Defects in Metal Additive Manufacturing: Formation, Process Parameters, Postprocessing, Challenges, Economic Aspects, and Future Research Directions.

Metal additive manufacturing (AM) is a revolutionary technological advancement that has made significant inroads in a wide range of sectors, including aerospace, defense, automotive, health care, and engineering applications. It offers unprecedented design freedom, reduced material waste, and enhanced performance, in addition to significant enhancements to fabrication processes. Microstructural defects and internal stresses formed during fabrication directly affect the fabricated product's surface integrity, quality, and service life. Identification, characterization, and prediction of these defects help significant and direct production of defect-free structures with high density. This article provides detailed insights concerning the common defects, mitigation techniques, and challenges reported in both powder bed fusion-based and wire arc AM methods. Defects such as porosity may develop due to the powder sphericity, roughness of the powder, preheating, process parameters, build environment, postprocessing techniques, and environmental factors. Therefore, a critical study of the techniques, alloys, process parameter optimization, and different postprocessing techniques to tone down the defects is made from their formations.

The Effects of Laser-Assisted Winding Process Parameters on the Tensile Properties of Carbon Fiber/Polyphenylene Sulfide Composites.

Currently, there is limited research on the in situ forming process of thermoplastic prepreg tape winding, and the unclear impact of process parameters on mechanical properties during manufacturing is becoming increasingly prominent. The study aimed to investigate the influence of process parameters on the mechanical properties of thermoplastic composite materials (CFRP) using laser-assisted CF/PPS winding forming technology. The melting point and decomposition temperature of CF/PPS materials were determined using DSC and TGA instruments, and based on the operating parameters of the laser-assisted winding equipment, the process parameter range for this fabrication technology was designed. A numerical model for the temperature of laser-heated CF/PPS prepreg was established, and based on the filament winding process setup, the heating temperature and tensile strength were simulated and tested. The effects of process parameters on the heating temperature of the prepreg and the tensile strength of NOL rings were then analyzed. The non-dominated sorting genetic algorithm (NSGA-II) was employed to globally optimize the process parameters, aiming to maximize winding rate and tensile strength. The results indicated that core mold temperature, winding rate, laser power, and their interactions significantly affected mechanical properties. The optimal settings were 90 °C, 418.6 mm/s, and 525 W, achieving a maximum tensile strength of 2571.51 MPa. This study provides valuable insights into enhancing the forming efficiency of CF/PPS-reinforced high-performance engineering thermoplastic composites.

Temperature mapping of milling by dual centrifugation: A systematic investigation.

Using low quantities of drug compounds is often favorable in the early stages of drug development, especially for what require a large screening investigation to define the final formulation composition, such as nano- and microsuspensions. For that reason, the dual centrifugation approach has in the recent years been used due to its reproducible and fast-milling capacity with 40 samples in 2 mL vials simultaneously without the addition of cooling breaks due to a built-in cooling system. Nonetheless, heat can be dissipated into the samples during high-intensity milling, resulting in increased sample temperatures that potentially can affect thermolabile compounds and potential influence the obtained suspensions in the screening experiments if the used stabilizer has temperature dependent variations in the performance. Hence, a systematic investigation of the influence of different process parameters on the heat dissipation in samples during milling by the dual centrifugation approach was performed in the present study. It was found that the milling speed had the highest impact on the final sample temperature, but also other parameters, such as the bead loading, bead size, and placement in the centrifuge during milling had significantly influenced the final mean temperature of the milling media. Higher temperatures were obtained with higher bead loadings, i.e., 3000 mg milling beads/mL and milling speeds (1500 rpm), and when smaller milling beads, i.e., 0.1 mm, were used during production. The study further showed that higher temperatures were measured for samples located on the bottom disk during milling, and also when located on the outer placement on the sample disk. Upscale investigations showed immensely increased sample temperatures (almost up to boiling point) when samples were prepared under similar formulation parameters and milling speed as small-volume vials. Furthermore, the study indicated that the addition of drug compounds during suspension preparation decreased the final sample temperature compared to samples that only contained purified water due to energy absorption of the drug compound.

Effect of the gas layer evolution on electrolytic plasma polishing of stainless steel.

In recent years, electrolytic plasma polishing technology has attracted wide attention due to its advantages of shape adaptability, high efficiency, better precision, environmental friendliness, and non-contact polishing. However, the lack of research on the evolution mechanism of the gas layer at the anode interface restricts the improvement of the material removal mechanism and the regulation of the polishing effect. Firstly, the thermodynamic conditions of gas layer formation were analyzed based on the Clapeyron-Clausius equation, and the key parameters affecting the gas layer were identified. Secondly, the laws of voltage and electrolyte temperature on the dynamic evolution of the gas layer and its polishing effect were revealed. Additionally, the influence of the gas layer on the voltage-current characteristics was also investigated by analyzing the experimental phenomena. The results indicate that the optimal polishing effect is achieved at a voltage level of 300 V resulting in a decrease in Ra from 0.451 µm to 0.076 µm. Similarly, superior polishing results are obtained when the electrolyte temperature is 80 °C, with a decrease in Ra from 0.451 µm to 0.075 µm. This study provides theoretical guidance for the further development and application of electrolytic plasma polishing technology.

Controllable Fabrication of Gallium Ion Beam on Quartz Nanogrooves.

Nanogrooves with high aspect ratios possess small size effects and high-precision optical control capabilities, as well as high specific surface area and catalytic performance, demonstrating significant application value in the fields of optics, semiconductor processes, and biosensing. However, existing manufacturing methods face issues such as complexity, high costs, low efficiency, and low precision, especially in the difficulty of fabricating nanogrooves with high resolution on the nanoscale. This study proposes a method based on focused ion beam technology and a layer-by-layer etching process, successfully preparing V-shaped and rectangular nanogrooves on a silicon dioxide substrate. Combining with cellular automaton algorithm, the ion sputtering flux and redeposition model was simulated. By converting three-dimensional grooves to discrete rectangular slices through a continuous etching process and utilizing the sputtering and redeposition effects of gallium ion beams, high-aspect-ratio V-shaped grooves with up to 9.6:1 and rectangular grooves with nearly vertical sidewalls were achieved. In addition, the morphology and composition of the V-shaped groove sidewall were analyzed in detail using transmission electron microscopy (TEM) and tomography techniques. The influence of the etching process parameters (ion current, dwell time, scan times, and pixel overlap ratio) on groove size was analyzed, and the optimized process parameters were obtained.

Physics-informed neural networks for biopharmaceutical cultivation processes: Consideration of varying process parameter settings.

We present a new modeling approach for the study and prediction of important process outcomes of biotechnological cultivation processes under the influence of process parameter variations. Our model is based on physics-informed neural networks (PINNs) in combination with kinetic growth equations. Using Taylor series, multivariate external process parameter variations for important variables such as temperature, seeding cell density and feeding rates can be integrated into the corresponding kinetic rates and the governing growth equations. In addition to previous approaches, PINNs also allow continuous and differentiable functions as predictions for the process outcomes. Accordingly, our results show that PINNs in combination with Taylor-series expansions for kinetic growth equations provide a very high prediction accuracy for important process variables such as cell densities and concentrations as well as a detailed study of individual and combined parameter influences. Furthermore, the proposed approach can also be used to evaluate the outcomes of new parameter variations and combinations, which enables a saving of experiments in combination with a model-driven optimization study of the design space.

Exploring Osmotic Dehydration for Food Preservation: Methods, Modelling, and Modern Applications.

This study summarizes the most recent findings on osmotic dehydration, a crucial step in food preservation. The many benefits of osmotic dehydration are listed, including longer shelf life and preserved nutritional value. Mass transfer dynamics, which are critical to understanding osmotic dehydration, are explored alongside mathematical models essential for comprehending this process. The effect of osmotic agents and process parameters on efficacy, such as temperature, agitation and osmotic agent concentration, is closely examined. Pre-treatment techniques are emphasized in order to improve process effectiveness and product quality. The increasing demand for sustainability is a critical factor driving research into eco-friendly osmotic agents, waste valorization, and energy-efficient methods. The review also provides practical insights into process optimization and discusses the energy consumption and viability of osmotic dehydration compared to other drying methods. Future applications and improvements are highlighted, making it an invaluable tool for the food industry.

Ex vivo T cell differentiation in adoptive immunotherapy manufacturing: Critical process parameters and analytical technologies.

Adoptive immunotherapy shows great promise as a treatment for cancer and other diseases. Recent evidence suggests that the therapeutic efficacy of these cell-based therapies can be enhanced by the enrichment of less-differentiated T cell subpopulations in the therapeutic product, giving rise to a need for advanced manufacturing technologies capable of enriching these subpopulations through regulation of T cell differentiation. Studies have shown that modifying certain critical process control parameters, such as cytokines, metabolites, amino acids, and culture environment, can effectively manipulate T cell differentiation in ex vivo cultures. Advanced process analytical technologies (PATs) are crucial for monitoring these parameters and the assessment of T cell differentiation during culture. In this review, we examine such critical process parameters and PATs, with an emphasis on their impact on enriching less-differentiated T cell population. We also discuss the limitations of current technologies and advocate for further efforts from the community to establish more stringent critical process parameters (CPPs) and develop more at-line/online PATs that are specific to T cell differentiation. These advancements will be essential to enable the manufacturing of more efficacious adoptive immunotherapy products.

Carbon Fiber-Reinforced PLA Composite for Fused Deposition Modeling 3D Printing.

Polylactic acid (PLA) composite serve as widely used filaments in fused deposition modeling (FDM) 3D printing. This study investigates the enhancement of PLA composite's comprehensive mechanical properties and thermal stability through the incorporation of carbon fiber (CF). The influence of FDM process parameters on the mechanical properties of PLA composite is also analyzed. Results show that adding 5 wt.% CF significantly enhances the stiffness and comprehensive mechanical properties of PLA composite. The order of printing factors affecting the tensile strength of the PLA composite product is as follows: printing layer thickness, bottom plate temperature, printing speed, and nozzle temperature. Finally, optimal tensile strength is achieved under specific conditions: 0.1 mm layer thickness, 60 °C bottom plate temperature, 40 mm/s printing speed, and 215 °C nozzle temperature.

Effect of Process Parameters on the Appearance of Defects of Flake-Pigmented Metallic Polymer.

This study investigates the influence of the main process parameters of injection molding(mold temperature, melt temperature, and injection rate) on the appearance of defects of flake-pigmented metallic polymer parts. To understand the influence of process parameters, an appearance defects index (ADI) is proposed to quantify the appearance defects. In this process, we propose a criterion for judging the appearance of defects based on the results of fiber orientation and tensor distribution analyses of the skin layer, which is then verified analytically by simulating experiments from the literature. Using the Taguchi experimental method, we designed an L25 orthogonal array to systematically evaluate the influence of process parameters. For each experimental condition, the signal-to-noise ratio (S/N ratio) was calculated to determine the optimal level of each factor and its influence on the appearance of defects. According to the results, mold temperature has the greatest influence on the appearance of defects, with an influence of 48.7%, followed by injection rate with an influence of 40.8%, and melt temperature with an influence of 10.5%. The optimal process parameters were found to be a mold temperature of 40 °C, a melt temperature of 250 °C, and an injection rate of 10 cm3/s, which resulted in a 12.6% improvement in the Appearance defects index (ADI) compared to the standard injection molding condition of ABS materials. This study confirmed that it is possible to improve the appearance of defects by adjusting the process parameters of injection molding.

Orthogonal experimental method to investigate the effect of process parameters on the mechanical properties of thin-walled parts by PBF-LB/M.

Lightweight thin-walled parts are widely used in the aviation and aerospace industries, and with the further increase in the complexity of their features, the traditional manufacturing process can no longer fully meet the high requirements of industrial component manufacturing. In this work, thin-walled parts are processed by Laser based powder bed fusion of metals (PBF-LB/M), and the effects of process parameters on residual stress, hardness, mechanical properties and microstructure of thin-walled parts are systematically investigated. The simulation results show that the maximum equivalent residual stresses are distributed in the combination of the solid and the substrate, and the minimum equivalent residual stresses are mainly distributed in the top two ends and the middle part of the solid, and the stress distribution is symmetrical. In addition, the maximum equivalent residual stress increases with the increase of laser power, and decreases with the increase of scanning spacing or scanning speed. The experimental results show that with the increase of laser energy density, the tensile strength and yield strength of thin-walled parts show a tendency of increasing first and then decreasing. Finally, high-quality thin-walled parts were successfully fabricated by the optimized process parameters, and their tensile and yield strengths were increased by 6.1% and 15.9%, respectively.

Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering.

In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances and artificial intelligence evolves, these algorithms are assuming an increasingly vital role in the parameter optimization of machining processes. Currently, the development of the response surface method, genetic algorithm, Taguchi method, and particle swarm optimization algorithm is relatively mature, and their applications in process parameter optimization are quite extensive. They are increasingly used as optimization objectives for surface roughness, subsurface damage, cutting forces, and mechanical properties, both for machining and special machining. This article provides a systematic review of the application and developmental trends of optimization algorithms within the realm of practical engineering production. It delves into the classification, definition, and current state of research concerning process parameter optimization algorithms in engineering manufacturing processes, both domestically and internationally. Furthermore, it offers a detailed exploration of the specific applications of these optimization algorithms in real-world scenarios. The evolution of optimization algorithms is geared towards bolstering the competitiveness of the future manufacturing industry and fostering the advancement of manufacturing technology towards greater efficiency, sustainability, and customization.

Development, modelling and optimization of process parameters on the tensile strength of aluminum, reinforced with pumice and carbonated coal hybrid composites for brake disc application.

This study focuses on optimizing double stir casting process parameters to enhance the tensile strength of hybrid composites comprising aluminum alloy, brown pumice, and coal ash, intended for brake disc applications. Analytical techniques including X-ray fluorescence, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy were employed to characterize the composite constituents. The Taguchi method was utilized for experimental design and optimization to determine the optimal weight compositions of brown pumice and coal ash, as well as stir casting parameters (stirrer speed, pouring temperature, and stirring duration). Regression analysis was employed to develop a predictive mathematical model for the tensile strength of the hybrid composites and to assess the significance of process parameters. The optimized composite achieved a predicted tensile strength of 186.81 MPa and an experimental strength of 190.67 MPa using 7.5 vol% brown pumice, 2.5 vol% coal ash, a pouring temperature of 700 °C, stirrer speed of 500 rpm, and stirring duration of 10 min. This represents a 52.23% improvement over the as-cast aluminum alloy's tensile strength. Characterization results revealed that brown pumice and coal ash contain robust minerals (SiO2, Fe2O3, Al2O3) suitable for reinforcing metal matrices like aluminum, titanium, and magnesium. Thermogravimetric and differential thermal analyses demonstrated thermal stability up to 614.01 °C for the optimized composite, making it suitable for brake disc applications.

The Influence of Laser Remelting Parameters on the Surface Quality of Copper.

In order to improve the surface quality of copper after laser remelting, this article took laser frequency, pulse width, and energy density as the research objects and used scanning electron microscopy (SEM), a laser confocal three-dimensional measurement instrument, hardness tester, and friction and wear measurement instrument to study the surface morphology, surface roughness, microhardness, and wear resistance of copper, respectively. The results indicate that the frequency, pulse width, and energy density of laser remelting could directly affect the surface quality of the sample, but the influence of frequency and pulse width was more significant. When the laser remelting frequency was 10 Hz, the pulse width was 10 ms, and the energy density was 132.69 J/mm2, the sample exhibited good surface morphology, roughness, and wear resistance. The relevant research in this article can provide a good reference for the laser surface treatment of copper-based materials.

Determination of Residual Stresses in 3D-Printed Polymer Parts.

This paper presents the results of an investigation of the possibility of the reliable determination of the residual stress-strain state in polymers and composites using a combination of bridge curvature, optical scanning, and finite element methods. A three-factor experiment was conducted to determine the strength of printed PLA plastic products. The effect of the residual stresses on the strength of the printed products was evaluated. By comparing the values of the same strength stresses, a relationship between the nature of the stresses and the strength of the samples was found. A tendency of the negative influence of tensile stresses and the opposite strengthening effect of compressive stresses was obvious, so at the same values of tensile strength, the value of residual stress of 42.9 MPa is lower than that of the fibre compression at the value of 88.9 MPa. The proposed new methods of the residual stress determination allow obtaining a complete picture of the stressed state of the material in the investigated areas of the products. This may be necessary in confirming the calculated models of the residual stress-strain state, clarifying the strength criteria and assessing the quality of the selected technological modes of manufacturing the products.

An integrated perspective on measuring cytokines to inform CAR-T bioprocessing.

Chimeric antigen receptor (CAR)-T cells are emerging as a generation-defining therapeutic however their manufacture remains a major barrier to meeting increased market demand. Monitoring critical quality attributes (CQAs) and critical process parameters (CPPs) during manufacture would vastly enrich acquired information related to the process and product, providing feedback to enable real-time decision making. Here we identify specific CAR-T cytokines as value-adding analytes and discuss their roles as plausible CPPs and CQAs. High sensitivity sensing technologies which can be easily integrated into manufacture workflows are essential to implement real-time monitoring of these cytokines. We therefore present biosensors as enabling technologies and evaluate recent advancements in cytokine detection in cell cultures, offering promising translatability to CAR-T biomanufacture. Finally, we outline emerging sensing technologies with future promise, and provide an overall outlook on existing gaps to implementation and the optimal sensing platform to enable cytokine monitoring in CAR-T biomanufacture.

[Extraction process optimization and link evaluation of Reyanning Mixture based on quality by design idea].

Reyanning Mixture is one of the superior Chinese patent medicine varieties of "Qin medicine". Based on the idea of quality by design(QbD), the extraction process of the Reyanning Mixture was optimized. The caffeic acid, polydatin, resveratrol, and emodin were used as critical quality attributes(CQAs). The material-liquid ratio, extraction temperature, and extraction time were taken as critical process parameters(CPPs) by the Plackett-Burman test. The mathematical model was established by the star design-effect surface method, and the design space was constructed and verified. The optimal extraction process of the Reyanning Mixture was obtained as follows: material-liquid ratio of 11.84 g·mL~(-1), extraction temperature at 81 ℃, and two extractions. A partial least-square(PLS) quantitative model for CQAs was established by using near-infrared spectroscopy(NIRS) combined with high-performance liquid chromatography(HPLC) under the optimal extraction process. The results showed that the correlation coefficients of the correction set(R_c) and validation set(R_p) of the quantitative models of four CQAs were more than 0.9. The root mean square error of the correction set(RMSEC) were 0.744, 6.71, 3.95, and 1.53 µg·mL~(-1), respectively, and the root mean square error of the validation set(RMSEP) were 0.709, 5.88, 2.92, and 1.59 µg·mL~(-1), respectively. Therefore, the optimized extraction process of the Reyanning Mixture is reasonable, feasible, stable, and reliable. The NIRS quantitative model has a good prediction, which can be used for the rapid content determination of CQAs during extraction. They can provide an experimental basis for the process research and quality control of Reyanning Mixture.

Mechanical Properties of Raw Filaments and Printed Specimens: Effects of Fiber Reinforcements and Process Parameters.

Fused Deposition Modeling (FDM) is a well-established manufacturing method for producing both prototype and functional components. This study investigates the mechanical properties of FDM components by material and process-related influencing variables. Tensile tests were conducted on seven different materials in their raw filament form, two of which were fiber-reinforced, to analyze their material-related influence. To cover a wide range from standard to advanced materials relevant for load-carrying components as well as their respective variations, polylactic acid (PLA), 30% wood-fiber-reinforced PLA, acrylonitrile butadiene styrene (ABS), polycarbonate (PC), a blend of ABS and PC, Nylon, and 30% glass-fiber-reinforced Nylon were selected. The process-related influencing variables were studied using the following process parameters: layer thickness, nozzle diameter, build orientation, nozzle temperature, infill density and pattern, and raster angle. The first test series revealed that the addition of wood fibers significantly worsened the mechanical behavior of PLA due to the lack of fiber bonding to the matrix and significant pore formation. The polymer blend of ABS and PC only showed improvements in stiffness. Significant strength and stiffness improvements were found by embedding glass fibers in Nylon, despite partially poor fiber-matrix bonding. The materials with the best properties were selected for the process parameter analysis. When examining the impact of layer thickness on part strength, a clear correlation was evident. Smaller layer thicknesses resulted in higher strength, while stiffness did not appear to be affected. Conversely, larger nozzle diameters and lower nozzle temperatures only positively impacted stiffness, with little effect on strength. The part orientation did alter the fracture behavior of the test specimens. Although an on-edge orientation resulted in higher stiffness, it failed at lower stresses. Higher infill densities and infill patterns aligned with the load direction led to the best mechanical results. The raster angle had a significant impact on the behavior of the printed bodies. An alternating raster angle resulted in lower strengths and stiffness compared to a unidirectional raster angle. However, it also caused significant stretching due to the rotation of the beads.

Ultra-early prediction of the process parameters of coal chemical production.

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

Optimization of Membrane Condenser Process with PTFE Hollow Fiber Membrane.

A membrane condenser (MC) is a novel membrane separation technology that utilizes the hydrophobic nature of porous membranes to capture water vapor from humid gas. Factors such as temperature, pressure, flow rate, and gas composition entering the membrane condenser play a crucial role in water recovery efficiency. This study utilized hydrophobic polytetrafluoroethylene (PTFE) hollow fiber membranes to create multiple identical membrane modules. This research investigated the impact of temperature, flow rate, pressure on the intake side, gas flow on the cooling side, membrane area, and other variables on the performance of the membrane condenser process. This study compared water extraction efficiency under different conditions, focusing on feed flow temperature and sweeping flow. Results showed that at a temperature of 60 °C, the water recovery rate was 24.7%, while a sweep gas flow rate of 4 L/min resulted in a recovery rate of 22.7%. The efficiency of the membrane condenser decreased with higher feed flow rates but increased with larger membrane areas. A proportional relationship between inlet flow and membrane area was observed, suggesting an optimal range of 0.51-0.67 cm/s for both parameters. These findings offer valuable insights for the practical implementation of hydrophobic membrane-based membrane condenser technology.