Additional Processing Research Articles

Comparative evaluation of drying methods for struvite produced from electrocoagulated source-separated urine: Implications for quality, energy and cost-effectiveness

Struvite precipitation from source-separated urine is crucial for waste utilization and sustainability. However, after precipitation, the high moisture content of struvite necessitates an additional drying process that can be costly and inefficient. In the present study, the performance of different drying methods—open sun drying, air drying, conventional drying (20–100 °C), and microwave drying (180–720 W) on the quality of struvite obtained from source-separated urine through electrocoagulation using Mg–Mg electrodes were evaluated. It was found that higher temperatures and power in the convective oven and microwave resulted in higher diffusivity (10−9-10−7 m2s−1), leading to reduced drying times. Different models were employed to comprehend the drying mechanism, and the one with the highest correlation coefficient (R2 = 0.99) and the lowest statistical values was selected. The key findings indicated that higher power and temperature levels were more cost-effective. However, characterization of the dried struvite using X-ray diffraction and Fourier-transformed infrared spectroscopy, disintegration of struvite crystals at temperatures above 60 °C in the conventional oven and 180 W in the microwave oven was observed. Based on the results, we conclude that sun drying is a cost-effective and environmentally friendly alternative for drying struvite without compromising its quality.

Exploration and Practice of Nitrogen Addition Process for LF Refining Ladle Bottom Blowing Nitrogen Steel Liquid

Exploration and Practice of Nitrogen Addition Process for LF Refining Ladle Bottom Blowing Nitrogen Steel Liquid

Microstructure, hardness, and tribological performance of D2 tool steel fabricated by laser cladding using pulsed wave and substrate heating

D2 steel is a tool grade steel that presents hot cracking problems during the solidification process due to its alloying elements. On the other hand, additive manufacturing processes have great potential to manufacture, repair, and functionalize metal parts due to their low heat input, minimal distortion, and excellent metallurgical bonding. In this work, D2 metallic powder was deposited by the laser cladding process using pulsed wave, without and with preheating of the substrate. Through ANOVA statistical analysis using a complete factorial design 24, the optimal regions were determined to obtain D2 deposits with minimum dilution and depth, and maximum widths, heights and high hardness. Once the optimal parameters of the process were established, the coatings were deposited without and with preheating the substrate where, regardless of the condition, coatings without defects and good metallurgical adhesion were obtained. Coatings with preheating obtained higher cooling rates, where lower laser powers promoted higher rates; while in the coatings without preheating, lower duty functions promoted higher rates. The austenite, martensite and M7C3 eutectic carbides phases were obtained, with austenite being the main phase due to the C content in solid solution due to the cooling rates achieved. The coatings in as-built condition, reached hardness values between 606 and 711 HV (55–60 HRC), where the preheated coatings obtained the highest values. On the contrary, the coatings without preheating showed a better wear resistance (7.672 x10-6 mm3/Nm) due to the formation of tribo-layers due to the formation of oxides due to the higher amount of austenite.

The influence of deformation affected region on microstructure and mechanical property of 316L fabricated by hybrid additive-subtractive manufacturing

The hybrid of additive manufacturing and subtractive manufacturing is promising in producing high-quality and high-accuracy parts made of hard-to-machine metallic materials with complex geometries. However, few studies have been conducted to analyze the influence of responses from subtractive manufacturing, especially the plastic deformation region induced by mechanical cutting, on the following additive manufacturing processes. This paper comprehensively investigates the impact of finishing milling on the mechanical properties and quality of 316L fabricated by hybrid additive-subtractive manufacturing. The whole manufacturing process consisted of a two-stage micro selective laser melting μSLM process with three scanning speeds and one finishing milling process. Typical machining responses including phase, cutting force, surface roughness at different manufacturing stages were analyzed; the mechanisms of how the deformation-affected region induced in subtractive manufacturing process affects the microstructure, hardness, and mechanical properties after the second-stage additive manufacturing were explored. Fine grains were formed after the second-stage μSLM in the connected region, because of the lower G/R ratio and the higher G × R value, whereas the hardness in this region was lower than the bulk material consisting of coarse grains, caused by the thermal softening effect induced in mechanical cutting. The elongation rate was increased due to the existence of the grain-boundary-rich connected region.

ADVANCES IN ULTRAPRECISION DIAMOND TURNING: TECHNIQUES, APPLICATIONS, AND FUTURE TRENDS

Advances in ultraprecision diamond turning have revolutionized manufacturing processes across various industries, offering unparalleled precision and surface quality in the fabrication of optical components, microfluidic devices, and advanced mechanical parts. This review delves into the techniques, applications, and future trends in ultraprecision diamond turning, highlighting recent advancements and potential trajectories. Techniques in ultraprecision diamond turning have evolved significantly, driven by innovations in machine design, tooling materials, and control systems. Diamond turning machines equipped with ultra-stiff structures, high-speed spindles, and advanced feedback mechanisms enable sub-nanometer level accuracy and surface finishes down to Angstrom levels. Additionally, advancements in single-point diamond turning (SPDT), fast tool servo (FTS), and deterministic microgrinding (DMG) techniques further enhance the versatility and precision of the process. Applications of ultraprecision diamond turning span a wide range of industries, including aerospace, automotive, biomedical, and telecommunications. In optics manufacturing, diamond turning facilitates the production of aspheric lenses, freeform optics, and diffractive optical elements with unprecedented accuracy, contributing to the development of high-performance imaging systems and laser applications. Moreover, in the biomedical field, diamond-turned microfluidic devices enable precise control over fluid flow and particle manipulation, empowering advancements in drug delivery systems and lab-on-a-chip technologies. Future trends in ultraprecision diamond turning are poised to address challenges related to scalability, multi-material processing, and in-situ metrology. Integration of adaptive control algorithms and machine learning techniques promises enhanced process stability and predictive maintenance, optimizing productivity and reducing downtime. Furthermore, the development of hybrid manufacturing approaches, combining diamond turning with additive manufacturing or laser processing, offers novel avenues for fabricating complex, multi-functional components with improved efficiency and cost-effectiveness. The ongoing advancements in ultraprecision diamond turning techniques, coupled with diverse applications across industries, underscore its pivotal role in advancing manufacturing capabilities. Anticipated future trends hold promise for further expanding the scope and impact of this technology, driving innovation and pushing the boundaries of precision engineering.
 Keywords: Ultraprecision, Diamond, Turning, Technique, Review.

A CALPHAD-Informed Enthalpy Method for Multicomponent Alloy Systems with Phase Transitions

Solid–liquid phase transitions of metals and alloys play an important role in many technical processes. Therefore, corresponding numerical process simulations need adequate models. The enthalpy method is the current state-of-the-art approach for this task. However, this method has some limitations regarding multicomponent alloys as it does not consider the enthalpy of mixing, for example. In this work, we present a novel CALPHAD-informed version of the enthalpy method that removes these drawbacks. In addition, special attention is given to the handling of polymorphic as well as solid–liquid phase transitions. Efficient and robust algorithms for the conversion between enthalpy and temperature were developed. We demonstrate the capabilities of the presented method using two different implementations: a lattice Boltzmann and a finite difference solver. We proof the correct behaviour of the developed method by different validation scenarios. Finally, the model is applied to electron beam powder bed fusion—a modern additive manufacturing process for metals and alloys that allows for different powder mixtures to be alloyed in situ to produce complex engineering parts. We reveal that the enthalpy of mixing has a significant effect on the temperature and lifetime of the melt pool and thus on the part properties.

Simulation Study on Temperature and Stress Fields in Mg-Gd-Y-Zn-Zr Alloy during CMT Additive Manufacturing Process.

A new heat source combination, consisting of a uniform body heat source and a tilted double ellipsoidal heat source, has been developed for cold metal transfer (CMT) wire-arc additive manufacturing of Mg-Gd-Y-Zn-Zr alloy. Simulations were conducted to analyze the temperature field and stress distribution during the process. The optimal combination of feeding speed and welding speed was found to be 8 m/min and 8 mm/s, respectively, resulting in the lowest thermal accumulation and residual stress. Z-axis residual stress was identified as the main component of residual stress. Electron Backscatter Diffraction (EBSD) testing showed weak texture strength, and Kernel Average Misorientation (KAM) analysis revealed that the 1st layer had the highest residual stress, while the 11th layer had higher residual stress than the 6th layer. Microhardness in the 1st, 11th, and 6th layers varies due to residual stress impacts on dislocation density. Higher residual stress increases dislocation density, raising microhardness in components. The experimental results were highly consistent with the simulated results.

Additive Metallization of Alumina with Copper-Titanium Powder Blends for Power Electronic Applications

Additive manufacturing shows great potential to further increase the performance of power electronic modules through novel packaging concepts. Such an approach is the integrated manufacturing of metal-ceramic substrates by means of laser powder bed fusion of metals (PBF-LB/M). With this layered additive manufacturing process, planar ceramic substrates can be metallized and electrically functionalized for power electronic applications. In this paper Al2O3 ceramic substrates are metallized via PBF-LB/M by selectively melting applied powder layers with software defined geometries. The investigated powders are mixtures of copper and titanium powders with 1 wt.%, 5 wt.% and 10 wt.% titanium in order to enable bonding by creating a titanium-oxide reaction layer at the interface to the ceramic. Shear tests and microstructural investigations show that a subsequent heat treatment increases the adhesion and density of the metallization. By energy dispersive X-ray microscopy (EDX) the partial formation of reaction layers is detected.

A Quick, Highly Selective and Sensitive Colorimetric and Fluorimetric Sensor for Cyanide Ion.

A newly developed diindolium moiety has been synthesized and structurally investigated by employing a number of spectroscopic methods like NMR and HRMS in order to serve as a cyanide sensor DI. The interaction between DI and the CN- ion causes a noticeable color shift from pink to colorless, making it easy to detect CN- ions with the naked eye. Besides, the sensor exhibited fluorescence color change from orange to light blue under UV lamp. Sensor DI has remarkable selectivity and sensitivity in distinguishing between CN- and a wide range of interfering anions. The sensing mechanism of sensor DI towards CN- ion involves the nucleophilic addition process of CN- to the electron deficient indolium moiety. The detection limit of cyanide ion by sensor DI is calculated to be 1.4 × 10- 7 M by UV-visible and 8.2 × 10- 8 M by fluorescence technique which are lower than the limit set by WHO. The application of sensor DI for cyanide ion is utilized by making test kit and by taking different sources of water to test the presence of cyanide ion.

Optimizing dispensing performance of needle-type piezoelectric jet dispensers: a novel drive waveform approach

The dispensing performance of needle-type piezoelectric jet dispenser constitutes a crucial factor that ensures the quality of additive manufacturing processes. In this paper, a novel approach is proposed to enhance the dispensing performance of needle-type piezoelectric jetting dispensers by introducing a more adaptable driving waveform based on Bézier curves. Initially, the approach considers the electromechanical coupling effect of the needle-type piezoelectric dispenser and constructs a high-precision fluid–solid coupling model of the dispensing process. Subsequently, a multi-physics field joint simulation platform combining Matlab and Fluent is established to systematically analyze control strategies in real service conditions. Next, a new driving waveform based on Bézier curves is introduced, and the control parameters are optimized using a genetic algorithm to address issues such as air bubbles in the droplets and instability of the dispensing process. The optimized waveform based on the Bézier curve reduces the volume of air suction during the dispensing process by over 20% compared to the traditional waveform and eliminates the uncontrolled vibration state of the needle in the fluid, ensuring the stability of the entire fluid refill process. Finally, the optimized control strategy is verified through experiments and compared with traditional methods. The experiment demonstrates its advantages in addressing issues with no air bubbles in the droplets and consistency of the droplets. This study provides valuable insights into optimizing the dispensing performance of needle-type piezoelectric jetting dispensers regarding control strategy.

Resolving syntactic-semantic conflicts: comprehension and processing patterns by deaf Chinese readers.

The current study combined sentence plausibility judgment and self-paced reading tasks to examine the comprehension strategies and processing patterns of Chinese deaf individuals when comprehending written Chinese sentences with syntactic-semantic cue conflicts. Similar to findings from previous crosslinguistic studies on deaf readers, the Chinese deaf readers showed great variability in their comprehension strategies, with only 38% robustly relying on syntactic cues. Regardless of their overall comprehension preferences, the deaf readers all showed additional processing efforts as reflected by longer reading time at the verb regions when they relied on the syntactic cues. Those with less robust reliance on syntactic cues also showed longer reading time at the verb regions even when they relied on the semantic cues, suggesting sensitivity to the syntactic cues regardless of the comprehension strategy. These findings suggest that deaf readers in general endure more processing burden while resolving conflicting syntactic and semantic cues, likely due to their overall high reliance on semantic information during sentence comprehension. Increased processing burden thus may contribute to an overall tendency of over-reliance on semantic cues when comprehending sentences with cue conflicts.

Predictive tools for the cooling rate-dependent microstructure evolution of AISI 316L stainless steel in additive manufacturing

This study presents a thorough evaluation of advanced predictive tools for the solidification microstructure of AISI 316L stainless steel, taking into account both chemical composition and cooling rate. In order to achieve a broad range of cooling rates comparable to prominent additive manufacturing processes, autogenous laser welds were carried out on the steel plate by means of powderless directed energy deposition (DED) and selective laser melting (SLM) machines. Computational analysis of cooling rates and metallographic investigations revealed that DED welds solidify at moderate rates, exhibiting a primary ferrite (FA) solidification mode. Conversely, SLM welds solidify at significantly higher rates, showing dual solidification modes: initial austenite (A) at the fusion boundary and subsequent ferrite (F) at the interior. The WRC-1992 constitution diagram predicts the FA mode in accordance with the findings in the DED welds. An implicated Schaeffler diagram, proposed for a comparable cooling rate, also predicts the same mode. An artificial neural network model, referred to as ORFN, consistently predicts the variation of ferrite content within the DED welds based on the cooling rate variation. The dual mode observed in the SLM welds aligns with established knowledge regarding the alteration of stainless steel solidification under extremely high cooling rates. While the occurrence of the A mode at the fusion boundary is anticipated according to implicated Schaeffler diagrams for extremely high cooling rates, the transition to the F mode is not addressed explicitly.

Enabling cross-morphological performance comparison: A case study in heat management design

Cross-morphological performance comparisons in mechanical engineering design may well resemble comparisons of apples and oranges. They are meaningful only if pertinent properties shared across compared morphologies (so-calledtertia comparationis) are identified, and equity with regard to these properties is established. This article demonstrates such an approach with the use of parametric geometry modeling, optimization, and additive manufacturing, enabling a thermal performance comparison of different heat sink morphologies, using both steady-state numerical simulation and experimental evaluation, by establishing equity across the compared heat sinks in terms of (thermal) mass. Limitations in additive manufacturing precision, as well as inconsistencies between numerical simulation and experimental evaluation results, were encountered in this case study. Likely resulting from the heat sinks’ thermal properties on the additive manufacturing process and the simulation environment’s disproportionate reliance on convection surface area, these limitations and inconsistencies will likely diminish as additive manufacturing and numerical simulation technologies improve. The cross-morphological comparison approach presented here is shown to be viable in principle and may inform decision-making in applied mechanical engineering design research and practice.

Expanding the Massachusetts Birth Defects Monitoring Program to include additional pregnancy outcomes: Programmatic efforts and impacts on case ascertainment, 2012-2020.

Birth defects affect 1 in 33 infants in the United States and are a leading cause of infant mortality. Birth defects surveillance is crucial for informing public health action. The Massachusetts Birth Defects Monitoring Program (MBDMP) began collecting other pregnancy losses (OPLs) in 2011, including miscarriages (<20 weeks gestation) or elective terminations (any gestational age), in addition to live births and stillbirths (≥20 weeks gestation). We describe programmatic changes for adding OPLs and their impact on prevalence estimates. Using population-based, statewide, data from the MBDMP (2012-2020), we assessed prevalence per 10,000 live births and 95% confidence intervals (CIs) with and without OPLs overall and for specific birth defects by time period, maternal age, and race/ethnicity. Including OPLs required amending a state statute and promulgating regulations, new data sources, and additional data processing, cleaning, and verification. Overall prevalence with OPLs increased from 257.4 (95% CI: 253.5-261.4) to 333.9 (95% CI: 329.4-338.4) per 10,000; increases were observed in all time periods, age, and race/ethnicity groups. After including OPLs, the prevalence increased for neural tube defects [3.2 (2.7-3.6) to 8.3 (7.6-9.0)], and trisomies 13 [0.5 (0.3-0.7) to 4.1 (3.6-4.6)], 18 [1.5 (1.2-1.9) to 8.2 (7.5-8.9)], and 21 [12.3 (11.4-13.2) to 28.9 (27.6-30.2)]. Cardiovascular defects increased slightly, while prevalence of eye/ear, respiratory, and gastrointestinal defects remained similar. Adding OPLs required substantial programmatic efforts and resulted in more complete case ascertainment, particularly for certain birth defects. More complete case ascertainment will allow for improved research, screening, and resource allocation.

High-definition direct-print of metallic microdots with optical vortex induced forward transfer

We demonstrate high-definition, direct-printing of micron-scale metallic dots, comprised of close-packed gold nanoparticles, by utilizing the optical vortex laser-induced forward transfer technique. We observe that the spin angular momentum of the optical vortex, associated with circular polarization, assists in the close-packing of the gold nanoparticles within the printed dots. The printed dots exhibit excellent electrical conductivity without any additional sintering processes. This technique of applying optical vortex laser-induced forward transfer to metallic dots is an innovative approach to metal printing, which does not require additional sintering. It also serves to highlight new insights into light–matter interactions.

Effects of Contaminations on Electric Arc Behavior and Occurrence of Defects in Wire Arc Additive Manufacturing of 316L-Si Stainless Steel

Additive Manufacturing is a manufacturing process that consists of obtaining a three-dimensional object from the deposition of material layer by layer, unlike conventional subtractive manufacturing methods. Wire Arc Additive Manufacturing stands out for its high productivity among the Additive Manufacturing technologies for manufacturing metal parts. On the other hand, the excessive heat input promotes increased residual stress levels and the occurrence of defects, such as pores, voids, a lack of fusion, and delamination. These defects result in abnormalities during the process, such as disturbances in electrical responses. Therefore, process monitoring and the detection of defects and failures in manufactured items are of fundamental importance to ensure product quality and certify the high productivity characteristic of this process. Thus, this work aimed to characterize the effects of different contaminations on the electric arc behavior of the Wire Arc Additive Manufacturing process and the occurrence of microscopic defects in thin walls manufactured by this process. To investigate the presence of defects in the metal preforms, experimental conditions were used to promote the appearance of defects, such as the insertion of contaminants. To accomplish the electric arc behavior analysis, voltage and current temporal data were represented through histograms and cyclograms, and the arc stability was assessed based on the Vilarinho index for a short circuit. Effectively, the introduction of contaminants caused electric arc disturbances that led to the appearance of manufacturing defects, such as inclusions and porosities, observed through metallographic characterization. The results confirm that the introduction of contaminations could be identified early in the Wire Arc Additive Manufacturing process through electric arc data analysis.

Subtractive processing and surface integrity of additive manufacturing materials

PurposeAs an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.Design/methodology/approachFirstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.FindingsIn this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.Originality/valueWith the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Comparative Evaluation of Titanium Feedstock Powder Derived from Recycled Battlefield Scrap vs. Virgin Powder for Cold Spray Processing.

Gas-atomization is extensively used to produce metallic feedstock powders for additive manufacturing processes, including gas dynamic cold spray processing. This work explores the potential utility of on-demand recycled titanium scrap feedstock powder as a viable substitute for virgin powder sources. Three recycled titanium powders were atomized from different battlefield scrap sources using a mobile foundry developed by MolyWorks Materials Corporation. Recycled titanium alloy powders were compared against virgin Ti-6Al-4V powder to verify there were no significant variations between the recycled and virgin materials. Powder characterization methods included chemical analysis, particle size distribution analysis, scanning electron microscopy (SEM), Karl Fischer (KF) titration moisture content analysis, X-ray diffraction (XRD) phase analysis, microparticle compression testing (MCT), and nanoindentation. Results indicate that recycled titanium powder provides a viable alternative to virgin titanium alloy powders without compromising mechanical capabilities, microstructural features, or ASTM-specified composition and impurity standards. The results of this work will be used to aid future research efforts that will focus on optimizing cold spray parameters to maximize coating density, mechanical strength, and hardness of recycled titanium feedstock powders. "Cold spray" presents opportunities to enhance the sustainability of titanium component production through the utilization of recycled feedstock powder, mitigating issues of long lead times and high waste associated with the use of conventional virgin feedstock.

Optimizing mechanical properties of virgin and recycled PLA components using Anova and neural networks

The increasing demand for polymers in additive manufacturing (AM) has led to a significant increase in plastic waste, with over 300 million metric tons used in recent years. This research article explores the use of Poly Lactic Acid (PLA) as a biodegradable thermoplastic recycled material for 3D printed components, comparing its properties with virgin PLA and discussing solutions for variation and mechanical features improvement. Fused Deposition Modeling (FDM) is a widely used additive manufacturing process that allows for the creation of three-dimensional objects by depositing molten material layer by layer. This study investigates the impact of infill density, layer thickness, and raster angle for recycled 3D printing material, focusing on their dimensions and their influence on processing efficiency. This research paper aims to investigate the mechanical effects of recycled 3d printed components which are printed by using FDM with the combination of different process parameters compared with virgin PLA. From results optimal process parameters are found to enhancing quality and performance of recycled 3D printed components. Later results are compared by Analysis of Variance (ANOVA) as a statistical tool and also with ANN technique, which minimizes error deviation.

Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm

As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R2) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R2 = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.