Anodic Dissolution Characteristics of GH4169 Alloy in NaNO3 Solutions by Roll-Print Mask Electrochemical Machining Using the Linear Cathode.

GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 µm mask hole and cathode size, 10 wt% NaNO3 electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 µm, depth of 92.8 µm and etch factor (EF) of 1.81 show an excellent profile and localization.

Development of temperature-controlled batch and 3-column counter-current protein a system for improved therapeutic purification.

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.

Process development for the production of mesenchymal stromal cell-derived extracellular vesicles in conventional 2D systems.

BACKGROUND: In recent years, the importance of extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) has increased significantly. For their widespread use, a standardized EV manufacturing is needed which often includes conventional, static 2D systems. For these system critical process parameters need to be determined. METHODS: We studied the impact of process parameters on MSC proliferation, MSC-derived particle production including EVs, EV- and MSC-specific marker expression, and particle functionality in a HaCaT cell migration assay. RESULTS: We found that cell culture growth surface and media affected MSCs and their secretory behavior. Interestingly, the materials that promoted MSC proliferation did not necessarily result in the most functional MSC-derived particles. In addition, we found that MSCs seeded at 4 × 103 cells cm-2 produced particles with improved functional properties compared to higher seeding densities. MSCs in a highly proliferative state did not produce the most particles, although these particles were significantly more effective in promoting HaCaT cell migration. The same correlation was found when investigating the cultivation temperature. A physiological temperature of 37°C was not optimal for particle yield, although it resulted in the most functional particles. We observed a proliferation-associated particle production and found potential correlations between particle production and glucose consumption, enabling the estimation of final particle yields. CONCLUSIONS: Our findings suggest that parameters, which must be defined prior to each individual cultivation and do not require complex and expensive equipment, can significantly increase MSC-derived particle production including EVs. Integrating these parameters into a standardized EV process development paves the way for robust and efficient EV manufacturing for early clinical phases.

Selection and Optimization of Carbon-Reinforced Polyether Ether Ketone Process Parameters in 3D Printing-A Rotating Component Application.

The selection of process parameters is crucial in 3D printing for product manufacturing. These parameters govern the operation of production machinery and influence the mechanical properties, production time, and other aspects of the final product. The optimal process parameter settings vary depending on the product and printing application. This study identifies the most suitable cluster of process parameters for producing rotating components, specifically impellers, using carbon-reinforced Polyether Ether Ketone (CF-PEEK) thermoplastic filament. A mathematical programming technique using a rating method was employed to select the appropriate process parameters. The research concludes that an infill density of 70%, a layer height of 0.15 mm, a printing speed of 60 mm/s, a platform temperature of 195 °C, an extruder temperature of 445 °C, and an extruder travel speed of 95 mm/s are optimal process parameters for manufacturing rotating components using carbon-reinforced PEEK material.

Prediction and optimization method for welding quality of components in ship construction.

Welding process, as one of the crucial industrial technologies in ship construction, accounts for approximately 70% of the workload and costs account for approximately 40% of the total cost. The existing welding quality prediction methods have hypothetical premises and subjective factors, which cannot meet the dynamic control requirements of intelligent welding for processing quality. Aiming at the low efficiency of quality prediction problems poor timeliness and unpredictability of quality control in ship assembly-welding process, a data and model driven welding quality prediction method is proposed. Firstly, the influence factors of welding quality are analyzed and the correlation mechanism between process parameters and quality is determined. According to the analysis results, a stable and reliable data collection architecture is established. The elements of welding process monitoring are also determined based on the feature dimensionality reduction method. To improve the accuracy of welding quality prediction, the prediction model is constructed by fusing the adaptive simulated annealing, the particle swarm optimization, and the back propagation neural network algorithms. Finally, the effectiveness of the prediction method is verified through 74 sets of plate welding experiments, the prediction accuracy reaches over 90%.

Influences of Composite Electrodeposition Parameters on the Properties of Ni-Doped Co-Mn Composite Spinel Coatings.

To enhance the comprehensive performance of solid oxide fuel cells (SOFCs) ferritic stainless steel (FSS) interconnectors, a novel approach involving composite electrodeposition and thermal conversion is proposed to prepare Ni-doped Co-Mn composite spinel protective coatings on FSS surfaces. The process involves the composite electrodeposition of a Ni-doped Co-Mn precursor coating, followed by thermal conversion to obtain the Co-Mn-Ni composite spinel coating. Crofer 22H was used as the substrate and orthogonal experiments were designed to investigate the influences of deposition solution pH, stirring rate, cathode current density, and the element content of Mn and Ni on the surface morphology and properties of the composite coatings, respectively. The characterization of the prepared coatings was conducted through macroscopic and microscopic morphology observations of the component surface, energy dispersive spectroscopy (EDS) analysis, and area specific resistance (ASR) testing, etc. Finally, the optimized composite electrodeposition parameters and the Mn-Ni content ratio in the solution were obtained. Experimental results indicated that the composite spinel coating prepared with the optimized process parameters exhibited excellent adhesion to the substrate, and the diffusion and migration of Cr element has been effectively inhibited. Compared with the substrate, the ASR of the coated components has also been decreased simultaneously, which provided an effective method for the surface modification of SOFC FSS interconnectors.

Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing.

In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

Parametric investigation of ultrashort pulsed laser surface texturing on aluminium alloy 7075 for hydrophobicity enhancement.

Hydrophobicity plays a pivotal role in mitigating surface fouling, corrosion, and icing in critical marine and aerospace environments. By employing ultrafast laser texturing, the characteristic properties of a material's surface can be modified. This work investigates the potential of an advanced ultrafast laser texturing manufacturing process to enhance the hydrophobicity of aluminium alloy 7075. The surface properties were characterized using goniometry, 3D profilometry, SEM, and XPS analysis. The findings from this study show that the laser process parameters play a crucial role in the manufacturing of the required surface structures. Numerical optimization with response surface optimization was conducted to maximize the contact angle on these surfaces. The maximum water contact angle achieved was 142º, with an average height roughness (Sa) of 0.87 ± 0.075 µm, maximum height roughness (Sz) of 19.4 ± 2.12 µm, and texture aspect ratio of 0.042. This sample was manufactured with the process parameters of 3W laser power, 0.08 mm hatch distance, and a 3 mm/s scan speed. This study highlights the importance of laser process parameters in the manufacturing of the required surface structures and presents a parametric modeling approach that can be used to optimize the laser process parameters to obtain a specific surface morphology and hydrophobicity. Supplementary Information: The online version contains supplementary material available at 10.1007/s00170-024-12971-8.

A 4H-SiC CMOS Oscillator-Based Temperature Sensor Operating from 298 K up to 573 K.

In this paper, we propose a temperature sensor based on a 4H-SiC CMOS oscillator circuit and that is able to operate in the temperature range between 298 K and 573 K. The circuit is developed on Fraunhofer IISB's 2 µm 4H-SiC CMOS technology and is designed for a bias voltage of 20 V and an oscillation frequency of 90 kHz at room temperature. The possibility to relate the absolute temperature with the oscillation frequency is due to the temperature dependency of the threshold voltage and of the channel mobility of the transistors. An analytical model of the frequency-temperature dependency has been developed and is used as a starting point for the design of the circuit. Once the circuit has been designed, numerical simulations are performed with the Verilog-A BSIM4SiC model, which has been opportunely tuned on Fraunhofer IISB's 2 µm 4H-SiC CMOS technology, and their results showed almost linear frequency-temperature characteristics with a coefficient of determination that was higher than 0.9681 for all of the bias conditions, whose maximum is 0.9992 at a VDD = 12.5 V. Moreover, we considered the effects of the fabrication process through a Monte Carlo analysis, where we varied the threshold voltage and the channel mobility with different values of the Gaussian distribution variance. For example, at VDD = 20 V, a deviation of 17.4% from the nominal characteristic is obtained for a Gaussian distribution variance of 20%. Finally, we applied the one-point calibration procedure, and temperature errors of +8.8 K and -5.8 K were observed at VDD = 15 V.

Application of the Quality by Design Concept (QbD) in the Development of Hydrogel-Based Drug Delivery Systems.

Hydrogel-based drug delivery systems are of interest to researchers for many reasons, such as biocompatibility, high diversity, and the possibility of administration from different routes. Despite these advantages, there are challenges, such as controlling the drug release rate and their mechanical properties during the manufacturing of these systems. For this reason, there is a need for the production and development of such drug delivery systems with a scientific strategy. For this reason, the quality by design (QbD) approach is used for the development of drug delivery systems. This approach, by identifying the most effective factors in the manufacturing of pharmaceutical products and controlling them, results in a product with the desired quality with the least number of errors. In this review article, an attempt is made to discuss the application and method of applying this approach in the development of hydrogel-based drug delivery systems. So that for the development and production of these systems, according to the type of drug delivery system, what target characteristics should be considered (QTPP) and what factors, such as material properties (CMA) or process parameters (CPP), should be taken into account to reach the critical quality attributes of the product (CQA).

Metal-Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm.

Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 µm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.

Three-dimensional printing of cell-laden bioink for blood vessel tissue engineering: influence of process parameters and components on cell viability.

Three-dimensional (3D) bioprinting is a potential therapeutic method for tissue engineering owing to its ability to prepare cell-laden tissue constructs. The properties of bioink are crucial to accurately control the printing structure. Meanwhile, the effect of process parameters on the precise structure is not nonsignificant. We investigated the correlation between process parameters of 3D bioprinting and the structural response of κ-carrageenan-based hydrogels to explore the controllable structure, printing resolution, and cell survival rate. Small-diameter (<6 mm) gel filaments with different structures were printed by varying the shear stress of the extrusion bioprinter to simulate the natural blood vessel structure. The cell viability of the scaffold was evaluated. The in vitro culture of human umbilical vein endothelium cells (HUVECs) on the κ-carrageenan (kc) and composite gels (carrageenan/carbon nanotube and carrageenan/sodium alginate) demonstrated that the cell attachment and proliferation on composite gels were better than those on pure kc. Our results revealed that the carrageenan-based composite bioinks offer better printability, sufficient mechanical stiffness, interconnectivity, and biocompatibility. This process can facilitate precise adjustment of the pore size, porosity, and pore distribution of the hydrogel structure by optimising the printing parameters as well as realise the precise preparation of the internal structure of the 3D hydrogel-based tissue engineering scaffold. Moreover, we obtained perfused tubular filament by 3D printing at optimal process parameters.

Laser powder bed fusion (LPBF) of commercially pure titanium and alloy development for the LPBF process.

Laser powder bed fusion (LPBF) of titanium or titanium alloys allows fabrication of geometrically more complex and, possibly, individualized implants or osteosynthesis products and could thus improve the outcome of medical treatments considerably. However, insufficient LPBF process parameters can result in substantial porosity, decreasing mechanical properties and requiring post-treatment. Furthermore, texturized parts with anisotropic properties are usually obtained after LPBF processing, limiting their usage in medical applications. The present study addresses both: first, a design of experiments is used in order to establish a set of optimized process parameters and a process window for LPBF printing of small commercially pure (CP) titanium parts with minimized volume porosity. Afterward, the first results on the development of a biocompatible titanium alloy designed for LPBF processing of medical implants with improved solidification and more isotropic properties are presented on the basis of conventionally melted alloys. This development was performed on the basis of Ti-0.44O-0.5Fe-0.08C-0.4Si-0.1Au, a near-α alloy presented by the authors for medical applications and conventional manufacturing, with yttrium and boron additions as additional growth restriction solutes. In terms of LPBF processing of CP titanium grade 1 powder, a high relative density of approximately 99.9% was obtained in the as-printed state of the volume of a small cubical sample by using optimized laser power, scanning speed, and hatch distance in combination with a rotating scanning pattern. Moreover, tensile specimens processed with these volume settings and tested in the as-printed milled state exhibited a high average yield and ultimate tensile strength of approximately 663 and 747 N/mm2, respectively, combined with a high average ductility of approximately 24%. X-ray diffraction results suggest anisotropic mechanical properties, which are, however, less pronounced in terms of the tested specimens. Regarding alloy development, the results show that yttrium additions lead to a considerable microstructure refinement but have to be limited due to the occurrence of a large amount of precipitations and a supposed higher propensity for the formation of long columnar prior ß-grains. However, phase/texture and microstructure analyses indicate that Ti-0.44O-0.5Fe-0.08C-0.4Si-0.1Au-0.1B-0.1Y is a promising candidate to achieve lower anisotropy during LPBF processing, but further investigations on LPBF printing and Y2O3 formation are necessary.

Precision Grinding Technology of Silicon Carbide (SiC) Ceramics by Longitudinal Torsional Ultrasonic Vibrations.

Silicon carbide (SiC) ceramic material has become the most promising third-generation semiconductor material for its excellent mechanical properties at room temperature and high temperature. However, SiC ceramic machining has serious tool wear, low machining efficiency, poor machining quality and other disadvantages due to its high hardness and high wear resistance, which limits the promotion and application of such materials. In this paper, comparison experiments of longitudinal torsional ultrasonic vibration grinding (LTUVG) and common grinding (CG) of SiC ceramics were conducted, and the longitudinal torsional ultrasonic vibration grinding SiC ceramics cutting force model was developed. In addition, the effects of ultrasonic machining parameters on cutting forces, machining quality and subsurface cracking were investigated, and the main factors and optimal parameters affecting the cutting force improvement rate were obtained by orthogonal tests. The results showed that the maximum improvement of cutting force, surface roughness and subsurface crack fracture depth by longitudinal torsional ultrasonic vibrations were 82.59%, 22.78% and 30.75%, respectively. A longitudinal torsional ultrasonic vibrations cutting force prediction model containing the parameters of tool, material properties and ultrasound was established by the removal characteristics of SiC ceramic material, ultrasonic grinding principle and brittle fracture theory. And the predicted results were in good agreement with the experimental results, and the maximum error was less than 15%. The optimum process parameters for cutting force reduction were a spindle speed of 22,000 rpm, a feed rate of 600 mm/min and a depth of cut of 0.011 mm.

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys.

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

Satisfying QTPP of Erythropoietin Biosimilar by QbD through DoE-Derived Downstream Process Engineering.

Well-characterized and scalable downstream processes for the purification of biologics are extremely demanding for delivering quality therapeutics to patients at a reasonable price. Erythropoietin (EPO) is a blockbuster biologic with diverse clinical applications, but its application is limited to financially well-off societies due to its high price. The high price of EPO is associated with the technical difficulties related to the purification challenge to obtain qualified products with a cost-effective defined process. Though there are reports for the purification of EPO there is no report of a well-characterized downstream process with critical process parameters (CPPs) that can deliver EPO consistently satisfying the quality target product profile (QTPP), which is a critical regulatory requirement. To advance the field, we applied the quality by design (QbD) principle and design of experiment (DoE) protocol to establish an effective process, which is scalable up to 100× batch size satisfying QTPP. We have successfully transformed the process from static mode to dynamic mode and validated it. Insignificant variation (p > 0.05) within and between 1×, 10×, and 100× batches showed that the process is reproducible and seamlessly scalable. The biochemical analysis along with the biofunctionality data ensures that the products from different scale batches were indifferent and comparable to a reference product. Our study thereby established a robust and scalable downstream process of EPO biosimilar satisfying QTPP. The technological scheme presented here can speed up the production of not only EPO but also many other life-saving biologics and make them available to the mass population at a reduced cost.

Predicting Residence Time and Melt Temperature in Pharmaceutical Hot Melt Extrusion.

Hot-melt extrusion is increasingly applied in the pharmaceutical area as a continuous processing technology, used to design custom products by co-processing drugs together with functional excipients. In this context, the residence time and processing temperature during extrusion are critical process parameters for ensuring the highest product qualities, particularly of thermosensitive materials. Within this study, a novel strategy is proposed to predict the residence time distribution and melt temperature during pharmaceutical hot-melt extrusion processes based on experimental data. To do this, an autogenic extrusion mode without external heating and cooling was applied to process three polymers (Plasdone S-630, Soluplus and Eudragit EPO) at different specific feed loads, which were set by the screw speed and the throughput. The residence time distributions were modeled based on a two-compartment approach that couples the behavior of a pipe and a stirred tank. The throughput showed a substantial effect on the residence time, whereas the influence of the screw speed was minor. On the other hand, the melt temperatures during extrusion were mainly affected by the screw speed compared to the influence of the throughput. Finally, the compilation of model parameters for the residence time and the melt temperature within design spaces serve as the basis for an optimized prediction of pharmaceutical hot-melt extrusion processes.

Approaching Polycarbonate as an LFT-D Material: Processing and Mechanical Properties.

Long-fiber thermoplastic (LFT) materials compounded via the direct LFT (LFT-D) process are very versatile composites in which polymers and continuous reinforcement fiber can be combined in almost any way. Polycarbonate (PC) as an amorphous thermoplastic matrix system reinforced with glass fibers (GFs) is a promising addition regarding the current development needs, for example battery enclosures for electromobility. Two approaches to the processing and compression molding of PC GF LFT-D materials with various parameter combinations of screw speed and fiber rovings are presented. The resulting fiber lengths averaged around 0.5 mm for all settings. The tensile, bending, Charpy, and impact properties were characterized and discussed in detail. Special attention to the characteristic charge and flow area formed by compression molding of LFT-D materials, as well as sample orientation was given. The tensile modulus was 10 GPa, while the strength surpassed 125 MPa. The flexural modulus can reach up to 11 GPa, and the flexural strength reached up to 216 MPa. PC GF LFT-D is a viable addition to the LFT-D process, exhibiting good mechanical properties and stable processability.

A Fast Prediction Model for Liquid Metal Transfer Modes during the Wire Arc Additive Manufacturing Process.

The liquid metal transfer mode in wire arc additive manufacturing (WAAM), plays an important role in determining the build quality. In this study, a fast prediction model based on the Young-Laplace equation, momentum equation, and energy conservation, is proposed, to identify the metal transfer modes, including droplet, liquid bridge, and wire stubbing, for a given combination of process parameters. To close the proposed model, high-fidelity numerical simulations are applied, to obtain the necessary inputs required by the former. The proposed model's accuracy and effectiveness are validated by using experimental data and high-fidelity simulation results. It is proved that the model can effectively predict the transition from liquid bridge, to droplet and wire stubbing modes. In addition, its errors in dripping frequency and liquid bridge height range from 6% to 18%. Moreover, the process parameter windows about transitions of liquid transfer modes have been established based on the model, considering wire feed speed, travel speed, heat source power, and material parameters. The proposed model is expected to serve as a powerful tool for the guidance of process parameter optimization, to achieve high-quality builds.

Effect of screw speed and die temperature on physicochemical, textural, and morphological properties of soy protein isolate-based textured vegetable protein produced via a low-moisture extrusion.

In this study, textured vegetable protein (TVP) based on soy protein isolate, wheat gluten, and corn starch was prepared at a 5:3:2 (w/w) ratio using a low-moisture extrusion process. To evaluate the effects of extrusion parameters, die temperature and screw rotation speed, on the properties of TVP, these two parameters were manipulated at a constant barrel temperature and moisture content. The results indicated that increasing the die temperature increased the expansion ratio while decreasing the density of the extrudates. Simultaneously, increasing the screw rotation speed clearly increased the specific mechanical energy of the TVP. Furthermore, mathematical modelling suggested that the expansion ratio increases exponentially to the die temperature. However, extreme process conditions bring about a decrease in water absorption capacity and expansion ratio, as well as undesirable texture and microstructure. The results suggested that the properties of SPI-based TVP are directly influenced by the extrusion process parameters, screw speed and die temperature. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01207-8.