Manufacturing Process Control Research Articles

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

AbstractIn the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.

The Application of Artificial Intelligence Technology in Mechanical Manufacturing and Automation

In order to achieve intelligent production and quality control, improve production efficiency and accuracy, the author proposes an application method of artificial intelligence technology in mechanical manufacturing and automation. The author aims to explore the specific application of artificial intelligence technology in the mechanical manufacturing industry and summarize the main advantages of automation technology. By analyzing these advantages, the aim is to provide targeted recommendations for future technological development. Meanwhile, the author aims to provide theoretical support for promoting the application of modern artificial intelligence technology and automation technology in the field of mechanical manufacturing. In the manufacturing process, intelligent production and quality control can be achieved, improving production efficiency and accuracy; In the field of automation, artificial intelligence technology can achieve intelligent control systems and autonomous decision-making, improving the flexibility and adaptability of production lines. Overall, the application of artificial intelligence technology has brought revolutionary changes to mechanical manufacturing, promoting the upgrading and development of the industry.

Metode Pelaksanaan Pembuatan PC Spun Pile dengan Mutu F'c 52 MPa Pada PT. Wijaya Karya Beton Tbk PPB Majalengka

A concrete plant, also known as a batch plant or batching plant or a concrete batching plant, is equipment that combines various ingredients to form concrete. This implementation aims to determine the manufacturing process and quality control of concrete product production. The method used in this implementation is descriptive quantitative. Steps include operational variables to determine the problem and indicators of the cause of the problem. Data collection through observation and documentation. The results of this implementation show that the implementation of manufacturing at PT WIKA Beton Tbk PPB Majalengka aims to make good concrete products, the factors for making PC Spun Pile products are made based on work instructions or standard operational procedures.

Extension of paperboard modelling with Föppl–von Karman terms for improved stress state under suction pressure

A practical method for solving the bending stiffness of a thin orthotropic plate based on measured deflection and known loading was studied. The target application was paperboard manufacturing and related process control. Here, finite element (FE) method was used to create virtual paperboard and its deflection data. The accuracy of the linear Kirchhoff plate model used in the practical method was tested against the FE model with known load and material properties. It was found that in the case of significant deflections, the linear Kirchoff model was not accurate. The non-linear Föppl--von Kàrmàn model takes into account the occurring membrane stresses and extends on the Kirchhoff model, allowing for larger deflection. The non-linear Föppl--von Kàrmàn model was found to successfully describe the simulated situation and could be used to better solve for the paperboard's bending stiffness values over two axes.

Fabric Defect Detection Using Transfer Learning

Transfer learning in fabric defect detection involves utilizing pre-trained deep learning models on a large dataset, typically from a different domain, and fine-tuning them on a smaller dataset that is specific to fabric defects. By leveraging transfer learning, the limitations of limited annotated data for fabric defect detection can be overcome by utilizing the knowledge gained from a more extensive and diverse dataset. The pre-trained model's learned features are adjusted to recognize specific fabric defect patterns, resulting in more accurate and efficient defect detection. This approach reduces the reliance on a massive labelled dataset for training, which is particularly beneficial in industrial applications where obtaining a vast amount of annotated fabric defect images may be challenging. Ultimately, transfer learning enhances the model's ability to generalize and detect fabric defects with higher precision, thereby contributing to improved quality control in textile manufacturing processes.

Investigation of the Impact of Manufacturing Methods on Protein-Based Long-Acting Injectable Formulations: A Comparative Assessment for Microfluidics vs. Conventional Methods.

Therefore, rhCCL22-loaded poly(lactic-co-glycolic) acid (PLGA) microparticles were prepared by conventional homogenization and microfluidics methods. The resulting microparticles were analyzed comparatively, focusing on critical quality attributes such as microparticle size, size distribution, morphology, drug encapsulation efficiency, release kinetics, and batch-to-batch variations in relation to the manufacturing method. Our results demonstrated that the conventional method resulted in microparticles with denser surface porosity and wider size distribution as opposed to microparticles prepared by the microfluidics method, which could contribute to a significant difference in the drug-release kinetics. Additionally, our findings indicated minimal variation within batches for the microparticles prepared by the microfluidics method. Overall, this study highlights the comparative assessment of several critical quality attributes and batch variations associated with the manufacturing methods of protein-loaded microparticles which is crucial for ensuring consistency in efficacy, regulatory compliance, and quality control in the drug formulation manufacturing process.

Tensor‐based process control and monitoring for semiconductor manufacturing with unstable disturbances

Tensor‐based process control and monitoring for semiconductor manufacturing with unstable disturbances

A Woman with Plaques: Atypical Mycobacterial Infection Case Report

Cosmetic enhancement products administered by injection are readily available online. These materials circumvent both the quality control manufacturing process and physician supervision. One of the most serious complications is infection with Mycobacterium abscessus. Described is the case of a forty-six-year-old female who purchased a compound promoted for dermal injection and subsequently developed a persistent skin infection with M. abscessus.

Full-scale evaluation of FFU configurations in an optical cleanroom injection molding for improving thermal performance and contaminant removal

Cleanrooms are critical in the semiconductor industry, particularly for optical fabrication, where even minor contamination can compromise product quality. Effective airflow and uniform airflow distribution help to prevent contaminant accumulation, mitigate temperature variations, and avoid cross-contamination, which is crucial for achieving high-quality results and compliance with industry standards. This study introduces a novel approach to optimizing Fan Filter Unit (FFU) configurations to enhance thermal performance and contaminant removal in cleanroom settings. Integrating field measurements with advanced Computational Fluid Dynamics (CFD) simulations to comprehensively evaluate four distinct FFU designs, from dispersed to centralized configurations, focused on their impact on airflow dynamics, temperature uniformity, and particle concentration. The study identifies a significant advancement in FFU configuration by demonstrating that centralized arrangements (Designs 2 and 4) substantially improve cleanroom performance. Design 4 emerged as the most effective, showcasing a marked improvement in airflow circulation, a 12 % reduction in temperature variation, and a 15 % decrease in particle concentration compared to conventional designs. This achievement underscores the novel contribution of this study in optimizing FFU configurations to enhance both thermal performance and contaminant control. These findings not only advance the theoretical understanding of FFU design but also provide practical insights for improving cleanroom environments. The study offers a significant step forward in developing efficient cleanroom designs, contributing valuable guidelines for achieving good environmental control in sensitive manufacturing processes.

A mechanical characteristic capture method considering printing configurations for buildability modeling in concrete 3D printing

The structure failure modeling of 3D printing concrete (3DPC) during production is crucial for structure design, manufacturing process control and optimization. Serving as model inputs, the accuracy of 3DPC fresh material properties measurement highly affects the model prediction performance. The measured mechanical properties of freshly printed concrete strongly depend on the geometry, deformation and hardening process of used samples in testing. Herein, we propose an all-in-one method (AIOM) that synchronously considers these key factors (layer geometry, deformation, and hardening process) to more accurately capture the early-age mechanical performance distribution in printed structures. Different parametric-mechanical buckling models and two printable cementitious materials with distinct hardening characteristics (printable cement and printable geopolymer) were used to validate the performance of AIOM, with the traditional testing method, uniaxial unconfined compression test (UUCT), as the reference. Compared with UUCT, AIOM can improve the buildability prediction accuracy by 11.9 % to 50.8 % for different validation scenarios. This novel testing method for 3DPC fresh material properties contributes to improving the accuracy of 3DPC structure failure models, thereby facilitating a better production phase control for 3DPC.

Advances in Laser Powder Bed Fusion of Tungsten, Tungsten Alloys, and Tungsten-Based Composites.

In high-tech areas such as nuclear fusion, aerospace, and high-performance tools, tungsten and its alloys are indispensable due to their high melting point, low thermal expansion, and excellent mechanical properties. The rise of Additive Manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (L-PBF), has enabled the precise and rapid production of complex tungsten parts. However, cracking and densification remain major challenges in printing tungsten samples, and considerable efforts have been made to study how various processing conditions (such as laser power, scanning strategy, hatch spacing, scan speed, and substrate preheating) affect print quality. In this review, we comprehensively discuss various critical processing parameters and the impact of oxygen content on the control of the additive manufacturing process and the quality of the final parts. Additionally, we introduce additive manufacturing-compatible W materials (pure W, W alloys, and W-based composites), summarize the differences in their mechanical properties, densification, and microstructure, and further provide a clear outlook for developing additive manufactured W materials.

Slot-die coating of vinyl-PDMS composite membranes for alcohol perm-selective pervaporation

The use of pervaporation membranes has shown great potential in the extraction of bioethanol. However, a simple, continuous, and controllable alcohol perm-selective membrane manufacturing process is needed for practical applications. In this study, the slot-die coating technique was utilized to deposit low-viscosity vinyl-polydimethylsiloxane (vinyl-PDMS) and crosslinker onto a porous substrate. A defect-free separation layer could be created within just 20 min, which includes the membrane forming and drying process, due to the rapid hydrosilylation reaction between vinyl-PDMS and the crosslinker. Therefore, the efficient fabrication process is suitable for continuous roll-to-roll industrial production. Additionally, by optimizing the slot-die parameters, the thickness of the separation layer could be regulated from 0.4 to 15 μm. The obtained vinyl-PDMS composite membrane demonstrated a total flux of 2.8 kg m−2 h−1 and a separation factor of 10.3 for 5 wt% ethanol/water mixture at 60 °C.

Maximizing process control and efficiency in CAR-T cell manufacturing

Maximizing process control and efficiency in CAR-T cell manufacturing

Neural network driven sensitivity analysis of diffraction-based overlay metrology performance to target defect features

Overlay (OVL) is one significant performance indicator for the lithography process control in semiconductor manufacturing. The accuracy of the OVL metrology is extremely critical for guarantee the lithography quality. Currently, diffraction-based overlay (DBO) is one of the mainstream OVL metrology techniques. Unfortunately, the accuracy of the DBO metrology is largely affected by the defect features of the OVL target. Therefore, there is a strong need to investigate the impacts of these target defects on the DBO metrology performance. However, efficiently investigating the statistical and interactive impacts of various DBO target defects remains challenging. This study aims to address this issue through proposing an intelligent sensitivity analysis approach. A cumulative distribution based global sensitivity analysis (GSA) method is utilized to assess the nonlinear influences of multiple defects in the OVL target on the DBO inaccuracy. The scenarios with both known and unknown distributions of the OVL target defects are considered. For the former, a neural network driven forward model is constructed for fast calculating the optical diffraction responses to accelerate the GSA process. For the latter, another neural network based inverse model are built for efficiently estimating the distribution of the target defects. Finally, a series of simulation experiments are conduct for typical DBO targets with multiple common defect features. The results demonstrate the effectiveness and robustness of the proposed approach as well as give valuable insights into the DBO defect analysis. Our study provides a strong tool to assist the practitioners in achieving intelligent and efficient DBO analysis and thus in enhancing OVL metrology performance.

Development of Sustainable 3D Printable Ternary Composite

3D printing technology has the potential to reduce construction waste through its controlled manufacturing process and optimized material consumption, making it a promising sustainable construction approach with reduced environmental impact. However, current 3D printed structures often have a high carbon footprint due to the large amounts of fine-grained primary materials used in mortar composites. Most efforts to develop 3D printed structures are associated with concrete and Portland cement-based materials.This research presents an innovative approach for the reuse of gypsum to develop a material suitable for 3D printing, with the goal to industrialize the usage of waste gypsum in civil engineering. The objective of this study is to develop a sustainable gypsum-cement-pozzolanic (GCP) ternary binder composite, incorporating recycled industrial waste gypsum materials such as construction demolition waste gypsum (CDG), along with Portland cement and secondary pozzolanic materials. The GCP ternary composite combines the desired properties of gypsum, such as fast setting time, with those of Portland cement, which offers high final strength. The addition of a pozzolanic component is essential to ensure the chemical stability of this ternary system.As part of this study, ternary composites were designed and tested for their mechanical properties, durability, and suitability for 3D printing. Development and optimization of the ternary composite was carried out as a loop of procedures (Fig.1). Through these optimization procedures, a range of mixtures with necessary properties for 3D printing was found. To ensure sustainability and to assess the environmental impact of the mixtures, life cycle assessment (LCA) was performed.The LCA of GCP composites showed at least 1.5 times lower embodied energy and carbon dioxide emissions compared to Portland cement-based mortars. This is without considering avoided emissions from the end-of-life stage of gypsum waste, as well as higher technical properties that make GCP composites superior to regular cement composites. Considering these factors, GCP composites exhibit even lower emissions and are considered a viable alternative to Portland cement-based mortars.

Q3D: a complete solution for quality control and inspection in additive manufacturing processes

PurposeThis paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies.Design/methodology/approachThe study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments.FindingsThe main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes.Originality/valueThe proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

Coating process control in lithium-ion battery manufacturing using cumulative sum charts

Coating process control in lithium-ion battery manufacturing using cumulative sum charts

Aflatoxin and microbial safety in commercial confectionery peanut paçoca produced in São Paulo state – Brazil

Paçoca is a typical Brazilian confectionery of Indigenous origin prepared by mixing ground roasted peanuts, sugar, and salt. A total of 52 samples of paçoca (37 pressed, 15 cooked), produced and sold in the state of São Paulo, were evaluated for aflatoxin content (AFB1, AFB2, AFG1 and AFG2), moisture content and water activity. The microbiological quality was assessed by counting mesophilic aerobic bacteria, fungal and yeast counts, most likely number of thermotolerant coliforms and Escherichia coli and Salmonella sp. The limit of quantification (LOQ) by Ultra Performance Liquid Chromatography coupled to the Mass Spectrometer (UPLC-MS/MS) ranged from 0.29 μg kg−1 for AFB1 and AFG2; 0.36 μg kg−1 for AFG1 and 0.37 μg kg−1 for AFB2. The recovery averages were 89.2, 83.5, 80.5 and 82.8% for AFB1, AFB2, AFG1 and AFG2. 71% of the samples showed aflatoxin contamination, ranging from 0.29 to 38.8 μg kg−1 (mean of 7.06 μg kg−1). Six samples (16%) exceeded the limit value tolerated by the Brazilian guideline for AFT (20 μg kg−1). The mean AFT content in cooked paçocas (19.55 μg kg−1) was higher than the levels in pressed paçocas (9.08 μg kg−1) (p < 0.05). AFB1 had a higher incidence in the two groups of paçocas (75% pressed and 60% cooked). The mean moisture content and water activity of cooked paçocas (8.86% and 0.7081) were higher than those of pressed paçocas (2.08% and 0.4863). Twenty-one samples (56%) showed fungal and yeast contamination ranging from 1.0 to 4.25 log CFU g−1. The total count of aerobic mesophilic bacteria in cooked paçocas (4.85 log CFU g−1) was also higher than in pressed paçocas (2.77 log CFU g−1) (p < 0.05). E. coli was confirmed in these samples in the range of 3.0–160 NMP g−1. Salmonella sp. was detected in 2 samples of pressed paçocas, indicating poor hygiene, inefficient heat treatment or possible cross-contamination. The results of this study demonstrate the need for greater control of the paçoca production chain through the adoption of Good Manufacturing Practices and process control, to prevent exposure of the population to aflatoxins and possible pathogenic microorganisms.

Powder-size driven facile microstructure control in powder-fusion metal additive manufacturing processes

Microstructure control in metal additive manufacturing is highly desirable for superior and bespoke mechanical performance. Engineering the columnar-to-equiaxed transition during rapid solidification in the additive manufacturing process is crucial for its technological advancement. Here, we report a powder-size driven melt pool engineering approach, demonstrating facile and large-scale control in the grain morphology by triggering a counterintuitive response of powder size to the additively manufactured 316 L stainless steel microstructure. We obtain coarse-grained (>100 μm) or near-monocrystalline microstructure using fine powders and near-equiaxed, fine-grained (<10 μm) microstructure using coarse powders. This approach shows resourceful adaptability to directed energy deposition and powder-bed fusion with no added cost, where the particle-size dependent powder-flow preheating effects and powder-bed thermophysical properties drive the microstructural variations. This work presents a pathway for leveraging feedstock particle size distribution towards more controllable, cost-effective, and sustainable metal additive manufacturing.