Process Parameters Research Articles

A Comprehensive Study on Electrospun Cholesteric Liquid Crystal Core Fibers for Detecting Volatile Organic Compounds

Encapsulation of the cholesteric liquid crystal (CLC) into the core of poly(vinylpyrrolidone) (PVP) microfibers is accomplished via coaxial electrospinning technology and is applied to detect volatile organic compounds (VOCs). The impacts of various process parameters on the morphology of CLC fibers are deeply explored. The periodic arrangement of CLC molecules within the fibers enables the reflection of specific wavelengths. Consequently, changes in the reflected color of the CLC fiber membrane allow for the measurement and analysis of targeted gas molecules. The dynamic response of CLC fibers upon exposure to organic vapor is observed by a polarizing optical microscope, and its gray scale is used to quantitatively analyze the changes of reflected light signals of CLC fibers. Furthermore, the study investigates the reaction time for the failure of CLC molecular planar alignment in the CLC fiber membrane when subjected to perturbations induced by organic gases. The CLC fiber sensor exhibits heightened sensitivity to gases characterized by greater polarity. In light of potential future advancements, it is suggested that the spun fibers imbued with responsive properties through the incorporation of a liquid crystal core exhibit promise as a next-generation sensor technology in textile form, well-suited for applications in wearable technology.

Integration of CO2 sequestration with the resource recovery of red mud and carbide slag

Red mud and carbide slag, strongly alkaline solid wastes, are produced during the manufacturing of alumina and acetylene gas, respectively. Improper management of these wastes can pose significant environmental hazards. This study proposed a novel method for the collaborative treatment of red mud and carbide slag, enabling the recovery of constituent elements with CO2 sequestration. During the hydrochloric acid leaching process, Si was reclaimed as Si-rich residue, whereas Ti, Al, Ga, Fe, and Sc were stepwise recovered as Ti-rich product, Al/Ga-rich filtrate, and Fe/Sc-rich product via the subsequent hydrolysis, co-precipitation, and alkali leaching processes, respectively. Utilizing carbide slag to replace the energy-intensive NaOH for pH adjustment in the leaching solution resulted in a Ca2+ concentration of 21,922 mg/L in the filtrate after co-precipitation. Through further mineralization process, CO2 sequestration was achieved alongside the production of CaCO3 with the content reaching 98.5 % along with a whiteness of 94.4 %. Furthermore, by adjusting the experimental parameters in the mineralization process, controlled manipulation of the phase and morphology of the CaCO3 product could be achieved to meet various industry requirements. To summarize, the complete resource recovery of red mud and carbide slag was achieved in this study, demonstrating the promising potential in practical applications.

Influence of Defects and Microstructure on the Thermal Expansion Behavior and the Mechanical Properties of Additively Manufactured Fe-36Ni.

Laser-based powder bed fusion of metals (PBF-LB/M) is a widely used additive manufacturing process characterized by a high degree of design freedom. As a result, near fully dense complex components can be produced in near-net shape by PBF-LB/M. Recently, the PBF-LB/M process was found to be a promising candidate to overcome challenges related to conventional machining of the Fe64Ni36 Invar alloy being well known for a low coefficient of thermal expansion (CTE). In this context, a correlation between process-induced porosity and the CTE was presumed in several studies. Therefore, the present study investigates whether the unique thermal properties of the PBF-LB/M-processed Fe64Ni36 Invar alloy can be tailored by the selective integration of defects. For this purpose, a full-factorial experimental design, representing by far the largest processing window in the literature, was considered, correlating the thermal expansion properties with porosity and hardness. Furthermore, the microstructure and mechanical properties were investigated by scanning electron microscopy and quasi-static tensile tests. Results by means of statistical analysis reveal that a systematic correlation between porosity and CTE properties could not be determined. However, by using specific process parameter combinations, the microstructure changed from a fine-grained fan-like structure to a coarse columnar structure.

Recommended Practices for the Electrochemical Recovery of Cobalt from Lithium Cobalt Oxide: A Case Study of the Choline Chloride:Ethylene Glycol Deep Eutectic Solvent.

We recommend best practices for the recovery of cobalt from LiCoO2 (LCO) lithium-ion battery (LIB) cathodes by (i) leaching using green deep eutectic solvents (DES) and (ii) subsequent electrodeposition, through a case study of the choline chloride (ChCl):ethylene glycol (EG) DES. DES physical properties (conductivity, viscosity, and surface tension) were tailored by varying the composition between mole ratios of 1:2 and 1:5 (ChCl:EG). Examined along with leaching process parameters (temperature, duration), increasing the fraction of hydrogen bond donors (HBDs) decreased DES surface tension and enhanced leaching. Complete Co recovery was achieved using1:5 ChCl:EG DES at 160oC and 48 hours. Leaching temperatures >160oC are discouraged due to DES thermal degradation. The electrodeposition process was optimized for selective Co recovery with high faradaic efficiency. The leaching ability of the DES was antithetical to the stability of electrodeposition cell components and required operational parameter adjustment to minimize degradation. The optimized system (copper cathode and stainless-steel anode) employing 1:5 DES leachate exhibited a faradaic efficiency of ~80 %, specific Co recovery of ~0.8 mg hr-1 cm-1 at 50 oC and evidence of uniform deposition. DES surface tension is a key descriptor of metal recovery, and guidelines are presented to maximize selective Co recovery.

An experimental process parameter study on the identification of defects in additively fabricated Al6061 with laser powder bed fusion

Additively fabricated metal parts using laser powder bed fusion (L-PBF) possess sophisticated morphology due to the recurrent use of laser-induced metal powder melting and solidification. The surface and 3D morphology of these parts often include defects in the form of protrusions, depressions, pores, voids, keyholes, or cracks that are known to be influenced by laser scanning paths and layer-to-layer processing. Such inconsistent part quality hampers the extensive adoption of L-PBF. Pores and cracks are detrimental to the fatigue life of the parts and components. Quantifying and controlling part defects and optimizing processing and scanning strategy parameters adaptively in real-time through in situ monitoring systems are highly desired. This study investigates the optimization of experimental process parameters (power, scan velocity, and hatch spacing) and their effects on the cracking and porosity of Al6061 alloy using machine learning techniques. Multi-objective optimization is formulated and conducted to determine the L-PBF parameters that minimize both porosity and crack densities.

Model-based optimization strategy for intensification in the chromatographic purification of oligonucleotides

Oligonucleotides (ONs) are acquiring clinical relevance and their demand is expected to grow. However, the ON production capacity is currently limited by high manufacturing costs. Since the purification of the target ON sequence from molecularly similar variants represents a major bottleneck, this work presents a resource-effective strategy for the optimization of their preparative reversed-phase chromatographic purification. First, a model based on the equilibrium-dispersive theory was introduced to describe the chromatographic operation. Considering a deoxyribose nucleic acid with 20 nucleobases as case study, a genetic algorithm was developed to efficiently determine the adsorption isotherm and mass transfer parameters for the target ON and impurities. After the estimation of these parameters, a strategy for the in-silico optimization of the operation was established. The product collection window, gradient duration, and resin loading were considered as process variables and their influence on yield and productivity was investigated after setting a purity specification of 99.0%. The optimal process parameters identified through this analysis were experimentally verified, confirming the reliability of the model, calibrated with only 5 experimental runs. In addition, this optimal setpoint was exploited to design the multicolumn countercurrent solvent gradient purification (MCSGP) of this ON mixture, which allowed to boost the yield of the process and to work at cyclic steady state, while respecting the purity constraint. This study confirmed the potential of this in-silico optimization strategy in both improving the performance of the traditional single-column operations and in the rapid development of multicolumn processes.

Local and global mechanical properties of orbital friction stir welding on API X65 PSL2 steel / Inconel 625 clad pipes

Orbital friction stir welding (FSW) is a promising approach to joining clad pipes. In this work, the influence of individual process parameters on the material flow even as, mechanical properties in orbital friction stir welded clad pipes is investigated. Due to the local heterogeneous microstructures within the weld, joints were evaluated by optical and scanning electron microscopy (SEM), microhardness, and digital image correlation (DIC). A microhardness increase was observed, with a maximum of 380 HV0.5 within the Inconel 625 and 265 HV0.5 within the X65 steel stir zones. Sound joints had an average ultimate tensile strength (UTS) exceeding up to 13% of the calculated theoretical tensile strength, which is obtained considering the minimum tensile strength of both materials. Additionally, failure occurred in the base material during the tensile testing, where the local strain in the stir zones of the X65 steel and Inconel 625 were only 3.3% and 10%, respectively. In summary, this investigation shows friction stir welded joints of API X65 steel and Inconel 625 clad pipes with high mechanical properties.

Influence of spatial position of sound source on dual-frequency ultrasound-assisted preparation of magnesium alloy

In this work, a dual-frequency ultrasonic field was employed to improve the quality and mechanical properties of magnesium alloy ZW61 ingot. The effect of the spatial position of the sound source on the cavitation volume, microstructure size, and morphology was discussed in detail combined with numerical simulations and experiments. The mechanical properties were analyzed elaborately and the relationship between yield strength (YS), ultimate tensile strength (UTS), and average grain size (d) was established. The results showed that the dual-frequency ultrasonic field (DUF) had a larger cavitation range and superior grain refinement ability than the single-frequency ultrasonic field (SUF) at the same energy consumption, with the grain refining from 578 μm (untreated) and 395-433 μm (SUF) to 55.2 μm. The grain refining effect of DUF weakened with increasing sound source spacing, with d increasing from 55.2 μm to 163 μm. The radiating angle had little effect on the grain size. Too shallow or too deep inserting depth of the ultrasonic rods could seriously weaken the grain refinement. In this case, 30mm source spacing, 60° radiating angle, and 30mm inserting depth were the optimal process parameter combinations, and the ingot strength was increased from YS of 83MPa and UTS of 151MPa to 138MPa and 241MPa, respectively. YS and d for all conditions conformed to the Hall-Petch relationship, with σ0 and k values of 71.3MPa and 503.9MPa μm-1/2. The relationship between UTS and d could be expressed as UTS = 126.7 + 907.0 d-1/2.

Machine learning-assisted wire arc additive manufacturing and heat input effect on mechanical and corrosion behaviour of 316 L stainless steels

Predicting the track forming factor or height-to-width ratio (H/W) in wire arc additive manufacturing is crucial for optimal path planning, heat distribution, structural integrity, distortion control, process efficiency, and defect prevention, ensuring high-quality and reliable components. Different analytical and numerical modeling methods have been introduced to predict the H/W ratio. However, the accuracy of these predictions is relatively low due to the challenges associated with handling complex and non-linear regression equations. This study addressed the challenges by implementing data-driven predictive modeling to predict the H/W ratio from the input process parameters. A stacking ensemble learning approach is employed, where a meta-model integrates predictions from multiple base learners to enhance overall performance. The model performance was evaluated by Coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). The model was validated by comparing the experimental and predicted values based on which five thin walls are printed with different heat inputs. The study also explores the impact of heat input on microstructure, mechanical, and corrosion properties. The findings indicate that a significantly low heat input (178 J/mm) and higher heat inputs (> 356 J/mm) decrease the wire utilization. With increasing heat input, ferrite content increases, microstructures become coarser, dendritic spacing increases, and ferrite transitions from lathy to skeletal. Further, lower heat input (235 J/mm) reduces δ-ferrite, suppresses atomic segregation, and increases Cr and Mo in the matrix. YS and UTS decrease with higher heat inputs, anisotropy decreases, and Vickers microhardness drops from 228 (178 J/mm) to 194 Hv (586 J/mm). Additionally, the corrosion resistance deteriorates with increasing heat input, as evidenced by higher pitting potential (Epit: 0.389 V vs. 0.368 V vs. −0.023 V) and lower corrosion current density (Icorr: 6.76 ×10−7 A/cm2 vs. 7.946 ×10−7 A/cm2 vs. 8.91 ×10−7 A/cm2) for heat inputs of 235 J/mm, 281 J/mm, and 356 J/mm, respectively.

Influence of Process Parameters on Properties of Non-Reactive RF Magnetron-Sputtered Indium Tin Oxide Thin Films Used as Electrodes for Organic Light-Emitting Diodes

Influence of Process Parameters on Properties of Non-Reactive RF Magnetron-Sputtered Indium Tin Oxide Thin Films Used as Electrodes for Organic Light-Emitting Diodes

The Effect of FDM Parameters on the Mechanical Properties of PLA Printed Parts

The most common method for additive manufacturing of thermoplastics is fused deposition modeling (FDM), which is becoming a growing trend in a variety of engineering applications since it can easily create intricate parts. The appropriate choice of process parameters has a significant impact on the mechanical qualities of 3D-printed parts. This study examined the effect of four crucial process variables on the tensile strength of polylactic acid (PLA) samples: infill density, printing speed, build orientation, and layer thickness. Samples were printed in accordance with ASTM D638 using an FDM 3D printer. The findings of this study show that the tensile strength of the PLA-printed samples is highly influenced by factors such as layer thickness, build orientation, and infill density. The PLA-printed samples' tensile strength and Young's modulus were significantly affected by the 90° orientations, hollow infill, 0.4 mm thickness, and 100 mm/s speed. Therefore, as the FDM 3D printer becomes progressively more significant for manufacturing engineering components, finding the parameter values that may lead to stronger mechanical and physical characteristics would definitely help designers and manufacturers globally.

Effect of Laser Powder Bed Fusion Process Parameters on the Microstructures, Mechanical Properties, and Conductivity of CuCrZr Alloy

Effect of Laser Powder Bed Fusion Process Parameters on the Microstructures, Mechanical Properties, and Conductivity of CuCrZr Alloy

Experimental investigations on influence of processing parameters on the microstructure and properties of AM-printed stainless steel

ABSTRACT The current study has investigated the mechanical properties and microstructure of the DMLS processed 316L experimentally by varying process parameters and solution annealing as a post-processing technique. The feedstock material characteristics, laser power input, layer thickness, scanning velocity, hatch spacing, and building direction are important factors to consider. The modified microstructure not only affects mechanical performance and corrosion resistance but also alters the specimen's properties. An analysis of the powder feedstock and density measurement has been conducted. The samples were subjected to tensile testing, hardness testing, and microstructural analysis. According to the Taguchi analysis findings, the most significant parameter affecting product quality was layer thickness. The observation of grain boundary precipitation in a few samples could be reversed through solution annealing. The results of this study will assist in determining the optimal process parameters for achieving the desired mechanical properties.

Optimization of multiple responses in the laser cladding process parameters

ABSTRACT An orthogonal test method was used to optimize parameters for Ni60A-25% tungsten carbide (WC) laser cladding layers. Meanwhile, the tensile properties of the optimized laser cladding layer are explored. A multi-factor analysis of variance (ANOVA) was conducted to examine the effect of factors (laser power-A, scanning speed-B, powdering rate-C, and spot diameter-D) and interactions on responding variable. The results showed that the interaction of scanning speed and powdering rate with spot diameter has a more pronounced impact on the dilution rate compared to a single factor. Based on the ANOVA of the dilution rate, the optimal combination of process parameters was A3C2B1D3. Furthermore, the optimal combination of process parameters was C3B2D3A3 for the effective area. The results showed that the yield strength of the specimens was 512.3 MPa and 509.7 MPa, and the tensile strength was 646.3 MPa and 605.7 MPa for process parameters I and II, respectively.

Toward Predicting the Formation of Integral-Asymmetric, Isoporous Diblock Copolymer Membranes.

The self-assembly and nonsolvent-induced phase separation (SNIPS) process of block copolymers and solvents enables the fabrication of integral-asymmetric, isoporous membranes. An isoporous top layer is formed by evaporation-induced self-assembly (EISA) and imparts selectivity for ultrafiltration of functional macromolecules or water purification. This selective layer issupported by a macroporous bottom structure that is formed by nonsolvent-induced phase separation (NIPS) providing mechanical stability. Thereby the permeability/selectivity tradeoff is optimized. The SNIPS fabrication involves various physical phenomena-e.g.,evaporation, self-assembly, macrophase separation, vitrification - and multiple structural, thermodynamic, kinetic, and process parameters. Optimizing membrane properties and rationally designing fabrication processes is a challenge which particle simulation can significantly contribute to. Using large-scale particle simulations, it is observed that 1) a small incompatibility between matrix-forming block of the copolymer and nonsolvent, 2) a glassy arrest that occurs at a smaller polymer concentration, or 3) a higher dynamical contrast between polymer and solvent results in a finer, spongy substructure, whereas the opposite parameter choice gives rise to larger macropores with an elongated shape. These observations are confirmed by comparison to experiments on polystyrene (PS)-block-poly(4-vinylpyridine) (P4VP) diblock copolymer membranes, varying the chemical nature of the coagulant or the temperature of coagulationbath.

Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting

High-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the casting of near net shape components from nonferrous alloys. The pressure and temperature conditions within the cavity impact the cast product quality during and after the conclusion of the die filling process. Die surface cavity sensors can deliver information describing the conditions at the die-casting interface. They are associated with high costs and limited service lifetimes below the achievable total cycle count of the die inserts and therefore ill-suited for industrial use cases. In this work, the suitability of long short-term memory (LSTM) recurrent neural networks (RNN) for substituting physical cavity temperature and pressure sensors virtually after the production ramp-up or at the end of the sensor service life is investigated. Training LSTMs with data of 233 casting cycles with different process parameters provides networks which are then applied to 99 further cycles. The prediction accuracy is investigated for different time interval lengths in the solidification and cooling phase. For longer time intervals, the cavity pressure prediction deteriorates, potentially due to a highly individual and hardly ascertainable buildup of casting distortion and internal stresses. Overall, however, the accuracy of the developed LSTMs is excellent for the cavity temperatures and good for the cavity pressures.

Microbial Production of Biopesticides for Sustainable Agriculture

Food security is threatened by biotic stress on crops, e.g., from invasive pests, in the context of climate change. Pest control has traditionally been achieved using synthetic pesticides, yet pollution and the persistence and toxicity of some pesticides are inducing food contamination and, in turn, generating public concern. As a consequence, biopesticides are increasingly used, notably for organic crops. For instance, some microorganisms produce biopesticidal compounds such as secondary metabolites or growth-inhibitory enzymes. Most reviews on this topic mainly focus on describing microbial species and their active compounds. However, there are very few studies and reviews describing various process parameters. Here, we review both microbial biopesticides and factors controlling physicochemical conditions for the scaling up of biopesticide production. We present biopesticides from bacteria (e.g., Bacillus thuringiensis), algae, fungi, and viruses and review the biocontrol mechanisms and applications of commercial biopesticides. The benefits of genetic engineering for enhancing activity and drawbacks such as commercialization are also discussed.

Study on the Extraction of Nervonic Acid from the Oil of Xanthoceras sorbifolium Bunge Seeds.

Seven fatty acids were detected by GC-MS in Xanthoceras sorbifolium Bunge seed oil extracted at different temperatures, including Palmitic acid C16:0, Stearic acid C18:0, Oleic acid C18:1, Eicosenoic acid C20:1, Docosenoic acid C22:1, Tetracosenoic acid C24:1, and Linoleic acid C18:2. The highest content of nervonic acid (NA) was found in Xanthoceras sorbifolium Bunge seed oil extracted at 70 °C. Three methods were selected to analyze the extraction rate of nervonic acid in Xanthoceras sorbifolium Bunge seed oil, including urea complexation, low-temperature solvent crystallization, and a combined treatment using these two methods. The final content of nervonic acid obtained was 14.07%, 19.66%, and 40.17%, respectively. The combined treatment method increased the purity of nervonic acid in Xanthoceras sorbifolium Bunge seed oil by 12.62 times. Meanwhile, thermogravimetric behavior analysis of samples extracted using different methods was conducted by thermogravimetric analyzer, which suggested that the thermal stability of the samples extracted by the combined treatment was enhanced. These results can provide a new process parameter and scientific basis for the extraction of NA. At the same time, FTIR and NMR were also used to characterize the combined extraction sample, and the structure of the samples was proved.

Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding.

Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.

Influence of Infill Patterns on the Shape Memory Effect of Cold-Programmed Additively Manufactured PLA.

In four-dimensional additive manufacturing (4DAM), specific external stimuli are applied in conjunction with additive manufacturing technologies. This combination allows the development of tailored stimuli-responsive properties in various materials, structures, or components. For shape-changing functionalities, the programming step plays a crucial role in recovery after exposure to a stimulus. Furthermore, precise tuning of the 4DAM process parameters is essential to achieve shape-change specifications. Within this context, this study investigated how the structural arrangement of infill patterns (criss-cross and concentric) affects the shape memory effect (SME) of compression cold-programmed PLA under a thermal stimulus. The stress-strain curves reveal a higher yield stress for the criss-cross infill pattern. Interestingly, the shape recovery ratio shows a similar trend across both patterns at different displacements with shallower slopes compared to a higher shape fixity ratio. This suggests that the infill pattern primarily affects the mechanical strength (yield stress) and not the recovery. Finally, the recovery force increases proportionally with displacement. These findings suggest a consistent SME under the explored interval (15-45% compression) despite the infill pattern; however, the variations in the mechanical properties shown by the stress-strain curves appear more pronounced, particularly the yield stress.