reaction temperature of 50 °C; and during the crystallization stage, a pH of 1.5 and a reaction temperature of 90 °C, with an aging time of 1 h. The resulting FePO4 has a round cake morphology with a diameter of approximately 1.5 μm and a thickness of about 0.5 μm. The particle size distribution is narrow, with a D50 of 2.64 μm. The products exhibit consistent crystalline morphology, high crystallinity, an Fe content of 36.595 %, a P content of 20.676 %, and an Fe/P ratio of 0.981. The synthesized LiFePO4/C derived from this FePO4 shows a discharge capacity of 154 mAh/g at 0.2C. The proposed preparation mechanism has significant theoretical implications for the efficient and environmentally friendly production of FePO4 in the industry.

A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization

Perovskite solar cells (PSCs) have attracted significant attention due to their high power conversion efficiency (PCE) and affordability. However, optimizing the preparation parameters for PSCs is crucial. This study establishes a machine learning model incorporating a crude estimation of property (CEP) strategy to enhance prediction accuracy and precisely control process parameters. The model’s evaluation metrics improved by utilizing excess non-stoichiometric components (Ensc) and perovskite additive compounds (Pac) as CEP. Notably, the coefficient of determination (R2) on the test set increased by 16.14%, while the root mean square error decreased by 20.44%, respectively. Nine machine learning algorithms, including decision tree (DT), random forest (RF), CatBoost, LassoLarsCV, histogram gradient boosting, extreme gradient boosting (XGBoost), K nearest neighbor, ridge regression (Ridge), and linear regression (Linear R), were employed to optimize PSC preparation and assess its impact on device performance. The best-performing models, DT and RF, were combined to create a stacking model demonstrating the most stable overall performance on training and test sets. The study identified key process parameters affecting PCE based on the stacking model. Among these, adding Ensc was the most critical factor, followed by perovskite thickness, thermal annealing time (Ta-ti), perovskite deposition solvent (Pds), solvent mixing ratio, and Pac. Experimental verification showed that PSCs with a 10% excess of PbI2 exhibited higher PCE compared to those with 5% excess, confirming that adding Ensc can effectively enhance PCE. These findings offer a valuable reference for optimizing PSC process parameters and improving performance, thereby saving time and labor costs.

Optimisation of dry sliding wear characterisation of as-cast and T6 treated hypereutectic aluminium alloy (AlSiCuCe) by Taguchi technique

ABSTRACT To find the optimum levels of controlling and dominating factors on the wear behaviour and COF of as-cast and T6-heat-treated Al.18Si.3.6Cu.0.34Ce composites, the Taguchi technique was applied. A pin-on-disc setup was used for the wear study by considering the controlling variables like speed(m/s) and load(N) at four levels. An analysis of variance was performed to identify the controlling process parameters and their influence on the objective function. From ANOVA analyses of wear, based on the S/N ratio, the load dominates both the VWR and temperature, while the frictional force is influenced by the speed. Linear regression models are developed and are used for confirmation tests. A good agreement was found between the experimental and predicted values, and the error is within 10%.

Enhancement of magnetostriction in laser powder bed fusion Fe-Ga alloy by texture formation

Magnetostrictive Fe–Ga alloys have received much attention in sensing and harvesting applications owing to their outstanding mechanical and magnetostrictive properties. The largest magnetostriction in Fe–Ga is found along the <100> direction. Utilising additive manufacturing (AM) for preparation of Fe–Ga will expand the potential to manufacture complex shapes and geometries, if the strong <100> texture is formed. This study investigated the saturation magnetostriction of a polycrystalline Fe–Ga alloy prepared by laser powder bed fusion (L-PBF) under controlled processing parameters. The results revealed that the combination of a narrow hatch space and laser remelting treatment significantly enhances the apparent magnetostriction of the L-PBF Fe–Ga alloy. Notably, at a hatch space of 40 μm, the highest saturation magnetostriction is around 70 ppm. This value is almost three times higher than that of a polycrystalline Fe–Ga alloy with the same chemical composition where grains are randomly oriented.

Physics-Based and Data Driven Modeling of Electrophoretic Coatings: Hybrid and Multiphysics Strategy

Electrophoretic coatings are used in several applications where surface treatment is intended to provide corrosion protection. These coatings are widely used in the automotive, aerospace, or many other sectors. These kind of coatings are electrochemically applied and have good levelling, penetration, adhesion, and corrosion resistance properties. A wide variety of materials can be deposited by this technique and among these coatings, this presentation will focus on ceramic deposits. In this case, electrophoretic deposition is carried out using a suspension containing ceramic particles and the deposition takes place on a conductive substrate through the application of an electric field. The control of process parameters and post-processing parameters (heat treatment or sintering) necessary to consolidate the raw coatings obtained can be difficult to define.In order to improve process management and understanding of electrophoretic deposition, we will explore several strategies and modelling theories implemented in the field of electrophoretic coating through a comprehensive analysis in order to optimize the coating process, improve quality and increase efficiency on complex geometry parts. Several theoretical approaches and models have been used to better understand electrochemical interactions and forces involved in electrophoresis to identify parameters related to electrophoretic mobility. The multiple parameters involved in electrophoretic coatings make the modelling of this process complex and the models developed are neither universal nor applicable to all chemistries which requires a specific development for each electrolyte and suspension.In a second part, we discuss limits of theoretical models and describes a strategy to develop a new modelling approach. This approach combines a physics-based model of current distribution and a data-driven optimization applied to electrophoretic coating. This methodology allows to model the thickness distribution on a part with complex geometry on an industrial scale, with the ability to adapt to a variation in the chemistry of the suspension used. This hybrid methodology allow to reduce experimental and fundamental characterizations, and can be easily applied to a new chemistry to estimate and well describe the throwing power at a macroscale.Furthermore, in some cases, physics-based modeling with secondary distribution can exhibit accuracy defects when the local concentration of particles drops, due to their consumption and low mass transport phenomena.In order to better describe the process behavior in this specific case, we develop a multiphysics model of tertiary current distribution than can account for the local distribution of particles, particularly near the part. This modeling approach allows to better anticipate thickness distribution in areas with low fluid mixing and agitation. The local efficiency is linked to the local current and particle concentration, as describe by a two phase flow model.The models developed are intended to help accurately predict and control the distribution of coating thickness and also local particle concentration to predict critical depletion zones.In conclusion, this conference explore different modeling strategies of electrophoretic coatings, by focusing on the optimization of process parameters and the consideration of the different mechanisms governing deposition with the objective of improving coating distribution on complex part.

Development of Nasicon-Type Electrolytes Using Combination of Experimental Techniques and Materials Informatics

Introduction Research and development of large lithium-ion batteries is essential for new societal infrastructures such as electric vehicles, aircraft and smart grids. Replacing the liquid electrolyte of conventional lithium-ion batteries with solid electrolytes not only makes them less prone to ignition, but also increases their energy density. Sulfide, chloride and oxide systems are known as solid electrolytes. Sulphide and chloride types have high lithium-ion conductivity but are generally unstable in air. Therefore, there is an urgent need to develop oxide-based solid electrolytes that are stable in air, and the Na superconductor (NASICON) type LiZr2(PO4)3 (LZP) is a candidate oxide-based solid electrolyte for all-solid-state lithium-ion batteries. However, its ionic conductivity is inadequate. We have therefore investigated co-doped LZP (Li1+x+2yCayZr2-ySixP3-xO12), in which Zr is partially replaced by Ca and P by Si. First, a total of 49 compositions were synthesized and their crystal structure, relative density and lithium-ion conductivity were experimentally evaluated [1]. Since lithium-ion conductivity is also affected by microstructural properties such as density, morphology and elemental distribution of the sintered pellets, the conductivity can be improved by controlling process parameters such as heating conditions during the solid-state reaction. Therefore, the material was synthesized in a solid-state reaction under different heating conditions in two stages and the effect of the heating conditions on the microstructural properties of the sintered pellets was investigated [2].Such thorough sample preparation requires considerable time, effort and cost, and consumes a large amount of energy and resources. Therefore, AI-based optimization methods called Bayesian Optimization (BO) have attracted attention. Bayesian optimizations is a method that uses statistical inference to select the next candidate to explore and find the best performing sample with the least number of samples. Using the experimental data obtained in our study, we demonstrated whether the BO algorithm can 'efficiently' find the optimal composition and heating conditions to achieve the highest ionic conductivity of such materials. Experimental Li1+x+2yCayZr2-ySixP3-xO12 was synthesized by a conventional solid-state reaction using two-stage heating. The crystalline phase of the samples was determined by X-ray diffraction (XRD). The micro morphology of the samples was evaluated by scanning electron microscopy. The ionic conductivity of the pellets was measured by AC impedance.BO was performed to evaluate the efficiency of the optimized composition or heating conditions to obtain the highest ionic conductivity of the experimentally evaluated samples. Conductivity at 30 °C was the objective. Results and discussion The Li1.45Ca0.15Zr1.85S0.15P2.75O12 sample heated at 1050°C and 1250°C gave the highest lithium-ion conductivity of 3.3 x 10-5 S/cm at 30°C. Investigation of the sintered samples showed that composition and heating conditions affected the crystalline phase, density, microstructure and elemental distribution. The relationship between ionic conductivity and these observations was so complex that it was difficult to determine the optimum composition and heating conditions intuitively from a few experiments. Figure 1 shows the number of experiments required to find (a) the optimal composition and (b) the optimal heating conditions with 80%, 90% and 100% probability using BO or random search. The results show that using BO can find the optimum composition or optimum sintering conditions in about half the number of trials compared to a random search. Thus, Bayesian optimization has been shown to be effective in finding the optimum composition or process conditions of a material.

Surface Roughness Control of Powder Bed Fused AlSi10Mg Alloy with Various Building Angles

Powder Bed Fusion (PBF) is advantageous for manufacturing complex shapes of metal products, so that its demand is increasing in various industrial fields. PBFed metal parts have a rough surface due to metal powders being partially melted and attached to the surface during the process. The surface roughness of the PBFed parts varies considerably according to the building angle. The rough surfaces with partially melted metal powder reduce dimensional accuracy limiting the performance of parts, so that some post-treatments are necessary to satisfy the designed part dimension of metal parts. Various surface post-treatment processes including sandblasting, shot peening, laser polishing, and chemical polishing are applied to PBFed parts to reduce surface roughness. However, these processes have limitations for complex shapes and to precisely control dimensional changes. On the other hand, electropolishing (EP) which is an electrochemical surface post-treatment process is not restricted by the shape of the workpiece. In addition, it is possible to control dimensional changes by controlling process parameters such as applied voltage and treatment time. Thus, the electropolishing can be a promising candidate for the post-treatment of PBFed metal parts. Even though various conventional EP solutions and processes have been introduced to control the surface roughness, effects on dimensional changes, which is essential for PBFed metal parts, were not widely explored. Therefore, it is necessary to explore EP processes to reduce the surface roughness of PBFed parts, while achieving a high dimensional accuracy. In this study, we studied various EP solutions for PBFed AlSi10Mg alloy, widely used in light metal parts, considering both surface roughness and dimensional change. The aim of this work is to find the optimum electropolishing condition to reduce roughness effectively while minimizing the dimensional change. Several electrolytes that are conventional used in EP of aluminum and its alloy were selected and applied to PBFed AlSi10Mg. Based on these electrolytes, a new electrolyte was also devised. The thickness and surface roughness change before and after EP according to the applied voltage and treatment time was analyzed to derive the optimum conditions. The optimum EP conditions were applied to the PBFed AlSi10Mg alloy manufactured with building angles of 45, 60, and 90˚ and investigated the reduction rate in surface roughness according to the building angle.

Resistivity Engineering of Atomic Layer Deposited Tungsten Carbonitride Thin Films Via Carbon Concentration Control for Cross-Point Memory Electrodes Applications

As the demand for data storage and processing continues to rise exponentially, the development of phase change memory (PCM) has gained significant attention. In particular, three-dimensional (3D) vertical cross-point (VXP) array architecture is one of the most promising solutions for PCM fabrication, offering increased integration density and cost-effectiveness compared to the conventional planar memory structures, realized by atomic layer deposition (ALD) due to its excellent controllability and conformality.1) However, the implementation of a 3D VXP structure necessitates selectors, such as an ovonic threshold switch (OTS), to suppress sneak current from unselected cells. The switching behavior of the OTS selector depends heavily on electrode materials, constituting an essential part of the OTS device.2) Thus, detailed engineering of electrode material is required to obtain desirable properties for 3D VXP. Electrodes for 3D VXP require moderate resistivity because of the trade-off between thermal efficiency and power consumption. Moreover, the resistivity of film can be shifted by carbon incorporation. In this regard, we controlled the resistivity of the electrode with varying carbon concentration of the film utilizing ALD technique. In this study, we focus on the development of thermal ALD tungsten carbonitride electrodes for 3D VXP arrays. Tungsten nitride is mainly used as an electrode due to its high thermal stability and conductivity. We present a novel ALD process with intentional carbon control of tungsten nitride to change film properties, including resistivity, by adjusting process parameters. Through systematic control of ALD process parameters, we investigated the impact of carbon concentrations on film characteristics and subsequently analyzed how these variations influence the electrical properties of OTS devices. This research opens the possibility of tuning electrode characteristics in detail, ultimately enhancing the overall electrical performance of the next-generation cross-point memory devices.

Controlled Formation of Porous Cross-Bar Arrays Using Nano-Transfer Printing.

Nano-transfer printing (nTP) has emerged as an effective method for fabricating three-dimensional (3D) nanopatterns on both flat and non-planar substrates. However, most transfer-printed 3D patterns tend to exhibit non-discrete and/or non-porous structures, limiting their application in high-precision nanofabrication. In this study, we introduce a simple and versatile approach to produce highly ordered, porous 3D cross-bar arrays through precise control of the nTP process parameters. By selectively adjusting the polymer solution concentration and spin-coating conditions, we successfully generated discrete, periodic line patterns, which were then stacked at a 90-degree angle to form a porous 3D cross-bar structure. This technique enabled the direct transfer printing of PMMA line patterns with well-defined, square-arrayed holes, without requiring additional deposition of functional materials. This method was applied across diverse substrates, including planar Si wafers, flexible PET, metallic copper foil, and transparent glass, demonstrating its adaptability. These well-defined 3D cross-bar patterns enhance the versatility of nTP and are anticipated to find broad applicability in various nano-to-microscale electronic devices, offering high surface area and structural precision to support enhanced functionality and performance.

Modeling and Parametric Study of Spent Refractory Material Dissolution in an Aluminum Reduction Cell

Utilizing spent refractory material (SRM), generated after the overhaul of aluminum electrolytic cells, as a raw material for producing Al-Si alloys presents an efficient approach towards achieving full resource utilization of SRM. However, a bottleneck restricting this technology has become the dissolution of SRM. Based on the heat and mass transfer mechanism, the shrinkage core model of SRM particle dissolution was established. The effects of alumina concentration, silica concentration, electrolyte superheat, particle temperature, and turbulent kinetic energy dissipation rate on the mass dissolution rate and dissolution time of SRM particles were investigated. Calculation results and experimental data were compared to confirm the accuracy of the established model. The results show that by maintaining low alumina and silica concentrations, increasing the electrolyte superheat and particle preheating temperature, and increasing the electrolyte turbulent kinetic energy dissipation rate, SRM particles can dissolve faster. The dissolution of agglomerated particles is greatly influenced by the turbulent kinetic energy dissipation rate and superheat. The present research provides promising guidance for practical application in predicting particle dissolution time, controlling process parameters, and accelerating the dissolution of SRM particles.

Floating potential probes for process control during reactive magnetron sputtering

Long term deposition of aluminum oxide by reactive magnetron sputtering results in a drift of the discharge voltage. A similar drift of the floating potential is observed. The latter observation is further investigated and it is shown that the change of the floating potential can be linked to a change of the electric properties of the vacuum chamber walls. Optimization of the floating potential probe is performed to use the difference between the discharge voltage and the floating potential as a parameter for process control. The best results were obtained with a planar probe sufficiently far positioned opposite to the magnetron, and close enough to the chamber wall facing the magnetron. This choice can be understood in terms of the ability of the probe to sense the discharge while being protected from deposition. The usage of the aforementioned difference to control the process is demonstrated with the measurement of process curves, more specifically IV-characteristics and hysteresis experiments. The shown reproducibility of these measurements opens a pathway for more precise quantification of reactive sputter deposition simulations and enhanced process control.

PROSPECTS FOR THE USE OF MICROARC OXIDATION IN THE PRODUCTION OF AVIATION AND AUTOMOTIVE COMPONENTS

This article discusses the prospects for the application of microarc oxidation (MAO) technology in the aviation and automotive industries. The MAO process allows the creation of durable, wear-resistant and corrosion-resistant coatings on the surface of metal parts, which significantly increases their durability and performance characteristics. Technological aspects of MAO are discussed, including surface preparation features, selection of electrolytes and control of process parameters. Examples of successful application of MAO in the production of aviation and automotive components are given. Also, directions for further research and development of the technology are explored, including the creation of new materials, combined coatings and the study of the durability of coatings under real operating conditions. The article emphasizes the importance of MAO as a promising technology for improving the reliability and efficiency of vehicles and equipment.

Fuzzy assessment of process parameter interactions on warpage defect modeling in plastic injection molding

AbstractStudying the interactions among major plastic injection molding process parameters is necessary to understand how they collectively influence major defects such as warpage hence enabling optimization of the process for improved product quality. Existing process parameter interaction studies have used statistical approximations, which have limitations such as reduced predictive power and limited accuracy. To overcome these limitations, this study presents an alternative method of analysis of the interactions among process parameters based on fuzzy logic intelligent algorithm. Through computer aided engineering, factorial design of experiment and fuzzy logic modelling, the study evaluated the effects of major injection molding process parameter interactions on warpage. The results obtained indicated a general increase in warpage with increase in parameters such as melt temperature, mold temperature, injection pressure and cool time whereas an increase in parameters such as ambient temperature and packing pressure decreased warpage. Parameter interactions were obtained both statistically and based on fuzzy logic model and their significance tested through ANOVA. Ambient temperature (30.6%) and melt temperatures (18.7%) had the greatest effect on warpage all with P-values of 0.000 whereas cooling time (0.1%) had the least effect with P-value of 0.250. The largest two way interaction affecting warpage involved melt temperature and cooling time with a contribution of 12.2% whereas the largest three way interaction involves ambient temperature, packing pressure and injection pressure with a contribution of 2.7%. Also, despite cooling time having the least mains effect, most interaction terms with greater effect on warpage involved cooling time and melt temperature. The results from this study provides an insight on targeted injection molding process parameter control for defect minimization.

The sulfurization precipitation and competition mechanisms of Cu(II) and As(V) in electrolyte towards efficient recovery of copper

The sulfide precipitation has been widely used to recover Cu(II) and remove As(V) in copper smelting electrolyte based on the Cu(II)-As(V) separation. However, the recovery efficiency was undesirable due to the inaccurate control of process parameters. It was necessary to investigate the process and mechanisms of sulfurization precipitation for accurate adjustment. Herein, under the single and mixed solutions of Cu(II) and As(V), the process parameters were investigated and the precipitation mechanisms were explored. And when using actual electrolytes, the corresponding sulfurization process and mechanisms were also revealed. The sulfurization results of single component solutions suggested that Cu(II) was rapidly precipitated in one step, but As(V) firstly generated intermediate, then it was gradually reduced to As(III) for a longer time and quickly precipitated in the form of amorphous. This phenomenon caused the significantly faster removal speed of Cu(II) than that of As(V). For their mixed solutions, only a small amount of As(V) were removed by adsorbing the precipitate at the low S/Cu molar ratios (RS/Cu), and Cu(II) and As(V) could achieve a good separation. Simultaneously, it revealed that the sulfidation competitive mechanisms were related to the solubility product constant (Ksp) and initial As(V) concentrations. In addition, the similar rules were also observed in actual electrolytes. Moreover, under RS/Cu = 1.2 and reaction time of 10 min, the arsenic content of sulfide precipitates in the actual electrolyte was less than 2.5%, which showed a great separation also could be obtained for the actual electrolyte. This result met the requirement of Cu(II) recovery. It can provide a good reference for adjusting sulfidation parameters in the actual process to achieve efficient copper recovery, reduce the production of hazardous waste containing arsenic at the source and realize the cleaner production of copper smelting system.