Selection Of Process Parameters Research Articles

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.

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

Wire arc additive manufacturing (WAAM) has attracted increasing interest in industry and academia due to its capability to produce large and complex metallic components at a high deposition rate. One of the basic tasks in WAAM is to determine appropriate process parameters, which will directly affect the morphology and quality of the weld bead. However, the selection of process parameters relies heavily on empirical data from trial-and-error experiments, which results in significant time and cost expenditures. This paper employed different machine learning models, including SVR, BPNN, and XGBoost, to predict process parameters for WAAM. Furthermore, the SVR model was optimized by the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. A 3D laser scanner was employed to obtain the weld bead’s point cloud, and the weld bead size was extracted using the point cloud processing algorithm as the training data. The K-fold cross-validation strategy was applied to train and validate machine learning models. The comparison results showed that PSO–SVR predicted process parameters with the highest precision, with the RMSE, R2, and MAE being 1.1670, 0.9879, and 0.8310, respectively. Based on the process parameters produced by PSO–SVR, an optimal process parameter combination was chosen by taking into comprehensive consideration the impacts of power consumption and efficiency. The effectiveness of the process parameter optimization method was proved through three groups of validation experiments, with the energy consumption of the first two groups decreasing by 10.68% and 11.47%, respectively.

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.

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.

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.

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.

Bayesian optimization for stable properties amid processing fluctuations in sputter deposition

We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.

Optimizing lyophilization primary drying: A vaccine case study with experimental and modeling techniques

In this study, we present the lyophilization process development efforts for a vaccine formulation aimed at optimizing the primary drying time (hence, the total cycle length) through comprehensive evaluation of its thermal characteristics, temperature profile, and critical quality attributes (CQAs). Differential scanning calorimetry (DSC) and freeze-drying microscopy (FDM) were used to experimentally determine the product-critical temperatures, viz., the glass transition temperature (Tg’) and the collapse temperature (Tc). Initial lyophilization studies indicated that the conventional approach of targeting product temperature (Tp) below the Tc (determined from FDM) resulted in long and sub-optimal drying times. Interestingly, aggressive drying conditions where the product temperature reached the total collapse temperature did not result in macroscopic collapse but, instead, reduced the drying time by ∼ 45 % while maintaining product quality requirements. This observation suggests the need for a more reliable measurement of the macroscopic collapse temperature for product in vials. The temperature profiles from different lyophilization runs showed a drop in product temperature following the primary drying ramp, of which the magnitude was correlated to the degree of macroscopic collapse. The batch-average product resistance, Rp, determined using the manometric temperature measurement (MTM), decreased with increasing dried layer thickness for aggressive primary drying conditions. A quantitative analysis of the product temperature and resistance profiles combined with qualitative assessment of cake appearance attributes was used to determine a more representative macro-collapse temperature, Tcm, for this vaccine product. A primary drying design space was generated using first principles modeling of heat and mass transfer to enable selection of optimum process parameters and reduce the number of exploratory lyophilization runs. Overall, the study highlights the importance of accurate determination of macroscopic collapse in vials, pursuing aggressive drying based on individual product characteristics, and leveraging experimental and modeling techniques for process optimization.

Theory-guided design of duplex-phase multi-principal-element alloys

Density-functional theory (DFT) is used to identify phase-equilibria in multi-principal-element and high-entropy alloys (MPEAs/HEAs), including duplex-phase and eutectic microstructures. A combination of composition-dependent formation energy and electronic-structure-based ordering parameters were used to identify a transition from FCC to BCC favoring mixtures, and these predictions experimentally validated in the Al-Co-Cr-Cu-Fe-Ni system. A sharp crossover in lattice structure and dual-phase stability as a function of composition were predicted via DFT and validated experimentally. The impact of solidification kinetics and thermodynamic stability was explored experimentally using a range of techniques, from slow (castings) to rapid (laser remelting), which showed a decoupling of phase fraction from thermal history, i.e., phase fraction was found to be solidification rate-independent, enabling tuning of a multi-modal cell and grain size ranging from nanoscale through macroscale. Strength and ductility tradeoffs for select processing parameters were investigated via uniaxial tension and small-punch testing on specimens manufactured via powder-based additive manufacturing (directed-energy deposition). This work establishes a pathway for design and optimization of next-generation multiphase superalloys via tailoring of structural and chemical ordering in concentrated solid solutions.

A Case Study: Electrically Assisted Stress Relief Annealing for Cold-Coiled Helical Automotive Springs.

This study experimentally investigated electrically assisted (EA) stress relief annealing for cold-coiled commercial automotive springs. In EA stress relief annealing, the temperature of a spring is rapidly increased to the annealing temperature (400 °C) and is held constant for a specified time using a pulsed electric current. Experimental findings show that the effectiveness of the EA stress relief annealing is superior to that of the conventional stress relief annealing, especially in terms of process time. The present study suggests that EA stress relief annealing, with properly selected process parameters, can effectively substitute for time-consuming conventional stress relief annealing using a furnace for cold-coiled automotive springs.

Investigation into hydraulic fracture propagation behavior during temporary plugging and diverting fracturing in deep coal seam

In this study, we conducted indoor temporary plugging and diverting fracturing (TPDF) experiments on samples of natural deep and shallow coal seams using a small-scale true triaxial fracturing simulation system. By integrating high-precision CT scanning technology and crack reconstruction techniques, and relying on the quantitative characterization of the crack complexity coefficient, we investigated the differences in internal structures between deep and shallow coal seam samples and the variations in the expansion of TPDF cracks. Furthermore, the study primarily focused on exploring the influence of temporary plugging agent (TPA) parameters (quantity and particle size) on the expansion patterns of TPDF cracks in deep coal seam coal-rock samples. The experimental results reveal that in shallow coal samples, artificially induced fractures are notably longer and extend to various surfaces of the samples. Conversely, in deep coal samples, the expansion of artificially induced fractures is influenced by well-developed cleavage and cleats. The crack complexity coefficient of artificial fractures in deep coal samples is 1.54 higher than that in shallow coal samples, with an increased crack width of 98 μm. However, the expansion distance of the fractures is shorter and does not extend to the S3 and S6 surfaces. Increasing the dosage of the TPA is advantageous for inducing frequent redirection of fractures and communication with previously untouched areas. When the dosage of the TPA is increased from 30 g/L to 60 g/L, the expansion distance of artificial fractures significantly increases, extending to various surfaces of the samples. The crack complexity coefficient increases by 1.1, and the crack width enlarges by 84 μm. Appropriate particle sizes of the TPA can effectively form seals. However, small particle sizes (200/400 mesh) struggle to seal initial fractures, while large particle sizes (10/20 mesh) can lead to excessive wellhead blockage, causing rapid shut-in pressure, destructive fracturing in deep coal samples, and the generation of a large amount of coal fines. This study holds significant guidance for the optimal selection of process parameters in the temporary plugging hydraulic fracturing of deep coal rock.

Sustainable formaldehyde-free copper electroless plating on carbon-epoxy substrates

In this paper, a formaldehyde-free copper electroless plating process was optimized using statistical analysis (Design of Experiment) in order to maximize the adhesion and the electrical conductivity when deposited on carbon-epoxy substrates. The coatings' morphologies, the electrical resistance, and the adhesion vary significantly as function of electroless plating parameters, but among the selected processing parameters, (pH of the electroless solution, temperature, concentration of reducing agent (glyoxylic acid), and concentration of complexing agent (EDTA)) only the temperature and the concentration of the reducing agent resulted to be influencing factors for the deposition process. A multi-objective optimization approach allowed to set the electroless process parameters in order to obtain optimized coatings presenting improved adhesion (scratch test’s critical load CL3 > 30 N) and electrical conductivity (Rs = 1.91 × 10–2 Ω/sq).

A study on the control of the printing accuracy in alumina parts by ceramic stereolithography

The printing accuracy in ceramic stereolithography is deteriorated by shrinkage due to photopolymerization of monomers and broadening owing to scattering of light. To improve the accuracy of printed alumina parts, a series of experiments involving the reduce of shrinkage and broadening were conducted. Firstly, the dependence of conversion of monomers, shrinkage of suspensions and warpage of parts on energy doses was quantitively investigated and correlated, with which the shrinkage was well controlled through optimization. Besides, the warpage problem was effectively resolved when adding 30 wt% PEG-200 in the suspensions to release the accumulated internal stress. Next, the broadening under different energy doses was observed and reduced by addition of absorbers, after which a self-defined parameter Cd/We was applied to suggest strategies in the selection of appropriate processing parameters and absorber content. On this basis, the holes with a diameter ranging from 50–200 μm were successfully printed.

Fabrication of Amisulpride Nanosuspension for Nose to Brain Delivery in the Potential Antipsychotic Treatment

ABSTRACT: Background: This research was aimed with the development of antipsychotic drug delivery for olfactory administration which could deliver drug to the brain. Amisulpride is a psychoactive drug that belongs to the benzamide derivatives class. It enhances dopaminergic neurotransmission by inhibiting presynaptic dopamine D2/D3 auto receptors selectively at lower dosages. Method: The nanosuspension was prepared by media milling technique for nose to brain delivery. The nose to brain delivery developed an effective route to bypass the BBB and deliver the drug to the brain. Factorial design was used for the designing and optimizing formulation based on various process and formulation factors. The optimized batch further analyzed to determine particle size, PDI, zeta potential, and drug content. With appropriate selection of process parameters like speed and bead amount. The media milling method is one of the effective methodology to reduce particle size and with the help of stabilizers nanoparticles could be stabilised. Result: The average particle size range of nanosuspension batch was observed 100-150 nm with a polydispersity index of 0.0927, Zeta potential +39.14 mV and drug content 88.12 ± 2 %. Conclusion: Intranasal administration is a promising alternative for bypassing the blood-brain barrier, reducing the adverse effects, and lowering the doses.