Process Parameters Research Articles

Optimizing Process Parameters for Controlled Drug Delivery: A Quality by Design (QbD) Approach in Naltrexone Microspheres

Optimizing Process Parameters for Controlled Drug Delivery: A Quality by Design (QbD) Approach in Naltrexone Microspheres

Rapid accomplishment of cost-effective and macro-defect-free LPBF-processed Ti parts based on deep data augmentation

Machine learning methods can accurately predict the density of as-built parts by laser power bed fusion (LPBF), providing a reference for optimizing process parameters. However, obtaining massive training data via experiments is time-consuming and high-cost. Herein, a novel data augmentation method based on the Wasserstein generative adversarial network and regularization strategy (LC-WGAN) was developed to generate new training data for density prediction modelling. Four machine learning (ML) algorithms, support vector regression (SVR), multilayer perceptron (MLP), random forest (RF) and gradient boosting decision tree (GBDT), were adopted to construct the density prediction models. Results show that the proposed LC-WGAN effectively enhanced the prediction performance of all four models. R2 of MLP dramatically increased by 73.06 %, while that of RF and GBDT was marginally improved by 10.07 % and 7.48 %, respectively. RF trained by augmented dataset has the highest prediction performance on test set with MAE, RMSE and R2 of 0.7589, 0.9584 and 0.9822, respectively, which is suitable for density prediction. Additionally, SHAP analysis reveals that energy density is the most important parameter with an overall positive impact on density. The proposed method can provide valuable insights for the limited sample modelling in other fields.

Influence of acid hydrolysis on the degree of polymerization of cellulose from colored cotton substrates

This study investigated the influence of sulfuric acid hydrolysis on the cellulosic fibers and films’ degree of polymerization (DP). Cotton textile residues (dyed and white) and hydrophilic cotton were used as the raw material. The films were formed by the dissolution of fibers in ionic liquid and regeneration in water. Results showed that dyed cotton residues required hydrolysis at 0.5 M sulfuric acid for 30 min to achieve complete dissolution during viscosimetry assay. Due to different dyeing processes and chemical compositions, the color of the fibers influences the DP. Undyed samples submitted to hydrolysis had DP around 60% lower than samples without pretreatment. This effect was not observed when comparing films with and without hydrolysis, because the depolymerization occurs in the dissolution and regeneration step. This study expands the applicability of viscometry to colored substrates, serving as a starting point for future research on the impact of process parameters and dyes on cellulose polymerization.

Design space identification of a coupled two-stage batch reactor system

The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. It was originally developed for a single unit system. Many industrial processes frequently involve multiple unit operations. Assessing the interaction of Critical Process Parameters (CPPs) with Critical Quality Attributes (CQAs) across stages enables informed decision-making and the capacity to balance different requirements. Analysis and visualization of the complex, multi-dimensional DS is a challenging task. This paper presents a framework for identification such DSs, considering both joint and decoupled strategies using a detailed analysis of a two-stage batch reactor case study. We assess and discuss the practicality and relevance of these methods.

Optimization of femtosecond laser processing parameters of SiC using ANN-NSGA-II

Abstract In the field of femtosecond laser machining, it is essential to select the appropriate process parameters to obtain near thermal damage-free and high efficient machining of SiC wafer. In this work, a method of process parameter optimization for femtosecond laser machining of 4H-SiC was proposed by using the predictive ability of the Artificial Neural Network (ANN) and the optimization algorithm of the non-dominated sorting genetic algorithm (NSGA-II). Firstly, the femtosecond laser was used to fabricate microgrooves on SiC wafers, and the effects of process parameters (laser average power, scanning speed and repetition rate) on groove depth, width, heat affected zone (HAZ) and material removal rate (MRR) were investigated. Secondly, The ANN model is established based on experimental data. Other experiments verify the accuracy of the model, and the average error in the model's predictions is around 5%. Thirdly, Pareto optimal solutions are obtained by global optimization of the ANN model using the NSGA-II. The experimental results show that the Pareto optimal solutions are effective and reliable. This proposed method offers dependable guidance for the selecting and optimizing process parameters of high hardness and brittle materials in the field of femtosecond laser processing, and reduces the cost of selecting the appropriate processing parameters in the production process. The method can also be extended to other machining means, such as turning and milling.

Preparation and characterization of composite-modified PA6 fiber for spectral heating and heat storage applications

Abstract Composite coating technology was used to prepare a modified polyamide 6 (PA6) polymer material masterbatch with low mutual interference and good spinnability using molybdenum oxide, tungsten trioxide, graphene oxide, etc., as modifiers. Experiment shows that using nanoscale composite-coated modified masterbatches capable of spectral heating and heat storage in the preparation of nylon 6 fiber, adding modified masterbatches with a mass ratio of 6% online, and controlling various process parameters can result in good functionality and usability as well as enable consistent production. Optimal production conditions are achieved by utilizing pre-dried PA6 chips with a moisture content of 420 ppm. The master batch is subjected to drying at a temperature of 95°C for 12 h. During this process, the master batch is introduced at an addition ratio of 6%. Subsequent spinning is conducted at a speed of 4,300 m·min−1 and a temperature of 255°C. Cooling is facilitated by air set at a temperature of 17°C, flowing at a speed of 0.45 m·min−1. In the production line, one roller operates without heating, while the second roller is maintained at a temperature range of 160°C. The stretching process is performed at a ratio of 1.3. To ensure proper lubrication, an oil rate of 1.3% is applied. These meticulously controlled process parameters collectively contribute to the most favorable production status.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

In-situ particle analysis with heterogeneous background: a machine learning approach

We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle–substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle–substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.

3D printed stents using Fused deposition modelling

3D printing has emerged as an appealing technology for designing and manufacturing bioresorbable stents. This innovative technology enables the creation of a diverse range of stent structures with specific features and intricate geometries, significantly impacting their mechanical properties and potentially affect clinical performance. In this study, we investigated the influence of various processing parameters during the 3D printing process and assess their effects on stent quality. Fused Deposition Modelling (FDM) was chosen as the 3D printing technology, with a focus on optimizing the printing of a novel chevron-strut stent design. Printing parameters were systematically investigated to understand their individual contributions to overall stent quality. The findings reveal the significance of print speed (6.5 mm/s) as a pivotal factor, influencing strut width and the difference between the Computer Aided Design (CAD) volume to calculated volume of stent. Furthermore, the optimization of printing parameters such as travel speed (200 mm/s), and line width (0.2 mm) led to the fabrication of stent with strut widths of 400 μm. However, it is important to also consider other parameters for comprehensive print optimization. In conclusion, by exercising control over the print process parameters, it becomes feasible to produce stents that closely align with the original CAD designs.

Destruction of n-hexane from the air stream by pulsed discharge plasma: Modelling and key process parameters optimization by CCD-RSM

In this study, a Dielectric-Barrier Discharge (DBD) plasma reactor was used for the degradation of n-hexane from an air stream. a Central Composite Design (CCD) based Response Surface Methodology (RSM), (CCD-RSM) was utilized to explore the effects of experimental parameters including discharge voltage, initial concentration, gas flow rate, relative humidity and their interactions on the reactor performance. The proposed optimization model showed a satisfactory correlation between the predicted and actual results. According to the results, discharge voltage was the most significant parameter affecting the removal efficiency, CO2 selectivity and the level of O3 and NOx formation, whereas energy yield was mainly influenced by gas flow rate and initial concentration. At optimized conditions (discharge voltage of 8.5 kV, inlet concentration of 94.3 ppm, relative humidity of 55 % and gas flow rate of 733.16 ml/min with combined desirability of 0.921), removal efficiency of 92.57 %, CO2 selectivity of 58.87 %, energy efficiency of 0.25 g/kWh, O3 formation of 140.88 mg/m3 and NOx formation of 122.46 ppm were obtained. Water vapour significantly increased the removal efficiency and CO2 selectivity as well as reduced O3 and NOx. The results revealed that the CCD-RSM provided the optimum process parameters for the Non-Thermal Plasma (NTP) system.

Effect of alloy 625 buffer layer on corrosion resistance of nickel base hardfacing

Nickel base hardfacing alloys serve to mitigate corrosion losses at elevated temperatures. Efficacy of Nickel base hardfacing alloys can be compromised when applied onto Fe base substrates due to iron dilution in hardfacing. The degree of Fe dilution can be reduced by using advanced deposition methods and optimized deposition parameters. Gas Tungsten Arc Welding (GTAW) method is commonly employed for its cost-effectiveness and versatility, but it tends to induce higher Fe dilution due to increased heat input. This study aims to reduce the degree of Fe dilution and enhance corrosion resistance by introducing a nickel base buffer layer (alloy 625) between the nickel base hardfacing alloy and AISI 316L stainless steel substrate. For comparative purposes, Ni base hardfacing alloy was deposited on the substrate without any buffer layer. Hardfacing depositions were prepared using manual GTAW process. Process parameters namely, deposition current, voltage, travelling speed, pre-heating temperature, and cooling method were kept constant. Samples underwent aging at 1123 K for 4 h, followed by microstructural examination via optical and scanning electron microscopes. Iron (Fe) dilution quantification was performed using energy dispersive spectroscopy (EDS). The EDS analysis showed that the use of buffer layer between the hardfaced deposit and the substrate decreased the degree of Fe dilution in the hardfaced deposit. X-Ray diffraction (XRD) analysis was performed to identify various phases formed in the hardfaced deposit. Potentiodynamic polarization test was performed to assess the corrosion resistance of hardfaced deposit in 3.5 wt% NaCl solution. Results show significant improvement in corrosion resistance of hardfacings deposited sample with buffer layer as compared to those without buffer layer. Aging treatment further enhanced corrosion resistance. The corrosion resistance data is correlated with the microstructure and phases formed in various samples.

Optimization of parameters and formulation of numerical model employing GRA–PCA and RSM approach for friction stir welded Ti–6Al–4V alloy joints

Abstract In this work, an endeavor was made to determine the impact of the tool’s shoulder diameter, pin profile, rotational and traverse speeds on the mechanical properties of the friction stir welded namely Ti–6Al–4V alloy joints. A total of 21 experiments were carried out based on the central composite design (CCD) concept of response surface methodology (RSM). A quadratic regression based numerical model was formulated to ascertain the relationship amidst the parameters of FSW process and the mechanical properties of the fabricated Ti alloy joints. Analysis of variance (ANOVA) was employed to confirm the importance of parameters and their interactive impacts. Optimized process parameter combinations were ascertained by grey relation based analysis (GRA) was coupled together with principal component analysis (PCA), a hybrid based approach. Single score of GRG based response was determined and GRG scores were ranked for all experiments. 1st rank was attained by the experiment no. 20 possessing a GRG score of 2.9989. Optimized values of 25mm shoulder diameter having a taper cylindrical pin geometry employed at a tool traverse speed of 40 mm/min, rotational speed of 1400 rpm resulted in the generation of flaw free Ti alloy joint possessing a largest tensile strength of 809.8 MPa, yield strength of 778.7 MPa and percentage of elongation of 6.9%. SEM based fractography ofTi alloy joint specimens announced that taper cylindrical pin geometry along with 25m shoulder diameter of employed tool have an inevitable part in generating frictional heat in ideal volumes and a perfect stirring action during FSW process.

Effect of Process Parameters on Welding Residual Stress of 316L Stainless Steel Pipe

316L stainless steel pipes are widely used in the storage and transportation of low-temperature media due to their excellent low-temperature mechanical properties and corrosion resistance. However, due to their low thermal conductivity and large coefficient of linear expansion, they often lead to significant welding residual tensile stress and thermal cracks in the weld seam. This also poses many challenges for their secure and reliable applications. In order to effectively control the crack defects caused by stress concentration near the heat-affected zone of the weld, this paper establishes a thermal elastoplastic three-dimensional finite element (FE) model, constructs a welding heat source, and simulates and studies the influence of process parameters on the residual stress around the pipeline circumference and axial direction in the heat-affected zone. Comparison and verification were conducted using simulation and experimental methods, respectively, proving the rationality of the finite element model establishment. The axial and circumferential residual stress distribution obtained by the simulation method did not have an average deviation of more than 30 MPa from the numerical values obtained by the experimental method. This study also considers the effects of welding energy, welding speed, and welding start position on the pipe’s circumferential and axial residual stress laws. The results indicate that changes in welding energy and welding speed have almost no effect on the longitudinal residual stress but have a more significant effect on the transverse residual stress. The maximum transverse residual stress is reached at a welding energy of 1007.4~859.3 J/mm and a welding speed of 6.6 mm/s. Various interlayer arc-striking deflection angles can impact the cyclic phase angle of the transverse residual stress distribution in the seam center, but they do not alter its cyclic pattern. They do influence the amplitude and distribution of the longitudinal residual stress along the circumference. The residual stress distribution on the surface of the pipe fitting is homogenized and improved at 120°.

THE EFFECT OF LASER OSCILLATING WELDING ON FORMATION AND PROPERTIES OF TITANIUM ALLOY

Titanium alloys are widely used in aerospace and other fields due to their advantages of low density, high specific strength and good corrosion resistance. Laser oscillating welding is an innovative process in the realm of welding technology. In this study, two laser oscillating welding methods (circular and linear) were employed to analyze the influence of different oscillation parameters on the formation of titanium alloy. The results indicate that the oscillation frequency has a significant impact on weld formation. The laser scanning trajectory also affects weld formation, with circular and linear scanning trajectories resulting in better weld quality. Finally, the effects of oscillation modes on the microstructure and properties of laser oscillating welding titanium alloy were investigated based on optimized process parameters. Among them, circular laser oscillating welding of titanium alloy exhibits higher tensile strength, with a maximum tensile strength of up to 1188.3[Formula: see text]MPa.

Effect of in-situ activated core-shell particles on fatigue behavior of carbon fiber reinforced thermoplastic composites

In this unique study, the effect of adding core-shell particles (CSPs) on fatigue performance of carbon-fiber reinforced PA6 (CF-PA6) laminates is investigated. The thermoplastic laminates were prepared using compression molding and were reinforced at ply interfaces with 2 wt% and 4 wt% CSPs of the polymer mass. A manual method was used to disperse CSPs using a sieve and carefully selected process parameters. The cyclic tests were conducted and assessed, considering S–N curve, stiffness degradation, and energy dissipation. Consequently, the fatigue life of modified composites improved respectively by eight and four times when 2 wt% and 4 wt% CSPs were used. The results showed that an optimal improvement was achieved with a 2 wt% CSPs. The fatigue strength coefficient and fatigue strength exponent of CF-PA6 composites improved by 22.13 % and 9.85 %, respectively. The findings have the potential to establish a new frontier in thermoplastic research and would help designers to enhance the fatigue properties of thermoplastic laminates in specific elastic tailoring structures.

Optimization of Process Parameters for Microwave Drying of Yellow- and Purple-Fleshed Potatoes

AbstractThe main objective of the present work was to study the optimization of microwave drying of potatoes that have different flesh colors. The effects of independent variables of microwave power (300, 450, 600 W), slice thickness (2–4, 6 mm), and steam blanching time (2, 5, 8 min) on the color, total phenolic content (TPC), antioxidant activity, starch ratio, and total monomeric anthocyanin content (TMA) were investigated by using the Response Surface Methodology (RSM). Before drying, potato slices that had different thicknesses were blanched in steam at 90 °C for indicated times. Optimization was applied to improve bioactive compounds, starch ratio, and color. The optimum drying parameters were determined as 300 W, 6 mm, and 8 min for purple-fleshed potatoes, and 450 W, 6 mm, and 2 min for yellow-fleshed potatoes. This study is beneficial to the development of the processing of potatoes in the food industry and provides more insights into the application of microwave drying technology.

Modelling the Melting Kinetics of Polyetheretherketone Depending on Thermal History: Application to Additive Manufacturing

Recent techniques for forming thermoplastics, such as welding, automated fibre placement or additive manufacturing, generate successive rapid heating and cooling cycles that cause the partial melting of crystals during the process. The melting of an interface is essential to guarantee a good molecular diffusion across the welded parts. Nevertheless, no model can correctly predict the melting kinetics and consequently the evolution of the crystalline degree during the layers’ deposition process. The purpose of this paper was to define the melting kinetics depending on the crystallization conditions for polyetheretherketone (PEEK). Firstly, a non-isothermal crystallization model was proposed over a wide range of cooling rates from 0.1 K.s−1 to 150 K.s−1. Experimental results have highlighted a dual-mode behaviour of melting and demonstrated the dependence of melting temperatures on crystallization conditions. Based on these observations, a model was developed to predict the melting behaviour after non-isothermal crystallization. The melting model revealed that after high cooling rates, primary and secondary crystals melt separately at low temperatures, while after slow cooling rates, both structures melt simultaneously at higher temperatures. Finally, the melting model was applied to the FFF thermal cycle to illustrate the influence of process parameters on the melting kinetics during deposition.

Model for designing process strategies in ultrafast laser micromachining at high average powers

The great potential to scale the productivity of laser micromachining processes offered by the development of ultrafast lasers with average powers exceeding 1 kW comes at the cost of new physical and technical challenges. The large number of adjustable parameters, the different physical process limits, and the various possible optimization strategies complicate the design of suitable laser micromachining processes that enable high quality and high throughput.In this contribution, a comprehensive model is proposed that helps to identify the optimization strategies and process parameters required for the scaling of laser micromachining to high throughput at high average laser power without exceeding the process limits to ensure high surface quality.The model was experimentally verified on the example of laser micromachining of pockets in metals and silicon using an ultrafast laser with an average power of 1.01 kW, a pulse duration of 600 fs, different pulse energies, pulse bursts, and beam diameters. As a result, high material removal rates exceeding 120 mm3/min were achieved for silicon, stainless steel, copper, and aluminum, which exceed previously achieved removal rates by one to two orders of magnitude. The machined surfaces exhibit high quality as confirmed by the low measured roughness of about 1 µm.

An ANN Hardness Prediction Tool Based on a Finite Element Implementation of a Thermal–Metallurgical Model for Mild Steel Produced by WAAM

WAAM has emerged as a promising technique for manufacturing medium- and large-scale metal parts due to its high material deposition efficiency and automation level. However, its high heat accumulation and complex thermal evolution strongly affect the resulting microstructures and mechanical properties. The heterogeneous and unpredictable nature of these properties hinder the widespread application of WAAM in the steel construction industry. In this study, an artificial neural network (ANN) hardness model is developed, based on a thermal–metallurgical model for mild steel. The objective is to establish non-linear relationships between the input process parameters and the desired output, i.e., hardness. The thermal–metallurgical model utilizes a well-distributed heat source model, a death-and-birth algorithm, and a metallurgical model to simulate the temperature field and to calculate the microstructure phase fraction. The temperature prediction errors at four thermocouple positions are mostly below 20%. Because of the limited experimental data, twenty-five simulation experiments are performed using the L25 orthogonal array based on the Taguchi method. The analysis of variance (ANOVA) reveals that the travel speed has the greatest impact on hardness. With the dataset from the thermal–metallurgical model, an ANN model to predict hardness is developed. A comparison to experimental data shows excellent performance and accuracy, with the Mean Absolute Percentage Error (MAPE) of ANN predictions within 10% of the targeted hardness.

Sensory and Physical Properties of Fibrous Meat Analogs Made from Faba Bean, Pea, and Oat Using High-Moisture Extrusion

Faba bean is a promising source of ingredients for the production of meat analogs. However, sensory properties of faba bean, especially the bitter taste of the protein concentrate, restrict its use. Our aim was to assess the feasibility of two types of faba bean ingredients—flour (from germinated, gently heat-treated beans) and groat (from non-germinated, roasted beans)—in combination with pea protein isolate and oat fiber concentrate for producing meat analogs using high-moisture extrusion. We produced six samples using varying recipes, while maintaining constant process parameters. An untrained panel (55 participants) evaluated the samples for key sensory attributes (check-all-that-apply) and rated their pleasantness. The water absorption capacity and mechanical properties of the samples were assessed using instrumental measurements. The samples were frequently described as ‘beany’ and ‘tasteless’, but very rarely as ‘bitter’. The most frequently cited attributes for mouthfeel varied between the samples containing 30% (‘tough’, ‘gummy’) and 50% (‘crumbly’, ‘floury’) of faba bean flour/groat and were associated with corresponding mechanical properties. On average, the sample containing a blend of faba bean groat and pea protein isolate (50% each) appeared to be the most pleasant. Our results suggest that faba bean groat with pea protein isolate enables the production of fibrous meat analogs with acceptable taste and texture, without the bitter off-taste.