Process Parameters Research Articles

Melt spinning of sustainable high-quality polypropylene from post-consumer waste for monofilaments

The growing emphasis on sustainability in the textile industry demands innovative solutions for material sourcing and production processes. This study investigates the melt spinning of high-quality polypropylene monofilaments derived from post-consumer waste, focusing on the recycling of food packaging from the plastic waste collection system in Germany. The research explores potential pathways to produce monofilaments that maintain the mechanical properties required for textile applications. Five commercially available polypropylene materials were examined, including a homopolymer polypropylene (hPP) and four recycled polypropylene variants obtained from post-consumer waste. Prior to processing, the materials underwent comprehensive thermoanalytical, rheological, and visual assessments to characterize their thermal stability, flow behavior, and appearance, respectively. The melt-spinning process was carried out by producing monofilaments and evaluating the basic mechanical properties and processability of each material under various process parameters. The mechanical and physical properties of the monofilaments—including tensile strength and elongation—were thoroughly analyzed. Key findings demonstrate that recycled polypropylene can yield monofilaments with mechanical properties comparable to those of virgin materials, highlighting its potential for advanced applications in the textile industry. The study provides valuable insights into the performance and limitations of post-consumer polypropylene in modern melt-spinning processes, contributing to efforts toward more sustainable material use in textile manufacturing.

Experimental investigation into nano-finishing of pure copper built using atomic diffusion additive manufacturing

Purpose Atomic diffusion additive manufacturing (ADAM) is an indirect way of building a metallic part. Like other additive manufacturing processes, the part built by this process also suffers from high surface roughness. Hence, the parts made by the ADAM process must be post-processed before using them in the intended functional application. Therefore, this study aim to investigate the finishing aspects of ADAM built pure copper using an advanced finishing technique. Design/methodology/approach In this study, abrasive flow finishing (AFF) was used to achieve nano-finish on the pure copper part built using the ADAM process. Because of the higher surface roughness, the as-built part was finished in two stages using the AFF process. In the first stage (primary finishing), the part was finished using an indigenously developed polysaccharide-based abrasive medium with coarser abrasive particles. The influence of process parameters such as extrusion pressure, number of cycles and abrasive particle concentration on material removed (MR) and percentage change in surface roughness (%ΔRa) was studied and then optimized to obtain minimum MR and maximum %ΔRa. The pre-finished part at optimized parameters was further finished (secondary finishing) using an abrasive medium with smaller abrasive particles. Findings The primary finishing resulted in the minimum material removal of 40.2 g and %ΔRa more than 84% in both the longitudinal and lateral direction of the as-built part at 7.8 MPa, 183 finishing cycles and 62% concentration of abrasive particles in the medium. During the secondary finishing, the best surface roughness of 0.27 µm and 0.46 µm was obtained from the initial surface roughness of 7.62 µm and 5.03 µm, respectively, in the longitudinal and lateral directions. Originality/value A novel abrasive medium that comprised natural polysaccharides as a base polymer was developed and used to nano-finish ADAM-built pure copper using the AFF process. Sequential finishing steps were performed to achieve submicron surface roughness on the as-built part.

Process optimization mitigated the retention loss of an Fc-fusion protein during ultrafiltration/diafiltration.

In the downstream processing of antibody-based therapeutics, ultrafiltration/diafiltration (UF/DF) is commonly applied for concentration and buffer exchange in the final formulation. For a given molecule, various factors such as membrane type, feed flux, and transmembrane pressure (TMP) can significantly influence the performance of UF/DF, impacting yield, buffer exchange efficiency, and product quality. Conventional membrane pore size selection is based on product molecular weight to ensure high retention. While working on an Fc-fusion protein, we found that the pH of load material had a critical effect on the retention of the molecule due to conformational changes at different pH values, as evidenced by the size-exclusion chromatography (SEC). Meanwhile, optimization of the UF/DF process underscored the importance of concentration polarization to protein retention. Approaches to reduce concentration polarization, such as increasing feed flux and lowering TMP, resulted in less protein loss in the permeate stream. High retention of this Fc-fusion protein during the UF/DF step can be achieved not only by utilizing a 5 kDa membrane but also by employing a 10 kDa membrane with optimized process parameters such as load conditions, feed flux, and TMP. These observations provide important insights on the factors impacting protein retention beyond the molecular weight cutoff (MWCO) of UF/DF membrane.

Constraint Active Search in Process Window Optimization for Powder Feed Directed Energy Deposition

Abstract Optimizing process parameters for directed energy deposition is crucial to achieve high-quality printed parts. However, this optimization process often entails significant time and cost investments. An initial investigation into the process window can be conducted through the examination of single tracks. In this work, we investigate the utility of constraint active search (CAS) to efficiently identify process window that yield 4340 low-alloy steel single tracks with desired geometrical features. The effectiveness of the CAS method was assessed through experiments with physical and interpolated measurement. Fifty single tracks from randomly sampled process parameter combinations with different power, scan velocity, and laser spot size and ten single tracks from CAS-generated parameters were produced and analyzed. The results demonstrate that our search method outperforms random search, with 80% of parameter sets identified as desirable compared to only 4% in the case of random search. Moreover, an interpolated ground truth in input spaces of various dimensionalities was built in order to assess repeatability without excessive experimental cost. The results indicate that the CAS achieves higher precision compared to grid search and random search, especially in higher-dimensional process parameter spaces.

The impact of cellulosic pulps on thermoforming process: effects on formation time and drainage efficiency

Abstract The growing environmental and health concerns associated with plastic pollution have driven the search for sustainable alternatives. This study investigated the impact of various types of cellulosic pulps and degree of refining on the production of thermoforming eco-friendly fiber-based materials as alternatives to plastics. By examining the influence pulp and fibers characteristics, the study aimed to correlate these factors with the two process parameters, formation time and drainage efficiency. Trays with a target dry weight of 31 g were produced using slurry consistency of 0.2 % and 0.8 % on an industrial molding machine. In this study, formation times required to achieve the target weight are varied from 0 to 42 s, influenced by pulp type, refining level, and slurry consistency showing that the longest time can affect the quality. Higher refining levels extended formation time, making it crucial to adjust slurry consistency to optimize production efficiency. Formation trials revealed that most pulps followed a logarithmic formation pattern at both consistencies. Dryness and drainage gain varied significantly across pulp types. Hardwood pulps exhibited the highest initial dryness, while alternative fibers like canola had the lowest, making them longer to dry. Recycled and mechanically pulped fibers retained more water due to fines content, further decreasing dewatering. Additionally, increased refining levels decreased both the initial dryness and the gain in dryness over equal drainage times. Since dryness directly influences drying time and energy consumption, optimizing pulp selection and refining strategies are essential for enhancing cost efficiency in thermoformed fiber production.

Influence of Hull and Impurity Content in High-Oleic Sunflower Seeds on Pressing Efficiency and Cold-Pressed Oil Yield

This study investigates the effects of hull and impurity content on the efficiency of cold-pressing high-oleic sunflower seeds using a screw press. High-oleic sunflower oil is valued for its oxidative stability and health benefits, and optimizing pressing conditions is crucial for maximizing yield and maintaining oil quality. The identification of high-oleic sunflower oil was performed by analyzing its fatty acid composition, iodine value, and refractive index. Eleven seed samples with varying hull and impurity contents were processed to assess their impact on cake composition, pressing efficiency, and pressing oil yield. Oil yield ranged from 39.24% to 76.52%, with higher hull content contributing to increased yield due to its role in facilitating oil drainage. Multiple linear regression models were developed to predict moisture and oil content in the cake, as well as pressing efficiency, based on hull and impurity content, demonstrating strong predictive accuracy. These parameters were selected as they represent economically significant indicators, given that moisture and oil content indirectly reflect the protein content in the cake, while sunflower cake is primarily used as animal feed. Additionally, pressing efficiency indicates oil yield during pressing, which is the most critical economic parameter of the cold-pressing process. Cluster analysis identified three sample groups with distinct characteristics, revealing interactions between seed composition and pressing performance. The results highlight the significance of seed preparation in optimizing cold-pressing efficiency and provide insights for improving oil extraction processes. These findings support the industrial application of high-oleic sunflower seed pressing and contribute to the development of sustainable, high-quality oil production methods.

Low-Heating-Rate Thermal Degradation of Date Seed Powder and HDPE Plastic: Machine Learning CDNN, MLRM, and Thermokinetic Analysis

Finding reliable, sustainable, and economical methods for addressing the relentless increase in plastic production and the corresponding rise in plastic waste within terrestrial and marine environments has garnered significant attention from environmental organizations and policymakers worldwide. This study presents a comprehensive analysis of the low-heating-rate thermal degradation of high-density polyethylene (HDPE) plastic in conjunction with date seed powder (DSP), utilizing thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TGA/FTIR), machine learning convolutional deep neural networks (CDNNs), multiple linear regression model (MLRM) and thermokinetics. The TGA/FTIR experimental measurements indicated a synergistic interaction between the selected materials, facilitated by the presence of hemicellulose and cellulose in the DSP biomass. In contrast, the presence of lignin was found to hinder degradation at elevated temperatures. The application of machine learning CDNNs facilitated the formulation and training of learning algorithms, resulting in an optimized architectural composition comprising three hidden neurons and employing 27,456 epochs. This modeling approach generated predicted responses that are closely aligned with experimental results (R2~0.939) when comparing the responses from a formulated MLRM model (R2~0.818). The CDNN models were utilized to estimate interpolated thermograms, representing the limits of experimental variability and conditions, thereby highlighting temperature as the most sensitive parameter governing the degradation process. The Borchardt and Daniels (BD) model-fitting and Kissinger–Akahira–Sunose (KAS) model-free kinetic methods were employed to estimate the kinetic and thermodynamic parameters of the degradation process. This yielded activation energy estimates ranging from 40.419 to 91.010 kJ·mol⁻1 and from 96.316 to 226.286 kJ·mol⁻1 for the selected kinetic models, respectively, while the D2 and D3 diffusion models were identified as the preferred solid-state reaction models for the process. It is anticipated that this study will aid plastic manufacturers, environmental organizations, and policymakers in identifying energy-reducing pathways for the end-of-life thermal degradation of plastics.

Comprehensive Review on the Novel Formulations and Tribological Performance of Copper and Iron based Composite Sintered Friction Materials for Heavy-Duty Applications

Abstract Sintered friction materials contribute to efficiency and effectiveness in various domains, encompassing high-performance racing cars, motorcycle clutches, industrial brakes designed for machinery and equipment, railroad brakes, aerospace components, and wind turbines. Heavy-duty applications include wind turbines that require friction material to slow down the speed of the high-speed shaft to zero rotation conditions. Currently, wind turbines employ Cu and Fe-based sintered composite materials for effective braking. It is possible to customize the performance of sintered friction materials to fulfil particular needs by varying the material's composition, the sintering process parameters, and the surface treatments applied to the finished product. Engineers can enhance these attributes to attain certain objectives, such as a high coefficient of friction, low rates of wear, consistent performance across various operational circumstances, and resistance against thermal deterioration. Many significant advancements made today to improve the frictional performance of friction materials in wind turbines. Because failure of such a braking system results in robust failure of the wind turbine under harsh environmental conditions, consolidation of the novel formulations and their frictional performance of such developed friction materials is the need of the hour. In light of the view, we attempted to consolidate the same. A comprehensive overview of formulation, microhardness, wear rate, and friction coefficient are presented in this review article.

Understanding a frequency shifting phenomenon interfering with in-line acoustic monitoring of an extrusion compounding process for polymer composites

This study investigated an anomalous frequency shift observed in collected spectra from an inline monitoring system based on guided ultrasonic waves, with the changing flow rate of an extrusion compounding process for fiber-reinforced thermoplastics. Three possible process parameters to explain the ultrasonic peak shifting, namely melt temperature, velocity, and fiber length were evaluated. The unlikely potential of a doppler moment due to melt velocity, was readily dismissed in the analysis since fluid flow through the die was too slow and while resonance frequency variation may be possible from the related fiber damage associated with increasing flow rates, there was insufficient physical evidence of this anticipated effect in this study. Melt temperature variation associated with viscous dissipation was concluded to be the dominant cause for the frequency shifting noted in the acoustic spectra. The changes in material temperature through which the sound travelled were varying the extent and frequency of the dispersion modes in the polymer melt. These findings are new guidance to processors on setting up a system using active ultrasonics for in-line monitoring in the polymer composites industry.

Lyophilized monkeypox mRNA lipid nanoparticle vaccines with long-term stability and robust immune responses in mice

ABSTRACT The World Health Organization (WHO) has recently declared another global health emergency due to the rapidly spreading monkeypox (Mpox) outbreak in numerous African countries. To address the unmet need to contain the outbreak using the existing vaccines, this study developed a lyophilization process for an effective, scalable and affordable Mpox mRNA-LNP vaccine candidate to address the global health crisis. A comprehensive evaluation and optimization of the vaccine formulation (the type/concentration of cryoprotectants, the type/concentration of buffer system, as well as the mRNA concentration and reconstitution solvent) and the freeze-drying process parameters (freezing method, temperature, cooling rate and primary/secondary drying conditions) were conducted. The freeze-dried product exhibits a uniform appearance and a moisture content of less than 1%. Reconstitution of the lyophilized mRNA-LNP resulted in equivalent particle size/polydispersity index, encapsulation efficiency and mRNA integrity compared to that of freshly prepared mRNA-LNP. Furthermore, the lyophilization process can be scaled up 100-fold to 2000 vials/batch. Notably, the lyophilized mRNA-LNP demonstrated a storage stability of at least 12 months at 4°C, and at ambient temperature for a minimum of 8 h post-reconstitution, exhibiting minimal deterioration in product quality. The in vitro biological activity and in vivo immunogenicity of the lyophilized mRNA-LNP was comparable to that of the freshly prepared mRNA-LNP. These results provide a compelling rationale for the utilization of lyophilization technology in enhancing the accessibility of the Mpox mRNA vaccine in developing countries, a strategy that is crucial for containing the global epidemic of Mpox infection.

Machine learning approaches for real-time process anomaly detection in wire arc additive manufacturing

Abstract In gas metal arc welding (GMAW) processes, including wire arc additive manufacturing (WAAM), machine learning (ML) is emerging as a powerful tool for monitoring both process and product anomalies. However, a significant challenge in real industrial environments is the reliance on large, balanced datasets for training supervised learning models. To address this issue, a shift toward unsupervised learning is gaining attention in this research field, offering the potential to work effectively with small and unbalanced datasets. However, different materials, sensors, and welding technologies have been used in the literature, making complex the comparison of the results. This work fills that gap by presenting a comprehensive comparison of both supervised and unsupervised learning methods. An experimental campaign was conducted on Invar 36 alloy—a material with limited WAAM research—where 15 wall structures were deposited with varying process parameters using the natural dip transfer process, aiming to identify the optimal parameters for this alloy. Data on welding current and voltage were captured, and during the qualification procedure, anomalies were detected, some of which led to product defects. Supervised, unsupervised, and semi-supervised ML approaches, along with a detailed frequency domain analysis of the collected signals, were applied to process the obtained unbalanced dataset. The results provide key insights: while supervised learning models can be applied to anomaly detection in small and unbalanced datasets, they are prone to overfitting, which limits their practical use due to the prevalence of normal cases over anomalies in the dataset, resulting in higher number of missed anomalies. In contrast, unsupervised models, with their lower generalization capability, tend to exhibit higher false alarm rates but better performance to identify anomalous data. This work not only compares in depth these data analytics methodologies but also offers guidance on selecting the appropriate ML algorithm based on specific industrial objectives and provides insights into the printability of Invar 36 for WAAM applications under natural dip transfer process.

Advances in Hydrothermal Carbonization for Biomass Wastewater Valorization: Optimizing Nitrogen and Phosphorus Nutrient Management to Enhance Agricultural and Ecological Outcomes

This study presents a novel approach that integrates hydrothermal carbonization (HTC) technology with circular economy principles to optimize the management of nitrogen and phosphorus in agricultural wastewater. Given the increasing global resource scarcity and continuous ecological degradation, the valorization of biomass wastewater has become a critical pathway for the promotion of sustainable development. Biomass wastewater, which contains crop residues, forestry leftovers, and food processing byproducts, has long been regarded as useless waste. However, this wastewater contains abundant organic matter and possesses significant renewable energy potential. The valorization of biomass wastewater can significantly reduce environmental pollution. Through the optimization of the HTC process parameters, we achieved an improvement in the quality and yield of carbonized products, facilitating the efficient recycling and utilization of resources. This research demonstrates that HTC technology can transform agricultural wastewater into valuable biofertilizers, biomass energy, and organic feed, while simultaneously reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and mitigating the environmental impact of agricultural activities. This paper provides a comprehensive exploration of the application of HTC technology in agricultural ecosystems, highlighting its beneficial role in nitrogen and phosphorus management, resource utilization efficiency, and environmental pollution reduction. The findings of this study suggest that HTC technology holds significant potential in optimizing agricultural wastewater treatment, promoting resource recycling, and advancing sustainable agricultural development. Furthermore, this research offers theoretical support and practical guidance for the implementation of HTC technology in agricultural ecosystems, which is of paramount importance in fostering circular economic development and achieving sustainable agriculture.

Precision replication and quality analysis of the molded glass cylindrical lens array

Precision glass molding (PGM) is a well-established and cost-effective manufacturing process used to fabricate high-quality and high-precision optical elements with complex geometries, such as aspherical lens, microlens array, and microprisms. This study focuses on the replication quality of molded glass cylindrical lens arrays, which is critical for achieving exceptional precision and consistency in optical applications. The effect of process parameters on replication quality of the molded cylindrical lens array was studied. A 4 × 1 cylindrical lens array, fabricated using wire electrical discharge machining and polishing, served as the replication mold. The profile deviation and replication ratio were analyzed under different processing conditions using a surface profilometer, while optical quality was assessed through birefringence measurements and light transmission experiments. The results indicated that integrated control of molding parameters is vital for high-fidelity replication, with optimal conditions yielding a height deviation of approximately 4 nm and a filling ratio of 99.999% at 550 °C and 9.8 kN. Additionally, we observed that increased molding force reduces height deviation, while excessive molding temperatures can lead to increased profile deviation. The minimum profile deviation achieved was below 1 µm, and residual birefringence was found to be less than 10 nm. This work not only enhances the understanding of precision glass molding but also provides valuable insights for manufacturers to identify and mitigate potential defects, ultimately improving product reliability and supporting innovation in optical design.

Concurrent enhancement in fire retardancy and mechanical properties of flax/vinyl ester bio-composites via magnesium hydroxide incorporation.

The fire-retardant properties of bio-composites are generally enhanced through nano fillers incorporation at the cost of their mechanical properties. In this study, magnesium hydroxide (MH) nano filler was incorporated into flax/vinyl ester (VE) bio-composite to enhance its fire-retardancy and thermal stability simultaneously with mechanical properties. MH is chemically compatible with cellulosic fibers which played a role in improving the interfacial bonding and hence the mechanical properties in this study. The composites fabrication process parameters including curing temperature and vacuum pressure were also optimized in this study. The concentration of MH was varied as 0, 5, and 10% in the flax/VE composite. The tensile and flexural strengths of the 5% MH filled flax/VE composites were observed to increase by 10% and 48% respectively. This enhancement in strength was attributed to the improved interfacial bonding and compatibility of MH with flax fiber, verified through Field Emission Scanning Electron Microscopy (FESEM) and Fourier Transformed Infrared Spectroscopy (FTIR), respectively. The horizontal burning rate of the composites was decreased by 25% after MH incorporation, which was validated through a limiting oxygen index (LOI) test. The results of cone calorimetry highlighted a decrease of 11.73% in the peak values of heat release rate (HRR) which is a sign of enhancement in fire retardancy. The thermogravimetric analysis also discovered an improvement in the thermal stability of the composites. These bio-composites with improved mechanical, thermal and fire-retardant properties may find their applications in automobiles, marine and aerospace industries.

Development and Validation of a Desktop 3D Printing System with Thermo-Mechanical In Situ Consolidation for Continuous Fiber-Reinforced Polymer Composites

A controlled laminate consolidation is one of the most essential approaches in the production of fiber-reinforced thermoplastics components. With the use of specific consolidation models, almost the entire strength potential of the material can be exploited. However, a controlled thermo-mechanical in situ consolidation (TMIC) strategy in the fused filament fabricated (FFF) process of continuous fiber-reinforced polymer composites (CFRPC) has not been considered so far and leads to deconsolidation defects in the 3D-printed material. These defects in terms of micro and macro volumetric flaws in the joining zone indicate a poor process parameter selection and inadequate thermo-mechanical consolidation. These imperfections lead to a reduction in the fiber volume content and a significant deterioration in the mechanical properties. In this work, a self-developed test rig is presented, which is able to influence and monitor the consolidation during the additive manufacturing (AM) process with a TMIC unit in a controlled manner. To evaluate the test rig, the mechanical construction and the implemented sensors were tested for full functionality. Subsequently, test specimens were fabricated for mechanical characterization using three-point bending (3PB) tests and microstructural analysis. Based on these results, the influence of TMIC, with its dependent process parameters (consolidation force, temperature, printing speed), is presented. A perspective on the future development of controlled consolidation in AM of CFRPC is shown.

Ion beam etching of barium titanate for integrated photonics

The perovskite material barium titanate (BTO) has shown great promise in the next generation electro-optical modulators integrated on Si photonic platforms. In this work, the dry etching of BTO using an argon (Ar) ion beam etching system and the underlying mechanisms are investigated. The results indicate that reducing the pressure and increasing ion beam current, ion energy, and incidence angle all contribute to an increased etch rate. The increase in ion energy and beam current leads to higher surface roughness, whereas a negative incidence angle effectively reduces surface roughness. Through the optimization of various process parameters, an etching recipe showing an etch rate of 16.1 nm/min and a postetching surface roughness of 0.486 nm is realized.

Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples.

Despite the advantages of additive manufacturing, its widespread adoption is still hindered by the poor quality of the fabricated parts. Advanced machine learning techniques to predict part quality can improve repeatability and open additive manufacturing to various industries. This study aims to accurately predict the relative density, surface roughness and hardness of AlSi10Mg samples produced by selective laser melting regarding process parameters such as scan speed, layer thickness, laser power, and hatch distance. For this purpose, data including porosity, surface hardness, and roughness were extracted from the literature, and additional measurements were performed on additive manufactured samples in the current work. This work compares five supervised machine learning algorithms, including artificial neural networks, support vector regression, kernel ridge regression, random forest, and Lasso regression. These models are evaluated based on the coefficient of determination and the mean squared error. On the basis of the computational results, the artificial neural network outperformed in predicting relative density, surface roughness, and hardness. Feature importance analysis on the compiled dataset using ANN revealed that laser power and scan speed are the most important features affecting relative density (e.g., porosity) and hardness, while scan speed and layer thickness significantly impact the surface roughness of the parts. The study identified an optimal laser power and scan speed region that achieves a relative density > 99%, surface roughness < 10 µm, and hardness > 120 HV. The results presented in this study provide significant advantages for additive manufacturing, potentially reducing experimentation costs by identifying the process parameters that optimize the quality of the fabricated parts.

Review on Recent Progress and Challenges in Laser‐Structuring of Electrodes for Lithium‐Ion Batteries

AbstractThe traditional lithium‐ion battery (LIB) electrode has reached its limitations in terms of energy density and fast charging capabilities. To enable implementation in electric vehicles and other applications, 3D structuring is a method frequently proposed to allow for performance enhancement due to an increase in electrode surface area. Due to the decrease in lithium ion diffusion pathways, potential for application in thick electrodes exists, allowing further enhancement of energy density. Laser ablation is a promising manufacturing technique for the fabrication of 3D structured electrodes due to its ease of implementation in the existing electrode manufacturing process, as well as its flexibility and precision. This review details the main process parameters of the laser ablation process and their influence on the process efficiency and quality of generated structures. It further summarizes recent progress in finding the optimal electrode structure for both cathode and anode, and discusses the opinions on relative importance of cathode v. anode structuring. Lastly, current progress and challenges for the implementation in the battery manufacturing process are detailed, providing techniques for the upscaling and increase of belt speeds.

Systematic Investigation of the Residence Time Distribution in a Twin‐Screw Extruder for the Continuous Mixing Process of Electrode Slurry in the Battery Cell Production

Due to high scrap rates and manufacturing costs, battery cell production requires continuous process optimization. The potential for material efficiency is particularly high in electrode production, specifically in the mixing process. Challenges in the continuous mixing process are related to automation and traceability of material. As one of the most relevant parameters, the residence time of particles must be known, otherwise it is not possible to make a statement about the traceability of the slurry ingredients. Without knowledge of the residence time distribution (RTD), autonomous process control or traceability of battery cells and their components is not possible. The influence of process and material parameters on the RTD of the continuous mixing process in battery cell production is being systematically investigated. Based on a design of experiment, the mean residence time and the RTD are determined for a graphite‐based anode slurry by manipulating the conductivity by adding a tracer. Special attention is given to the properties of the tracer as well as the tracer behavior within the mixing process. The influence of different parameters is analyzed based on the conductivity changes. It is shown that the parameters mass flow and solid content have the greatest influence on the RTD.

Quantum Computing Aided Quality Control and Defect Detection in Laser Melting Deposition Manufacturing

An innovative additive manufacturing process called laser melting deposition (LMD) has many benefits for precisely generating complex structures. However, process optimization, defect detection and quality control are difficulties that LMD faces despite its potential. The manual inspection and traditional algorithms used in traditional defect identification and quality control procedures are time-consuming, computationally costly and prone to errors in high-dimensional datasets. This research examines the integration of defect detection and quality control methods in LMD production. Information from various sensors, including optical, laser, pressure and temperature sensors, are used to track the LMD process. To guarantee consistency and correctness in the dataset, the initial stage entails pre-processing the gathered data using cleaning and min–max normalization techniques. Feature extraction is performed using Principal Component Analysis (PCA), which lowers the data’s dimensionality while keeping the crucial details required for a successful analysis. An approach called Dynamic Artificial Rabbits optimized quantum support vector machine (DARO-QSVM) is used to identify quality control parameters and identify flaws in the LMD process; the suggested methodology seeks to increase the accuracy (95.36%) of defect detection, and optimize process parameters in real-time. This study demonstrates how high-dimensional data problems and the dynamic, nonlinear character of LMD could be resolved with quantum-based technologies, providing a viable way forward for improvements in manufacturing quality control in the future.