Selection Of Process Parameters Research Articles

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

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 2wt% and 4wt% 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 is improved respectively by eight and four times when 2wt% and 4wt% CSPs are used. The results showed that an optimal improvement was achieved with a 2wt% CSPs. The fatigue strength coefficient (FSC) and fatigue strength exponent (FSE) 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.

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.

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).

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.

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.

Recovery of Omega-3-Rich Lipids: Toward the Sustainable Valorization of Sea-Bass Industry Side Streams

This study pertains to the selection of the appropriate process parameters of lipid extraction aimed at the valorization of sea-bass gutting and filleting side stream biomass. A comparative kinetic study between an environmentally friendly polar (ethanol) and a conventional non-polar solvent (hexane) was implemented to determine the effect of temperature, solvent to solid ratio and time, providing insights in terms of the yield recovery and quality of omega-3 fatty acids. Maximum recovery yield (43.8 and 34.0 g/100 g of dried gutting and filleting by-products, respectively) was achieved within 10 min. In gutting by-products, ethanol achieved full lipid recovery at 35 °C and a 50:1 ratio, while for hexane, the maximum yield was 83%. In the case of filleting side-streams, hexane achieved exhaustive recovery at a 50:1 ratio and 20 °C compared with ethanol, where a higher temperature (50 °C) was needed for exhaustive recovery to be achieved. Differences were attributed to the diverse fatty acid profiles among the two by-products, with 27.3% and 40.8% polyunsaturated fatty acids in viscera and fish frames, respectively. All extracts at 20 °C and 35 °C remained below the oxidation limit set by the Codex Alimentarius Commission (peroxide value < 5 meqO2/kgoil; p-anisidine value < 20). Therefore, green solvents could assist conventional extraction, combining high efficiency and an improved environmental impact.

Enhancing the sensitivity of 3D printed sensors via ironing and void reduction

ABSTRACT Material Extrusion (MEX) Additive Manufacturing (AM) has risen as a promising technology to monolithically manufacture smart structures with embedded sensors. Despite all the benefits of MEX AM, 3D printed piezoresistive sensors still suffer from low sensitivity, making them unsuitable for the detection of low values of force, displacement and bending angle. In the present paper, a simple, effective, and inexpensive method to increase the sensitivity in 3D printed sensors is proposed, based on the leveraging of the ironing strategy, which resulted in an improvement in the sensitivity of 83% compared to traditional process parameter selection. The ironing strategy reduced intralayer porosity by 59%, as verified by X-Ray CT. Additionally, the ironing strategy involves an increased healing time, which promotes the polymer chain diffusion between layers, which translated into a greater stability of the sensor when cyclically stressed. Smart structures capable of detecting small forces (0.19 N of resolution against 1.96 N for traditional MEX scenario) and smart auxetic devices have been manufactured, demonstrating the potential of the proposed approach. The present research demonstrates the ability to reduce interlayer voids by using an intrinsic feature of the MEX process and consequently improve electrical performance of 3D printed sensors.

Stability Analysis of Milling Based on the Barycentric Rational Interpolation Differential Quadrature Method

Chatter causes great damage to the machining process, and the selection of appropriate process parameters through chatter stability analysis is of great significance for achieving chatter-free machining. This article proposes a milling stability analysis method based on the barycentric rational interpolation differential quadrature method (DQM). The dynamics of the milling process considering the regeneration effect is first modelled as a time-delay differential equation (DDE). When adjacent pitch angles of the milling cutter are symmetric, the milling dynamic equation contains a single time delay. Otherwise, when adjacent pitch angles are asymmetric, the dynamic equation contains multiple time delays. The barycentric rational interpolation DQM is then used to approximate the differential and delay terms of the milling dynamics equation, and to construct a state transition matrix between adjacent milling periods. Finally, the chatter stability lobe diagram (SLD) is obtained based on the Floquet theory. According to the SLD, the appropriate spindle speed can be selected to obtain the maximum stable axial depth of cutting, thereby effectively improving the material removal rate. The accuracy and efficiency of the proposed method have been validated by two widely used milling models, and the results show that the proposed method has good accuracy and computational efficiency.

Study on Temperature Field Uniformity of Dynamic Induction Heating for Camshaft of Marine Diesel Engine

This paper focuses on the study of the induction heating process of a camshaft in a marine diesel engine. A three-dimensional finite element model for dynamic induction heating is established using the finite element method of multi-physical field coupling, aiming to investigate the temperature uniformity of the cam during this process. Three elements are analyzed in this study: the moving speed, the gap between the induction coil and the workpiece, and the width of the induction coil. These factors allow for an analysis of the temperature distribution in the thickness direction and contour line direction of the cam under various conditions. On this basis, an equivalent parameter about the temperature uniformity in the thickness direction of the cam is proposed to guide the selection of the camshaft induction heating process parameters.

Optimization of natural rubber foams: Effect of foaming agent content and processing conditions on the cellular structure and mechanical properties

In the past decades, natural rubber (NR) foams became popular in the automotive, construction and aerospace industries because of their lightweight, flexibility and shock-absorbing properties. The selection of optimal formulation and processing parameters is critical to produce foam with specific properties depending on the application. In this study, the effect of foaming agent concentration, foaming temperature and time on the morphological and mechanical properties of NR foams was investigated. First, increasing the foaming agent content from 5 to 9 phr (parts per hundred rubber) increased the cell size (16%), while decreasing the compression modulus (28%). In the second part, increasing the foaming temperature (145 to 155°C) resulted in larger cell size (163%); while decreasing the cell density (28%), compression modulus (2%), and hardness (1%). In the third part, increasing the foaming time (25 to 45 min) led to smaller cell size (63%) combined with higher cell density (100%), compression modulus (16%), and hardness (3%). Based on all the results obtained, the best NR foam was obtained with 7 phr of foaming agent and produced at 150°C for 35 min leading to superior morphological and mechanical performance: the smallest cell size (25 µm) and the most uniform cell size distribution ( Đ = 1.03) generating the highest compression modulus (3.36 MPa). Finally, the experimental compression results were combined to build a nonlinear regression model to optimize the formulation and processing conditions leading to 6.5 phr of OBSH molded at 150°C for 36 min. The model showed good agreement with a validation test with less than 2% deviation observed for both compression modulus and strength.

Optimization of Process Parameters Based on Machine Learning and Determination of Relative Importance of Process Variables on Jute Ply Yarn Breaking Extension

The breaking extension of jute ply yarn is comparatively low and ordinarily varies from 1.5 – 3.5% depending upon the process parameters like number of plies, single yarn twist factor and ply to single yarn twist ratio. To achieve various levels of jute ply yarn breaking extension within the above range, optimization of the three process variables was done in this study using a machine learning-based decision tree. For such optimization, twenty-seven different types of jute ply yarns were produced using three levels of singles' twist factor, number of ply and ply-to-single twist ratio. A total of 216 observed breaking extension values of these yarns were used for regression-based machine learning wherein 67% of test data were used to train the mode and the remaining 33% of test data were used for validation. An 8-node decision tree thus achieved from the model was used for the optimization process. A boxplot vs. terminal node graph was also used for classified optimization of ply breaking extension for various levels. The study conducted in this work reveals that the breaking extension of jute ply yarn varies directly with the number of plies, where 4-ply jute yarn produces maximum breaking extension and 2-ply produces the minimum. It was also observed that the decision tree was useful for the judicial selection of process parameters to achieve various levels of jute ply yarn breaking extension, wherein, it was found that the critical values for ply to single yarn twist ratio and single yarn twist factor were 0.80 and 26. The study also shows that apart from the individual influence of the variables on the breaking extension of ply yarns, interactions between variables also influence the breaking extension of jute ply yarn.

Morphology analysis and process optimization of μ-SLA 3D manufactured micro-nano conic structure

PurposeThis paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.Design/methodology/approachThe µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.FindingsAfter the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.Originality/valueThis study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

Effects of selective laser melting process parameters on 3D-printing of Ti6Al4V/ST316L composite material and their optimization using response surface methodology

The mechanical properties of Ti6Al4V and ST316L bone implants make them superior to natural bone, which results in fewer contact points with the bone. Different manufacturing processes, such as selective laser melting (SLM), may result in different mechanical properties between Ti-6Al-4V and ST316L implants. Sustainable development for the composite implants was optimized (SLM) in this study to minimize their compressive strength and Young's modulus. A three-dimensional printing process using SLM was optimized based on laser power, hatch distance, laser velocity, and ST316L weight percentage. Composite implants made from titanium alloys and steel alloys were evaluated using response surface methodology (RSM). ST316L composition has been found to influence the mechanical properties of composites in a significant manner, based on the results of parameter optimization. Using Ti6Al4V/ST316L as a biomaterial in knee joint prostheses is possible.

Data-driven advisory system for industrial manufacturing. Application to the aluminum extrusion process

The article presents a methodology for developing systems supporting optimal decision-making in the selection of manufacturing process parameters and tooling, from the point of view of minimizing the tendency for the occurrence of product defects. The developed advisory system is based on two essential pillars: searching a database extracted from the company's database and a model obtained from the same data; the latter is used to extract the working database from the original enterprise database and set up the search algorithm. The paper first discusses the characteristics of typical data recorded in a manufacturing process and presents the data used in the present work. Next, various general concepts of building an advisory system are presented, and the choice of a system that combines a database approach with modeling based on machine learning is justified. The fundamentals of the system dedicated to the aluminum extrusion process are presented, followed by the system implementation, including examples of its functioning. The developed system not only allows decisions to be made based on recorded cases and hidden dependencies detected by models in the production process but also helps identify the causes of problems. It can also help to draw attention to critical parameters of a product from the point of view of its manufacturing.