Selection Of Process Parameters Research Articles

Computational analysis of the simultaneous application of ultrasound and electric fields in a lipid bilayer

The combined application of electric fields and ultrasonic waves has shown promise in controlling cell membrane permeability, potentially resulting in synergistic effects that can be explored in the biotechnology industry. However, further clarification on how these processes interact is still needed. The objective of the present study was to investigate the atomic-scale effects of these processes on a DPPC lipid bilayer using molecular dynamics simulations. For higher electric fields, capable of independently forming pores, the application of an ultrasonic wave in the absence of cavitation yielded no additional effects on pore formation. However, for lower electric fields, the reduction in bilayer thickness induced by the shock wave catalyzed the electroporation process, effectively shortening the mean path that water molecules must traverse to form pores. When cavitation was considered, synergistic effects were evident only if the wave alone was able to generate pores through the formation of a water nanojet. In these cases, sonoporation acted as a mean to focus the electroporation effects on the initial pore formed by the nanojet. This study contributes to a better understanding of the synergy between electric fields and ultrasonic waves and to an optimal selection of processing parameters in practical applications of these processes.

A novel alloy design approach in developing CoNi-based high entropy superalloy using high entropy alloys thermodynamic and spark plasma sintering

This study introduces an innovative alloy design strategy by integrating HEAs principles to develop CoNi-based high entropy superalloys (HESAs). The powder of designed HESA produced using gas atomization and consolidated using spark plasma sintering (SPS). The alloy design strategy validated by CALPHAD to mitigate detrimental phase precipitation and segregation during powder production, material consolidation, and post-processing heat treatments. CoNi-HESA powder with a calculated mixing entropy of 1.568R was successfully developed and consolidated using SPS. Optimization of SPS parameters at 1175 °C, 10 min, and 100 °C/min achieved a relative density of 99.9 %. Subsequent heat treatments further improved the material's characteristics. Solutionizing at 1190 ± 10 °C for 2 h with water quenching resulted in a fine grain structure of ∼7 μm grain size. Aging at 900 °C for 24 h, followed by water quenching, yielded uniformly distributed fine γ′ precipitates comprising approximately 65 % of the γ matrix, without unwanted secondary precipitates or TCP phases. This study demonstrates the effectiveness of the novel approach in designing CoNi-based HESAs and optimizing their properties through careful process parameter selection and post-heat treatment cycles. The combination of thermodynamic calculations, HEA principles, and powder metallurgy techniques offers a promising approach for developing advanced high entropy superalloys with tailored microstructures.

Viscosity Calculation for Al‐Si‐Mg‐Fe System through CALPHAD Method

Viscosity is a crucial parameter affecting the fluidity of metal melts, which directly influences founding properties of Al alloys. However, obtaining viscosity measurement data is difficult for metal melts, and the viscosity prediction for ternary and multi‐component alloys would provide the significant data for selection of process parameters. This study compares the applicability of the Hirai model, SDS model, and R‐K function for viscosity calculations in the sub‐binary systems of Al‐Si‐Mg‐Fe alloys. R‐K function shows the best agreement with experimental data. However, the SDS model shows lower relative error than Hirai model which would play an important role in those system lacking experimental data to predict the viscosities. Ultimately, a database capable of predicting viscosity values for the entire composition and temperature range of the Al‐Si‐Mg‐Fe system was established using the CALPHAD method. Viscosity parameters for Al‐Si, Al‐Mg, Al‐Fe, Mg‐Si, and Mg‐Fe were evaluated and optimized through the R‐K derivation, corroborated with existing experimental data. Using the R‐K function, successful extrapolation and prediction of viscosity for the Al‐Si‐Mg‐Fe ternary and quaternary systems were achieved, with a mean square error between predicted and experimental values of only 0.7%, demonstrating the successful application of the Al‐Si‐Mg‐Fe database for viscosity prediction.This article is protected by copyright. All rights reserved.

Multi-objective optimization of high-power fiber laser cutting process using data augmentation-based ANN-Adam model

During fiber laser cutting of sheet metal, excessive heat-affected zone (HAZ) and recast layer thicknesses (RLT) can adversely affect subsequent precision machining and welding processes on the parts. As a result, it is necessary to optimize the process parameters to control the HAZ and RLT. In this work, laser power, gas pressure, focal plane position (FPP), and cutting speed are selected as input parameters. The cutting experiments are conducted using response surface methodology, and the factors that significantly affect the cutting quality are identified by analysis of variance (ANOVA). To improve the generalization ability and robustness of the model, a data augmentation-based artificial neural network (ANN) model is developed to achieve accurate prediction of cut quality characteristics. Further, this study performs a multi-objective optimization of the cutting process parameters using the Adam optimizer. The research results show that the mean relative errors of the ANN model in predicting the HAZ and RLT are 2.44 % and 1.55 %, respectively. The determination coefficients R2 are 0.9542 and 0.9779, respectively. The optimal combinations of process parameters obtained through optimization are laser power 8.9 kW, gas pressure 0.8 bar, FPP 11.8 mm, and cutting speed 1.3 m/min. Experimental validation shows that the relative errors between the predicted and the experiment values of the cut quality characteristics are kept within 5 % under the optimal process parameters. This fully verifies the effectiveness of the optimization model in guiding the selection of laser-cutting process parameters and improving cutting quality.

Artificial Intelligence Modeling of the Heterogeneous Gas Quenching Process for Steel Batches Based on Numerical Simulations and Experiments

High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production.

The Influence of Selected Process Parameters on the Efficiency of the Process of Gas Nitriding of AISI 1085 Steel.

The main aim of the manuscript was to investigate the impact of modifying the parameters of the gas nitriding process of samples made from AISI 1085 steel on the course and results of the process carried out in a chemical reactor allowing for thermogravimetric measurements. The tested steel was subjected in a chemical reactor to the process of gas nitriding in the temperature range of 490-580 °C, using different sample heating rates (in the range of 1-25 °C/min) and various mixtures of nitriding gases (pure NH3, or NH3 with the addition of H2 or N2). To assess the impact of the tested process parameters on its efficiency, the thickness of the nitrided layers produced, the change in sample mass, the structure of the phases produced, the phase composition and the microhardness were examined. For the research methodology used, it was found that reducing the amount of NH3 and/or using H2 or N2 admixtures adversely affects the thickness of the nitride layers produced. At the same time, the use of a lower maximum process temperature with the same gas mixture resulted in a significant difference in the thickness of the layers. It was also found that the use of pure NH3 or a gas mixture (NH3 + H2) with higher NH3 contents resulted in higher surface microhardnesses of the samples and that for these samples, the hardness increased to a greater depth.

The effect of fine particle peening on aviation gear performance, part II: Rolling contact fatigue

AbstractFine particle peening (FPP) technology offers an effective method to achieve outstanding surface integrity and fatigue performance in aviation gears. However, the lack of sufficient research on the correlation between process parameters, surface integrity parameters, and fatigue life undermines the full potential of FPP in strengthening aviation gears. Therefore, we have developed an elastic–plastic contact fatigue life model to predict the fatigue life of AISI 9310 gear steel after different strengthening processes. This model can consider surface integrity parameters such as surface micro‐topography, residual stress gradient, and hardness gradient. To validate the model, we conducted rolling contact fatigue tests and compared the results with the predictions from our model. The influence of process parameters on the rolling contact fatigue of AISI 9310 gear steel was further investigated. A process window for FPP was established, and an optimization method was proposed in order to provide guidance for selecting process parameters for FPP on high‐performance aviation gears.

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

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

Selection and Optimization of Carbon-Reinforced Polyether Ether Ketone Process Parameters in 3D Printing-A Rotating Component Application.

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Effect of in-situ activated core-shell particles on fatigue behavior of carbon fiber reinforced thermoplastic composites

In this unique study, the effect of adding core-shell particles (CSPs) on fatigue performance of carbon-fiber reinforced PA6 (CF-PA6) laminates is investigated. The thermoplastic laminates were prepared using compression molding and were reinforced at ply interfaces with 2 wt% and 4 wt% CSPs of the polymer mass. A manual method was used to disperse CSPs using a sieve and carefully selected process parameters. The cyclic tests were conducted and assessed, considering S–N curve, stiffness degradation, and energy dissipation. Consequently, the fatigue life of modified composites improved respectively by eight and four times when 2 wt% and 4 wt% CSPs were used. The results showed that an optimal improvement was achieved with a 2 wt% CSPs. The fatigue strength coefficient and fatigue strength exponent of CF-PA6 composites improved by 22.13 % and 9.85 %, respectively. The findings have the potential to establish a new frontier in thermoplastic research and would help designers to enhance the fatigue properties of thermoplastic laminates in specific elastic tailoring structures.

Casting of nickel powder based MMC using microwave energy

In this paper, an experimental setup is developed to cast a metal matrix composite (MMC) specimen, by using microwave energy as a heat source. A MMC (Nickel and 15 % of SiC by weight percentage) specimen of size 140 × 30 × 2 mm3 is in-situ cast by using a domestic microwave applicator at 2.45 GHz frequency, and 900 W, power output. As per selected process parameters, the exposure time to cast MMC specimen, inside an electromagnetic environment is optimized as 33 min. A COMSOL Multiphysics software tool is also used to model the in-situ microwave-assisted casting setup. Numerical simulations are performed to visualize the microwave heating effects within the applicator cavity. Microwave heating effects are analyzed in terms of electric field distribution, resistive heating and temperature profile. As per the simulation study results, maximum electric field intensity (4.46 × 104 (V/m)) is observed around the mold. Resistive heating losses are maximum (4 .9× 109 W/m3) in the susceptor zone. It is observed from the simulation study, that uniform heating of the specimen is observed throughout the cross-section and the melting temperature of the specimen is achieved in 38 min. A good correlation is observed between experimental and simulation study results in terms of exposure time of cast specimen and microwave heating effects within the applicator cavity

Mapping the roles of scan strategy and build orientation in predicting the crystallographic texture and yield strength of 316L stainless steel produced by laser powder bed fusion

Laser Powder Bed Fusion (L-PBF) has emerged as a leading additive manufacturing (AM) technique for manufacturing complex metal parts, with 316L stainless steel being favored for its exceptional corrosion resistance and mechanical properties. While previous studies have explored the effects of different scan strategies and build orientations on the microstructure and room temperature tensile properties of L-PBF printed 316L stainless steel, there is still a lack of a comprehensive predictive model that accurately captures the combined effects of these process parameters on the final crystallographic texture and tensile properties. This study presents a novel approach that establishes a direct correlation between process parameters, microstructure, and mechanical behavior by modeling the influence of scan strategy rotation angles and build orientations on the crystallographic texture of L-PBF-manufactured 316L stainless steel. To validate the model, a series of samples were fabricated using various scan strategy rotation angles and build orientations, enabling detailed microstructural analysis and mechanical testing. The findings demonstrate a direct relationship between the selected process parameters and the resulting mechanical properties, underscoring the importance of scan strategy and build orientation selection on mechanical response. Furthermore, our calibrated model exhibits high predictive accuracy as validated through a comparison with experimental data.

Comparing surface characteristics of Cu-Al-Fe alloys using thermal-based machining processes

The exceptional shape memory properties and load-handling capabilities of shape memory alloys makes them perfect for a spectrum of applications, such as actuators in robotics, control of aircraft wings and biomedical applications. Other than nickel-titanium shape memory alloy there are also various combinations of elements that show the same memory effect, namely Cu-Al-Fe, Cu-Al-Zn, and Cu-Al-Fe-Mn. However, a major problem often associated with shape memory alloys is the technical difficulty associated with machining them. Consequently, they do not tend to last longer when put to use. An optimal selection of input process parameters has a direct influence on the physical properties of these compounds. The traditional machining process, such as drilling, milling and grinding, pose a great threat to their properties. Hence, the research community has focused their study towards unconventional processes, such as (i) Wire Electric Discharge Machining, and (ii) Laser Beam Machining. In this research study, machining of the Cu-Al-Fe shape memory alloy was performed using three unconventional machining processes, namely (i) electric discharge machining (EDM), (ii) laser beam machining (LBM), (iii) powder-mixed electric discharge machining (PMEDM) and the effect of input process parameters were analysed. Various characterization tests provide useful information specific to changes in the microstructure and thermal behaviour of the alloy. The results show that the samples machined with PMEDM, EDM and LBM have surface roughness values of 2.377, 3.120 and 3.432 µm, respectively, which proves that PMEDM is best suited for machining the CuAlFe smart alloy.

Research on the calculation method of grinding temperature field of spiral bevel gear based on generative machining

At present, the process parameter setting of spiral bevel gear grinding is mainly based on an empirical method, and the tooth surface temperature is difficult to estimate effectively during grinding, which may lead to excessive tooth surface temperature and surface burn caused by improper selection of process parameters. Aiming at the above problems, this paper presents a method for calculating the temperature field of spiral bevel gear forming based on a combination of analytical and numerical methods. The motion equation of spiral bevel gear grinding is derived based on the method of tooth surface development. The time-varying heat flow calculation method in tooth surface grinding was developed through the discrete and two-dimensional contact zone. The time-varying temperature field analysis method of spiral bevel gear forming is developed using the time-varying heat flow and finite element calculation method. The related research can realize the temperature prediction of grinding tooth surfaces and provide the basis for improving grinding technology.

Atomistic analysis on implantation effects of hydrogen ions and copper ions into 4H-SiC

The microstructure evolution of 4H-SiC under the implantation of hydron ions and copper ions was investigated using molecular dynamics (MD) simulations and experimental analysis. MD models were developed to simulate the implantation of single Cu2+ and H+ ions into SiC, analyzing lattice damage, amorphous defects, and temperature distribution. H+ ion implantation resulted in a higher number of amorphous atoms compared to Cu2+ ions. The maximum atomic temperature during Cu2+ ion implantation was significantly higher (1635 K) than during H+ ion implantation (950 K), although the volume of the temperature rise region was larger in the H+ model. Cu2+ ions induced more severe lattice vibrations than H+ ions. Atom movement along regions of weak lattice binding energy was observed. The study also examined overlapping multiple ion implantations, considering ion type, initial energy, and incident angle. Cu2+ ions exhibited smaller sputtering yield, projection range, number of amorphous atoms, and energy increment compared to H+ ions. A 0° incident angle resulted in higher sputtering yield and increased amorphous structure formation due to the channeling effect, which was more pronounced in the H+ model. The experimental results demonstrate that compared to Cu2+ ions, H+ ions have a greater impact on the surface peak roughness, sub-surface damage depth, and surface damage of SiC samples. It can provide a reliable guidance for selecting process parameters and the types of ions to implant.

Parameter Tuning for Sustainable 3D Printing (3DP) of Clay Structures

Due to high carbon emissions, traditional construction methods are often costly, resource-intensive, and environmentally harmful. This study investigates the potential of additive manufacturing (AM) or 3D printing (3DP) using earthen clay as a sustainable alternative for building structures. Despite its promising benefits, challenges such as water/clay ratio, nozzle clogging, and temperature variations hinder widespread adoption. Employing the design of experiment (DOE) approach, this research systematically optimizes 3DP parameters, including nozzle diameter, layer height, infill percentage, and printing speed, using commercial earthen clay. Chemical compositions are analyzed using X-ray photoelectron spectroscopy (XPS) and energy-dispersive spectroscopy (EDS). Quantitative analysis compares measured dimensions of 3DP structures to assess dimensional accuracy, while buildability assessments explore structural integrity. Results indicate that lower layer heights generally enhance dimensional accuracy, with larger nozzle diameters enabling faster printing speeds. ANOVA analysis reveals significant relationships between printing parameters and dimensional outcomes, emphasizing the importance of 3DP process parameters selection. Optimal printing parameters lead to high-quality prints, although with limitations in scalability. This research contributes valuable insights for stakeholders seeking sustainable 3DP practices in construction, paving the way for more environmentally friendly and cost-effective building solutions.

Rotary friction welding of 304 stainless steel: parametric study, mechanical properties, and microstructure of the joint

In the rotary friction welding process, the selection of process parameters affects the friction, heat generation, and joint formation. These factors collectively cause microstructural changes that determine the mechanical properties of the joint. Therefore, the process parameters, microstructure, and mechanical properties were interconnected during rotary friction welding. This study examined the influence of process parameters on their correlation with microstructure and mechanical properties in the rotary friction welding of 304 SS. A 3×4 full factorial experimental design was used to evaluate the effects of the process parameters on the microstructure and strength of 304 SS joints produced through rotary friction welding. An accurate evaluation of joint strength was performed using the notch tensile test technique. The joint with the highest strength was achieved by applying a combination of friction pressure and friction time at 55 bars and 3 seconds, respectively, resulting in a welding efficiency of 103.6%. A very low friction time (i.e., 5 s) produced a weak joint, which should be avoided. The welding process created three distinct structural zones in the joint: a joint structure finer than the parent metal structure, a partially deformed structure, and a heat-affected zone with deformation. Hardness tests of the joints showed a high hardness in the deformed structure. The formed structure contributes to the resulting joint strength.

Shape memory behavior of 4D printed CF/PEKK high temperature composite under subsequent thermomechanical cycles

Shape memory effect (SME) in high temperature polymers (HTPs) has great significance in space for making self-deployable structures, needing high strength and stiffness during operation, signifying the necessity of exploring HTP composite with SME. This work investigates the SME of 4D printed carbon fiber/polyether ketone ketone (CF/PEKK) HTP composite under subsequent thermomechanical cycles for the first time. Scanning electron microscopy (SEM) revealed a uniform distribution of CFs in the developed composite, the extruded composite filament, and the composite prints with seamless raster and layer deposition, confirming a suitable selection of processing parameters. The CF/PEKK composite prints exhibited an excellent shape fixity, Rf = 90 % and 91 %, and shape recovery, Rr = 96 % and 89 % in the first and the tenth cycle, respectively. A significant loss of 7 % in Rr is observed in the tenth cycle. Glass transition (Tg) and degradation temperature (Td) of the CF/PEKK composite prints are observed to be 162 °C and 568 °C, while storage modulus is found 157.69 % and 38.69 % higher than the existing PEKK and PEEK, respectively. This study revealed an excellent SME of 4D printed CF/PEKK composite with outstanding Tg and Td, showing great potential for space applications.

Nitrogen alloyed austenitic Ni-free stainless steel for additive manufacturing

Nitrogen alloyed austenitic nickel-free stainless steel (ANFSS) is one of the most promising group of materials for consumer and healthcare products. They can be used to substitute not only conventional AISI 316L, but also more expensive titanium and Co-Cr alloys. Previously the utilization of nitrogen alloyed materials has been limited due to poor machinability resulting from high strength and ductility, and difficulties in manufacturing the nitrogen alloyed raw materials. Recent developments in powder metallurgy, for example, additive manufacturing (AM), offer feasible net shape manufacturing routes to go around machining and material related challenges. The present study investigates and introduces a viable processing route for an ANFSS by gas atomization, direct energy deposition (DED), and sintering. Special attention is paid to control the nitrogen content in different processing steps by investigating a Fe-16Mn-14Cr-0.27C-0.35N alloy with experimental and computational methods. The results show that the nitrogen content can be computationally estimated and maintained at desired level by proper selection of processing parameters, and thereby controlling the final nitrogen content of the ANFSS alloy.