Manufacturing Process Parameters Research Articles

The effects of joint process parameters of two-step manufacturing processes on the mechanical performance of biocomposites using Taguchi and multiple regression techniques

This study evaluates the influence of joint parameters of extrusion (extrusion profile temperature) and injection molding (barrel profile temperature and mold temperature) manufacturing processes on the mechanical performance of biocomposite materials via the Taguchi and multiple regression techniques. Statistical analysis of variance (ANOVA) was performed to determine the most influential controllable factors for the individual responses. The results of the study revealed that the mechanical properties of the biocomposites are affected by the joint parameters of the two-step manufacturing techniques. This study will help in the process design of biocomposites with improved mechanical performance.

The Influence of the Plasma Electrolytic Oxidation Parameters of the Mg-AZ31B Alloy on the Micromechanical and Sclerometric Properties of Oxide Coatings

This manuscript presents the influence of manufacturing process parameters (peak current density, frequency, process time) on the micromechanical and sclerometric properties of oxide coatings. These parameters were selected based on Hartley’s experimental design, considering three variables at three levels. The coatings were produced on the AZ31B magnesium alloy using the plasma electrolytic oxidation (PEO) method. A trapezoidal voltage waveform and an alkaline, two-component electrolyte were used during the process. The micromechanical and sclerometric properties were assessed by measuring the hardness (HIT) and Young’s modulus (EIT) and determining three critical loads: Lc1 (the critical load at which the first coating damage occurred—Hertz tensile cracks within the scratch), Lc2 (the critical load causing the first cohesive damage to the coating), and Lc3 (the load at which the coating was completely destroyed). Scratch tests were supplemented with profilographometric measurements, which were used to generate isometric images. To identify the relationship between micromechanical and sclerometric properties and the manufacturing parameters, statistical analysis was performed. Research has demonstrated that the plasma electrolytic oxidation (PEO) process improves the micromechanical and adhesive properties of oxide coatings on the AZ31B magnesium alloy. The key process parameters, including peak current density, frequency, and duration, are crucial in determining these enhanced properties.

Identifying and optimizing critical process parameters for large-scale manufacturing of iPSC derived insulin-producing β-cells

BackgroundType 1 diabetes, an autoimmune disorder leading to the destruction of pancreatic β-cells, requires lifelong insulin therapy. Islet transplantation offers a promising solution but faces challenges such as limited availability and the need for immunosuppression. Induced pluripotent stem cells (iPSCs) provide a potential alternative source of functional β-cells and have the capability for large-scale production. However, current differentiation protocols, predominantly conducted in hybrid or 2D settings, lack scalability and optimal conditions for suspension culture.MethodsWe examined a range of bioreactor scaleup process parameters and quality target product profiles that might affect the differentiation process. This investigation was conducted using an optimized High Dimensional Design of Experiments (HD-DoE) protocol designed for scalability and implemented in 0.5L (PBS-0.5 Mini) vertical wheel bioreactors.ResultsA three stage suspension manufacturing process is developed, transitioning from adherent to suspension culture, with TB2 media supporting iPSC growth during scaling. Stage-wise optimization approaches and extended differentiation times are used to enhance marker expression and maturation of iPSC-derived islet-like clusters. Continuous bioreactor runs were used to study nutrient and growth limitations and impact on differentiation. The continuous bioreactors were compared to a Control media change bioreactor showing metabolic shifts and a more β-cell-like differentiation profile. Cryopreserved aggregates harvested from the runs were recovered and showed maintenance of viability and insulin secretion capacity post-recovery, indicating their potential for storage and future transplantation therapies.ConclusionThis study demonstrated that stage time increase and limited media replenishing with lactate accumulation can increase the differentiation capacity of insulin producing cells cultured in a large-scale suspension environment.

Investigation of the effects of contour process parameters on mechanical properties and part quality in SLM

AlSi10Mg is an aluminium alloy preferred in the automotive, medical, and aerospace industries due to its engineering properties such as lightness, high corrosion resistance, and excellent castability. In metal-based additive manufacturing, offset value, laser power, and scanning speed are important manufacturing parameters. These parameters significantly impact the formation of defects that adversely affect the microstructure, mechanical properties, surface quality, and dimensional accuracy of the final product. Therefore, it is imperative to select these parameters correctly during the manufacturing process. In this context, the study focuses on improving product quality in the additive manufacturing of AlSi10Mg alloy, and samples were produced using different contour process parameters. The production of samples was carried out according to the Taguchi L27 orthogonal experimental design. Three different offset values (0.140–0.175–0.210 mm), three different laser powers (160–200-240 W), and three different scanning speeds (280–350-420 mm/s) were utilized as contour process parameters. At the conclusion of the study, it was determined that there are ideal process parameters for achieving high mechanical properties, low surface roughness, and dimensional accuracy according to nominal values. It was observed that the product quality decreased in samples produced with non-ideal parameters. Yield strength decreased by 4.12 % with an increase in the offset value from 0.140 mm to 0.175 mm, and by 3.83 % with an increase from 0.175 to 0.210 mm. Increasing the offset value from 0.140 mm to 0.175 mm resulted in a decrease in tensile strength by 1.94 % and increasing it from 0.175 mm to 0.210 mm resulted in a decrease of 2.83 %. The decrease in mechanical properties was attributed to the occurring defects. The lowest Ra value (2.011 µm) was measured at a 0.140 mm offset value, with a laser power of 200 W, and a scanning speed of 280 mm/s. For all offset values, the deviation in thickness measurement varies between approximately −5.13 % and + 4.96 %, and the deviation in width measurement varies between −1.89 and + 1.33. Multi-response optimization was conducted for mechanical properties, surface roughness, and dimensional accuracy using the Taguchi-based gray relational analysis method. According to the experimental results, the optimal contour process parameters for additive manufacturing of AlSi10Mg alloy were obtained as 0.140 mm offset value, 200 W laser power, and 280 mm/s scanning speed.

Influence of different rare earth oxides on CNC ultrasonic machining of HP-HVOLF sprayed ceramics coating

The current study aimed at the optimization of process parameters for microchannel fabrication on thermal sprayed carbide coatings using rotary ultrasonic machining (RUSM). Rare earth oxides (REOs) such as La₂O₃, CeO₂, and Er₂O₃ have been incorporated into WC-Co-Cr coatings to enhance machinability and improve coating characteristics. The research has demonstrated that La₂O₃ doped coatings have significantly increased the material removal rate (MRR), with a 15 % improvement when compared to undoped coatings. Additionally, surface roughness has been reduced by 20 %, resulting in a smoother and more uniform finish. The inclusion of REOs has refined the microstructure of the coatings, yielding denser and more compact layers, which has reduced defects and improved the overall surface quality. Furthermore, the hardness of the coatings has been enhanced by 45 %, with La₂O₃ doped coatings achieving a maximum hardness value of 1313 ± 11 HV. The findings have highlighted the potential of REO doping to enhance the performance and efficiency of microchannel fabrication on WC-Co-Cr coatings using RUSM, contributing valuable insights to industrial applications.

Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

The material characteristics of additively manufactured AlSi10Mg alloy, including the random distribution of process-induced micro defects, microstructual anisotropy, and grain morphologies with complex diversity, poses a significant challenge in accurately predicting its fatigue life, limiting its application in the aircraft field. This work herein introduces a novel approach incorporating a physics-informed neural network (PINN), in which an artificial neural network (ANN) is embedded with a damage mechanics model (CDM) and the critical process parameters of laser powder bed fusion (L-PBF) additive manufacturing. The partial differential equations of the updated CDM model are introduced into the training procedures of the PINN to building a loss function, effectively “teaching” the PINN to learn the physical knowledge. A comparison of the fatigue life prediction results of ANN and the proposed PINN models shows that the PINN model outperforms its counterparts with a 38.71% higher prediction accuracy. The effects of L-PBF process parameters on the fatigue life of as-built AlSi10Mg is examined using both ANN and PINN, proving the better predictive performance and data-physics consistency of the PINN model. The scheme of this work can inform the efficient prediction of fatigue life of additively manufactured alloys and also reverse prediction or fast finding of optimized process parameters for additive manufacturing.

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Finite Element Analysis and Machine Learning in the Loop Additive Manufacturing Process Parameter Optimization to Acquire the Desired Dynamics of Manufactured Parts

Determination of process parameters for additive manufacturing with Inconel 718 powder

Abstract In this study, the additive manufacturing (AM) of Inconel 718 was investigated, and the processing parameters can affect the mechanical properties of the product. The five-axis laser cladding composite processing machine Tongtai AMH-630 is used, and the laser power is set to 800W based on the thermal impact of the laser on the base plate. However, the laser scanning speed (600-1200mm/min) and powder feed rate (3~15g/min) are not arbitrary combinations. It is recommended to present the feed per unit length in g/mm, which seems to be more clearly expressed. Regardless of changes in laser scanning speed and wheel speed, it is a reasonable decision to control the combination of the two at about 0.01-0.013g/mm. Through single-pass cladding experiments, we select different combinations of laser scanning speed and powder feed rate with a cladding aspect ratio in the range of 3:1 to 4:1. In the multi-pass cladding experiment, an overlap rate of 30% was used as the plane cladding spacing; a spacing of 80% of the cladding height was used as the stacking height. This study uses scanning speeds of 600, 700, 800, 1000 and 1200mm/min to clad a rectangular body. Tensile test specimens were processed using a CNC lathe, and tensile tests were conducted to obtain variations in tensile strength and elongation at different scanning speeds. The mechanical properties of both will decrease as the scanning speed increases. At a scanning speed of 600mm/min, its tensile strength is comparable to that of Inconel 718 standard materials; but its elongation is 61% of that of standard materials. If Hot Isostatic Pressing (HIP) is applied after cladding, it seems that a higher scanning speed can achieve a performance closer to that of standard materials.

Effect of drying temperature on binder/current collector interfacial adhesion in electrode manufacturing of Li-ion batteries

Li-ion battery manufacturing process parameters are critical to the electrode properties and the final cell electrochemical performance. During the electrode drying process, the drying temperature plays a critical role on the binder migration, which affects the interfacial adhesion between the electrode and the current collector. However, the influence of the temperature on the properties of the binder material and the binder/current collector interface is yet unknown. In this work, we studied the effect of drying temperature on the interfacial adhesion between the binder and the current collector by direct coating of polyvinylidene fluoride (PVDF) solution on Al foil and then drying at various temperatures. The interfacial adhesion strength between the PVDF and the Al foil was significantly increased, from 9.72 N/m (dried at room temperature) to > 665.80 N/m (dried at 200 ℃) with increased temperature. DSC and XRD analyses showed the changes in the crystalline forms of PVDF under different drying temperature. This work revealed that the drying temperature during electrode manufacturing should be considered from the aspects of both binder migration in mid-stage and PVDF crystalline properties in late-stage solvent drying.

Effect of Interlayer Cooling Time on Microstructure and Properties of 2205 Duplex Stainless Steel Made by Cold Metal Transfer

By adjusting the parameters in the additive manufacturing process, the optimization of the microstructure and mechanical properties of 2205 duplex stainless steel (DSS) is of great significance to ensure the reliability of the material in harsh environments. The 2205 DSS samples are prepared by cold metal transfer conditions of interlayer cooling time of 0, 30, 60, and 120 s, respectively, and the microstructure and properties of the samples are characterized in detail. With the increase of cooling time, the height of the samples increases, and the width decreases, and no defects are found in all the samples. It also reduces the grain size and contributes to the uniform distribution of grains. With the extension of cooling time, the proportion of austenitic phase gradually increases from 41.60% to 47.36%, while the proportion of ferrite decreases correspondingly. The mechanical properties of the samples are tested. With the increase of cooling time, the hardness of the samples increases from 646.32(±1.46) to 679.36(±7.27) MPa, and the ductility decreases from 60.10(±0.50)% to 43.67(±2.10)%. The results show that the interlayer cooling time can be used to regulate the formation, microstructure, and properties of DSS.

Hot hydrogen testing of Mo30W matrix surrogate cermets

High temperature hydrogen exposure is one of the most challenging material issues for nuclear thermal propulsion (NTP) fuel development. Under legacy NTP programs, ceramic-metallic (cermet) fuel forms with a refractory metal matrix and dispersed uranium dioxide (UO2) fuel particles were developed and showed promising performance following hot hydrogen testing. However, since the conclusion of those programs, established fabrication techniques, material feedstocks, and the ability to use highly enriched have been reduced or lost all together. In this study, a cermet consisting of a solid solution alloy of molybdenum with 30 wt percent tungsten (Mo30W) was fabricated using spark plasma sintering. Fabrication process parameters were selected to optimize the cermet microstructure using lessons learned from historic NTP programs. Yttria stabilized zirconia particles (50 to 70 % volumetric loading) were used as a fuel particle surrogate. To evaluate whether as-fabricated microstructures exhibited similar resilience as legacy cermet fuels to a hot hydrogen environment, samples were exposed to hot flowing hydrogen from 1920 to 2500 °C (∼2290 to 2770 K). The cermets performed well with minimal mass loss, minor to no cracking, and good retention of internal surrogate particles. Based on these findings, recommendations for future studies with Mo30W-UO2 cermets as an NTP fuel form are provided.

Effects of fused filament fabrication (FFF) process parameters on tensile and flexural properties of ABS/PLA multi-material

Effects of fused filament fabrication (FFF) process parameters on tensile and flexural properties of ABS/PLA multi-material

Statistical modeling and optimization of process parameters for additive manufacturing of styrene–ethylene–butylene–styrene block copolymer parts using solvent cast 3D printing technique

AbstractAdditive manufacturing of thermoplastic elastomers is challenging using fused deposition modeling due to their high melt viscosity, low column strength, and poor fusion among layers. Solvent‐cast 3D printing (SC‐3DP) is an efficient alternative to successfully 3D print such materials. However, selection of suitable 3D printing parameters is crucial to realize parts with optimum physicomechanical properties. In this work, statistical modeling and SC‐3DP parameter optimization for styrene–ethylene–butylene–styrene (SEBS) block copolymer were performed. The effect of 3D printing process parameters on shrinkage, relative density, and tensile strength was analyzed using response surface methodology. Experiments were planned as per central composite design and analysis of variance was performed to evaluate the significant parameters. SEBS content in the polymer solution significantly affected the shrinkage of SC‐3DP samples. Moreover, relative density and tensile strength were significantly affected by print speed and layer height. A significant interaction between print speed and layer height was also noticed for tensile strength and relative density of printed samples. Multi‐objective optimization using genetic algorithm was also performed to minimize shrinkage and maximize relative density and tensile strength. Finally, a case study was conducted comparing the physicomechanical properties of SC‐3DP samples printed at optimized process parameters and compression molded samples.Highlights Statistical models were developed using response surface methodology. Genetic algorithm based multi‐objective optimization was performed. Optimum solvent‐cast 3D printing (SC‐3DP) process parameters were determined.

Controlling microstructure and B2 ordering kinetics in Fe–Al system through additive manufacturing

Multi-material fabrication between steel and aluminum is challenging because of the formation of several intermetallic phases. Embrittling B2 ordered intermetallics form in the steel rich side and are stable till about 60% of Al dilution in steel. In this work by using multi-length scale characterization coupled with integrated computational process and thermokinetic modeling, we show that the ordered B2 intermetallics in the steel rich side of the Fe–Al system forms via a nucleation and growth mechanism. The extent of B2 ordered intermetallics can be controlled by modifying the directed energy deposition-additive manufacturing (DED-AM) process parameters. Our findings lay the foundation for enabling fabrication of crack-free functionally graded compositions between the two alloys.

Programming the deformation of the temperature driven spiral structure in 4D printing

Abstract Achieving shape programming of 4D printed actuators by varying the manufacturing process parameters. In this study, the effect of different path combinations on structural deformation was investigated. By altering the driving layer, passive layer, and grid angle, the spiral deformation direction of the double-layer structure was precisely controlled. Additionally, a finite element analysis model was established to predict the deformation behavior of PLA-based spiral structures. Furthermore, the influence of printing speed, nozzle temperature, line width, layer height, and plate temperature on the spiral curvature of the structure was examined. The results show that increasing printing speed and plate temperature can improve the spiral behavior of the structure, whereas increasing line width, layer height, and nozzle temperature have opposite effects. A multiple linear regression analysis was conducted on the five printing parameters to predict their influence on the spiral curvature of the structure, and a predictive model for the spiral deformation was developed. The structure was partitioned for design purposes, aiming to achieve diverse deformations of the actuator under the same geometric configuration. A loop-shaped actuator was designed to capture objects. The results showed that the path combination determined the spiral direction of the actuator, while the forming parameters effectively controlled the spiral curvature of the actuator.

Two‐stage annealing method for short carbon fiber‐reinforced nylon 6 in fused filament fabrication

AbstractThe mechanical properties of short carbon fiber‐reinforced nylon 6 (CF‐PA6) components formed by fused filament fabrication (FFF) are significantly affected by the process parameters of additive manufacturing and heat treatment. In this study, the effects of printing speed, carbon fiber aspect ratio, and annealing temperature on fiber orientation were investigated by observation of single‐layer‐single‐fiber samples. Furthermore, a two‐stage annealing process was proposed to increase the tensile modulus of CF‐PA6 based on the correlation analysis results between the degree of fiber alignment and the variation of tensile modulus. According to the calculation of the Pearson correlation coefficient, the tensile modulus was highly correlated with the degree of fiber alignment under different printing speeds and fiber aspect ratios. The variation in tensile modulus and fiber orientation was also consistent when the annealing temperature was lower than the crystallization temperature. Conversely, the variation of tensile modulus and fiber orientation were opposite, as the annealing temperature was higher than the crystallization temperature because of the influence of crystallinity. Through increasing the degree of fiber alignment and crystallinity of the parts, the proposed two‐stage annealing method could increase the tensile modulus of the parts to 9.0 GPa, which was 16.81% higher than that of single‐temperature annealing.Highlights The effects of annealing temperature, carbon fiber aspect ratio and printing speed on fiber orientation were illustrated. The relationship between the degree of fiber alignment and the variation of tensile modulus was established. The proposed two‐stage annealing method, considering fiber orientation and crystallinity, significantly increased the tensile modulus of CF270‐PA6 parts from 7.7 to 9.0 GPa. The quantitative analysis of the fiber orientation is realized by the statistical analysis of the plane fiber orientation angle of the single‐layer‐single‐fiber sample.

Multiscale analysis of interlocking effects for polymer additive manufacturing on aluminum foam

AbstractHybrid structures are increasingly relevant for lightweight design and functional components. Various manufacturing techniques for creating hybrid components exist, often focusing on combining metal and polymer components. The present study examines a novel approach for manufacturing hybrid functional structures by directly printing thermoplastic onto aluminum profiles using closed‐cell aluminum foam, with the mechanical interlocking resulting in a structural bond. The filling of pores in the aluminum foam with the polymer affects the bond strength of this hybrid compound. Within a simulation‐based process design the additive manufacturing process parameters are investigated using a computational fluid dynamics (CFD) model with Fluid‐Structure Interaction for the pore‐filling process and a finite element (FE) model to evaluate the resulting bond strength. The material throughput, the nozzle distance, and the polymer temperature are the key process parameters taken into account. A parameterized, virtual foam model is used within this framework, where a linear movement of the extruder nozzle and the resulting polymer strand is investigated. This method includes the mesh conversion from the CFD results to the FE mesh for structural analysis by exporting the iso‐surface for the polymer geometry embedded in the pores of the aluminum foam structure. This approach made it possible to investigate the phenomena that occur in the process in more detail, particularly to quantify the influence of air inclusions. Thus, a decisive contribution to the hybridization of aluminum foams has been made, which opens up further potential for research and application in the long term.

The use of orthogonal analytical approaches to profile lipid nanoparticle physicochemical attributes

Abstract Lipid nanoparticles have become a major disruptor within the drug delivery field of complex RNA molecules. The wide applicability of prototype nanomedicines have the potential to fill clinical requirements against current untreatable diseases. The uptake and implementation of analytical technologies to evaluate these prototype nanomedicines have not experienced similar growth rates hindering the translation of LNPs. Here, we evaluate a model RNA-LNP formulation with a selection of routine, and high-resolution orthogonal analytical techniques across studies on manufacturing process parameter impact and formulation stability evaluation under refrigerated and ultra-low temperatures. We analysed model cationic RNA complexed lipid nanoparticle formulation through process impact on formulation critical quality attributes, short term refrigerated stability evaluation, and frozen storage stability using zetasizer dynamic light scattering and nanoparticle tracking analysis. We also evaluated freeze/thaw induced stress on LNP formulation using high resolution field-flow fractionation. Statistical analysis and correlations between techniques were drawn to further enhance our understanding of LNP formulation design, and physiochemical attributes to facilitate LNP formulation clinical translation.

Design of experiment (DoE) of mucoadhesive valproic acid-loaded nanostructured lipid carriers (NLC) for potential nose-to-brain application

Epilepsy is a highly prevalent neurological disease and valproic acid (VPA) is used as a first-line chronic treatment. However, this drug has poor oral bioavailability, which requires the administration of high doses, resulting in adverse effects. Alternative routes of VPA administration have therefore been investigated, such as the nose-to-brain route, which allows the drug to be transported directly from the nasal cavity to the brain. Here, the use of nanostructured lipid carriers (NLC) to encapsulate drugs administered in the nasal cavity has proved advantageous. The aim of this work was to optimise a mucoadhesive formulation of VPA-loaded NLC for intranasal administration to improve the treatment of epilepsy. The Design of Experiment (DoE) was used to optimise the formulation, starting with component optimisation using Mixture Design (MD), followed by optimisation of the manufacturing process parameters using Central Composite Design (CCD).The optimised VPA-loaded NLC had a particle size of 76.1 ± 2.8 nm, a polydispersity index of 0.190 ± 0.027, a zeta potential of 28.1 ± 2.0 mV and an encapsulation efficiency of 85.4 ± 0.8%. The in vitro release study showed VPA release from the NLC of 50 % after 6 h and 100 % after 24 h. The in vitro biocompatibility experiments in various cell lines have shown that the optimised VPA-loaded NLC formulation is safe up to 75 µg/mL, in neuronal (SH-SY5Y), nasal (RPMI 2650) and hepatic (HepG2) cells. Finally, the interaction of the optimised VPA-loaded NLC formulation with nasal mucus was investigated and mucoadhesive properties were observed. The results of this study suggest that the use of intranasal VPA-loaded NLC may be a promising alternative to promote VPA targeting to the brain, thereby improving bioavailability and minimising adverse effects.

Sensitivity study of process parameters of wire arc additive manufacturing using probabilistic deep learning and uncertainty quantification

This study employs a deep learning (DL) based stochastic approach to comprehensively interpret the effects of current intensity and velocity variations on temperature evolutions and cooling rates in the wire arc additive manufacturing (WAAM) process of a thin wall. Uncertainty raised from process parameters, material properties, and environmental conditions significantly impacts the final product quality. Furthermore, understanding the relationship between the process and temperature evolution within the WAAM process is complex. This study contributes to quantifying the uncertainty to the final product quality, such as temperature evolutions and cooling rates via a fast and accurate DL-based surrogate model. This contribution helps to precise adjustments and optimizations to enhance the overall WAAM process. Initially, a DL-based surrogate model is constructed using data obtained from a high-fidelity validated finite element (FE) model, ensuring an impressive 99% accuracy compared to the FE model while reducing computational costs. Subsequently, probabilistic methods are used to characterize uncertainties in current intensity and velocity, and the Monte-Carlo method is applied for uncertainty propagation. The findings illustrate that small variations in the input parameters can lead to significant fluctuations in temperature evolutions. Additionally, a sensitivity analysis is conducted to precisely quantify the influence of each input parameter. Finally, an uncertainty reduction is performed to enhance the variation of cooling rate. In general, this study is expected to make precise adjustments and optimizations to enhance the overall WAAM process for better quality of printed piece.