Manufacturing Control Research Articles

Electrostatically Actuated X-Band Mesh Reflector with Bend-Formed Support Structure

Increasing the size of radio frequency (RF) reflectors in space can enhance gain and spatial resolution in applications such as space-based communication and remote sensing. The size of current passive deployable reflectors is limited by a tradeoff between diameter and surface precision, which causes RF performance to degrade as size increases. A promising approach to overcome this tradeoff is to combine in-space manufacturing, which enables large structures, with distributed embedded actuation, which enables precise control over the reflector surface. Here we demonstrate a reflector antenna system that integrates these two technologies, using a candidate in-space manufacturing process, termed “Bend-Forming,” with embedded electrostatic actuators. We design and fabricate a 1-m-diam prototype of an electrostatically actuated X-band reflector with a knitted gold-molybdenum mesh as the reflector surface, carbon-fiber-reinforced plastic booms as electrodes, and a truss support structure fabricated with Bend-Forming. We characterize the RF performance of this reflector, successfully demonstrating i) control over a wide range of focal lengths by suppressing a pull-in instability and ii) beam steering over an angular range of 4.2° via asymmetric electrostatic actuation. This work lays the foundation for future space communication and remote sensing technologies, offering a scalable solution to enhance RF performance through in-space manufacturing and precision control.

Piezoelectric-pneumatic material jetting printing for non-contact conformal fabrication of high-temperature thick-film sensors

Conformal printing of high-temperature thick-film sensors (TFSs) on intricate surfaces is crucial for precise integration and enhanced functionality. However, traditional contact printing faces challenges in terms of the complex motion control of 3D surfaces. With the core concept of non-contact printing, this study employs piezoelectric-pneumatic material jetting (PPMJ) to achieve rapid and uniform manufacturing of complex and multi-material conformal TFSs. PPMJ enables a wide viscosity range (50–100,000 mPa.s) and high solid content (60 wt%), accommodating various types of inks, from insulating to semiconductor materials to high-temperature alloy slurries, thus providing a broad operational window for shape control and performance modulation in the conformal manufacturing of TFSs on curved surfaces. Microscale shape control of ceramic ink features, including a parallel three-phase contact line, uniform print dimensions, and post-curing shape control, was systematically investigated. PPMJ printing facilitates the successful in situ fabrication of multi-layer TFSs, featuring the self-assembly of droplets and dependable printing consistency, with nozzle-to-substrate heights ranging from 1 to 10 mm, thus advancing the structural arrangement, design, and manufacturing of conformal multi-layer TFSs on curved surfaces. For example, the polymer-derived ceramic heat flux sensor printed by PPMJ demonstrates repeatable high-temperature responsiveness and can be integrated on the surface of turbine blades, withstanding thermal impacts from flames exceeding 800 °C. Hence, PPMJ printing technology opens new possibilities for the conformal manufacturing of various sensors on complex 3D surfaces.

Chicken Sausage Production Process at PT. X

This study investigated the chicken sausage production process and explored problem-solving strategies using fishbone diagrams. The analysis identified key stages, including raw material preparation, grinding, mixing, molding, cooking, cooling, cutting, and packaging. Fishbone diagrams were employed to systematically dissect process issues, revealing root causes related to personnel, methods, machines, materials, and environment. The findings highlight the effectiveness of fishbone diagrams for identifying and addressing production bottlenecks, paving the way for optimized efficiency and quality control in sausage manufacturing. Highlights : Process breakdown: The study identified key stages in chicken sausage production (preparation, grinding, mixing, etc.) using fishbone diagrams. Root cause analysis: Fishbone diagrams helped pinpoint root causes of process issues across various factors (personnel, methods, machines, etc.). Quality improvement: The findings suggest fishbone diagrams as an effective tool for identifying and addressing bottlenecks, leading to optimized efficiency and quality control. Keywords: chicken sausage, production process, fishbone diagrams, problem-solving, quality control

Antibody-Drug Conjugate Overview: a State-of-the-art Manufacturing Process and Control Strategy.

Antibody-drug conjugates (ADCs) comprise an antibody, linker, and drug, which direct their highly potent small molecule drugs to target tumor cells via specific binding between the antibody and surface antigens. The antibody, linker, and drug should be properly designed or selected to achieve the desired efficacy while minimizing off-target toxicity. With a unique and complex structure, there is inherent heterogeneity introduced by product-related variations and the manufacturing process. Here this review primarily covers recent key advances in ADC history, clinical development status, molecule design, manufacturing processes, and quality control.The manufacturing process, especially the conjugation process, should be carefully developed, characterized, validated, and controlled throughout its lifecycle. Quality control is another key element to ensure product quality and patient safety. A patient-centric strategy has been well recognized and adopted by the pharmaceutical industry for therapeutic proteins, and has been successfully implemented for ADCs as well, to ensure that ADC products maintain their quality until the end of their shelf life. Deep product understanding and process knowledge defines attribute testing strategies (ATS). Quality by design (QbD) is a powerful approach for process and product development, and for defining an overall control strategy. Finally, we summarize the current challenges on ADC development and provide some perspectives that may help to give related directions and trigger more cross-functional research to surmount those challenges.

Integration of Artificial Intelligence into Metallography: Area-wide Analysis of Microstructural Components of a Jominy Sample

Abstract Analysing the microstructure is an essential part of quality control in many steel manufacturing and processing operations. In this work, a promising method for autonomous analysis of microstructures in low-alloy steels based on artificial intelligence image analysis is presented. This study focuses on the classification of different microstructure components in metallographic images of steel microstructures using a Deep Convolutional Neural Network (DCNN) model. Since the accuracy of the model strongly depends on the size of the data set, a data set consisting of two million optical microscopy images was created to ensure the presence of different microstructure components and their combinations for training the system. The Jominy test was performed to verify the accuracy and capability of the microstructure analysis software. The AI makes it possible to analyse large amounts of image data with high precision and at the same time with less effort than conventional methods of microstructure components analysis.

High-Throughput Microfluidic Extraction of Platelet-free Plasma for MicroRNA and Extracellular Vesicle Analysis.

Cell-free RNAs and extracellular vesicles (EVs) are valuable biomarkers in liquid biopsies, but they are prone to preanalytical variabilities such as nonstandardized centrifugation or ex vivo blood degradation. Herein, we report a high-throughput and label-free inertial microfluidic device (ExoArc) for isolation of platelet-free plasma from blood for RNA and EV analysis. Unlike conventional inertial microfluidic devices widely used for cell sorting, a submicrometer size cutoff (500 nm) was achieved which completely removed all leukocytes, RBCs, platelets, and cellular debris based on differential lateral migration induced by Dean vortices. The single-step operation also reduced platelet-associated miRNAs (∼2-fold) compared to centrifugation. We clinically validated ExoArc for plasma miRNA profiling (39 samples) and identified a 7-miRNA panel that detects non-small cell lung cancer with ∼90% sensitivity. ExoArc was also coupled with size exclusion chromatography (SEC) to isolate EVs within 50 min with ∼10-fold higher yield than ultracentrifugation. As a proof-of-concept for EV-based transcriptomics analysis, we performed miRNA analysis in healthy and type 2 diabetes mellitus (T2DM) subjects (n = 3 per group) by coupling ExoArc and ExoArc+SEC with quantitative polymerase chain reaction (RT-qPCR) assay. Among 293 miRNAs detected, plasmas and EVs showed distinct differentially expressed miRNAs in T2DM subjects. We further demonstrated automated in-line EV sorting from low volume culture media for continuous EV monitoring. Overall, the developed ExoArc offers a convenient centrifugation-free workflow to automate plasma and EV isolation for point-of-care diagnostics and quality control in EV manufacturing.

Study on the sintering behavior of the ceramic precursor Daqiuni clay

Daqiuni clay is an aluminosilicate clay with unique properties that make it a promising ceramic precursor. Understanding its sintering behavior is crucial for quality control in manufacturing. This study aimed to investigate the phase evolution, densification process, and reaction kinetics during sintering of Daqiuni clay. A combination of X-ray diffraction (XRD), simultaneous thermogravimetric-differential scanning calorimetry (TG-DSC), scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FTIR) techniques were employed to comprehensively analyse the phase transformations. The densification behavior, apparent activation energies, and reaction mechanisms were elucidated through thermodilatometric analysis conducted at varying heating rates. Four stages were identified during sintering including dehydration, dehydroxylation, mullite formation, and partial melting. The maximum shrinkages at each stage were measured. Kissinger analysis yielded apparent activation energies of 122.90, 258.40, 361.98 and 348.69 kJ/mol for the dehydroxylation, mullite nucleation, mullite growth, and partial melting stages, respectively. The dehydroxylation and partial melting processes followed the mechanisms of 3rd order reaction and 3D diffusion. Mullite formation was a two-step nucleation and growth process following the JMA model. This study clarified the phase evolution of Daqiuni clay during sintering.

Manufacture and Deformation Angle Control of a Two-Direction Soft Actuator Integrated with SMAs.

In this contribution, the development of a 3D-printed soft actuator integrated with shape memory alloys (SMA) wires capable of bending in two directions is presented. This work discusses the design, manufacturing, modeling, simulation, and feedback control of the actuator. The SMA wires are encased in Polytetrafluoroethylene (PTFE) tubes and then integrated into the 3D-printed matrix made of thermoplastic polyurethane (TPU). To measure and control the deformation angle of the soft actuator, a computer vision system was implemented. Based on the experimental results, a mathematical model was developed using the system identification method and simulated to describe the dynamics of the actuator, contributing to the design of a controller. However, achieving precise control of the deformation angle in systems actuated by SMA wires is challenging due to their inherent nonlinearities and hysteretic behavior. A proportional-integral (PI) controller was designed to address this challenge, and its effectiveness was validated through real experiments.

Training and Assistance in Tablet Manufacturing and Tablet Quality Control

Giving drug to patients or people who have been diagnosed with a disease is very rarely given as a pure active ingredient so most drugs are given in a dosage form. One form of drug dosage preferred by adult patients is a solid dosage form in the form of tablet. The aim is to increase understanding and technical abilities of pharmacy students in tablet manufacturing and tablet quality control. The method used was theoretical, practical and final evaluation which was carried out on 18 pharmacy students in the Pharmaceutical Technology Laboratory on the Universitas Muhammadiyah Lamongan. The result was the community service activities through training in tablet making and tablet quality control received enthusiasm and good responses from a total of 18 students participating. It is hoped that this training activity will be the beginning of opening up collaboration between the campus and private companies that make legal medicines and state-owned companies in expanding information and knowledge regarding tablet manufacturing and tablet quality control.

The thermal history of the directed energy deposition process monitored by pyrometer and camera

Abstract In the process of directed energy deposition (DED), the local thermal history of a component can change significantly owing to its complex heat transfer characteristics. The main objective of this study is to elucidate the distribution of the thermal history of a static scroll plate by monitoring its manufacturing process through a pyrometer and camera and to provide insights into the factors affecting the variation of its thermal history from the point of view of the part shape and scanning strategy. The melt pool area and temperature were extracted using a camera and pyrometer as the thermal features under study, and the thermal features were mapped in three dimensions. This mapping strategy provides a comprehensive visualization of the distribution of thermal features in parts with equal and variable cross-section structures. In the case of an equal cross-section structure, three factors, printing time, scan vector length, and probability density, are selected to analyze the main factors affecting the distribution of thermal features. In the case of the variable cross-section structure, the effects of the layer printing time, cumulative layer printing time, and probability density on the distribution of thermal features were analyzed. By calculating the correlation between the thermal features of the monitored signals and the factors mentioned above, it was found that the factors affecting the thermal distribution of the parts vary in different structures. In equal cross-section structures, the probability density plays a pivotal role, while in variable cross-section structures, the cumulative layer printing time has the most significant influence on the thermal distribution. The above studies have provided a better understanding of the thermal processes of DED technology, paving the way for the control and optimization of DED manufacturing.

Design, Manufacturing, and Open-Loop Control of a Soft Pneumatic Arm

Soft robots distinguish themselves from traditional robots by embracing flexible kinematics. Because of their recent emergence, there exist numerous uncharted territories, including novel actuators, manufacturing processes, and advanced control methods. This research is centred on the design, fabrication, and control of a pneumatic soft robot. The principal objective is to develop a modular soft robot featuring multiple segments, each one with three degrees of freedom. This yields a tubular structure with five independent degrees of freedom, enabling motion across three spatial dimensions. Physical construction leverages tin-cured silicone and a wax-casting method, refined through an iterative processes. PLA moulds that are 3D-printed and filled with silicone yield the desired model, while bladder-like structures are formed within using solidified paraffin wax-positive moulds. For control, an empirically fine-tuned open-loop system is adopted. This paper culminates in rigorous testing. Finally, the bending ability, weight-carrying capacity, and possible applications are discussed.

Fault diagnosis in semiconductor manufacturing processes using a CNN-based generative adversarial network1

In the current industry, quality inspection in semiconductor manufacturing is of immense significance. Significant achievements have been made in fault diagnosis in fabricated semiconductor wafer manufacturing due to the development of machine learning. Since real-time intermediate signals are non-linear and time-varying, the signals undergo various distortions due to changes in equipment, material, and process. This leads to a drastic change in information in intermediate signals. This paper presents a fault diagnosis model for semiconductor manufacturing processes using a generative adversarial network (GAN). The study aims to address the challenges associated with efficient and accurate fault identification in these complex processes. Our approach involves the extraction of relevant components, development of a paired generator model, and implementation of a deep convolutional neural network. Experimental evaluations were conducted using a comprehensive dataset and compared against six existing models. The results demonstrate the superiority of our proposed model, showcasing higher accuracy, specificity, and sensitivity across various shift tasks. This research contributes to the field by introducing a novel approach for fault diagnosis, paving the way for improved process control and product quality in semiconductor manufacturing. Future work will focus on further optimizing the model and extending its applicability to other manufacturing domains.

Determination of quality criteria for candidate standard for anti-D immunoglobulin

Quality control of medicines is an integral part of the technological cycle, obligatory for the release of products into public distribution. Both in Russia and abroad, when carrying out of laboratory testing of the preparations, it is prescribed to use the reference materials – the certified standard samples. Currently, it is important for the Russian pharmaceutical industry to provide the methods for evaluating the specific activity of human anti-D immunoglobulin with a national standard sample. Its production is impossible without the production of stabilized concentrate of anti-D antibodies and verifying the suitability of each batch for sale. This requires the establishment of specification requirements and the development of regulatory documentation.Aim is to define the quality criteria for candidate standard for anti-D immunoglobulin.Material and methods. The national and foreign normative bases in the field of quality control of blood products, manufacturing and application of biological standards of medicines were analyzed. Three series of lyophilized concentrate of anti-D antibodies were obtained and a laboratory-experimental study of their properties was carried out.Results and discussion. As a result of the work done, technical specifications were approved, including a list of consumer characteristics, a description of the methods for their assessment and relevant norms.Conclusions. The established requirements can become the basis for the releasing quality control of the candidate standard for anti-D immunoglobulin during its serial production.

Asia Core Dossier: Standardizing CMC Requirement to Facilitate Best Case Submissions in Asia.

When the regulatory requirements are converged or harmonized, the country-specific variance of countries is often reduced or omitted, and this facilitates the possibility of preparing a core dossier that caters to multiple countries. When such options of a core dossier are acceptable to multiple countries, the resource required to prepare the dossier and the time taken to prepare it is also reduced, thus eliminating resource constraints in supporting dossier planning and preparation and indirectly facilitating earlier submission in countries. In this paper, the authors have illustrated a process applied to standardize the dossier requirements amongst selected countries in Asia, producing an output of a core dossier that applies to four submission types amongst these countries. The core dossier adopts the International Council for Harmonization-Common Technical Dossier format as a reference. Main focus is the standardization of format and requirements within the Module 3 or Chemistry Manufacturing Controls sections of the dossier, which from the authors' organizational experience usually notes a higher variances and country-specific elements. Development of the dossier standardization process is due to an internal hurdle within the authors' organization, where global resource constraints and prioritizations of dossier preparation and compliance review process needed to be improved to facilitate earlier or near-simultaneously submissions in the majority of the Asia countries. The paper demonstrates an assessment of the dossier components and standardization to assemble a fit-for-purpose core dossier termed 'Asia Core Dossier' (ACD). ACD has been successfully implemented within the authors' organization to reduce country-specific requirements and facilitate earlier (fit for strategy) submissions in the selected Asia countries. The paper also discusses the tangible benefits of the authors' experiences from utilizing the ACD. Regulatory professionals in different organizations could reference the ACD as a template for preparing a simplified and efficient dossier and as a relevant component of Good Submission Practice(GSubP).

Statistical Quality Control in Manufacturing and Managing Processes for Continuous Improvement of Organisation

Statistical Quality Control in Manufacturing and Managing Processes for Continuous Improvement of Organisation

Motion-copying method with symbol sequence-based phase switch control for intelligent optical manufacturing

Motion-copying method with symbol sequence-based phase switch control for intelligent optical manufacturing

Metallurgy and Solidification Microstructure Control of Fusion-Based Additive Manufacturing Fabricated Metallic Alloys: A Review

Metallurgy and Solidification Microstructure Control of Fusion-Based Additive Manufacturing Fabricated Metallic Alloys: A Review

Visual Defect Detection and Analysis of Digital Robot Based on Virtual Artificial Intelligence Algorithm

The current research background of digital robot visual defect detection focuses on the application of virtual artificial intelligence algorithms. Convolutional neural networks (CNNS) perform well in the field of image processing and can learn and extract image features, which provides a more refined analysis ability for the visual defect detection of digital robots. Therefore, this paper focuses on the application of convolutional neural networks in digital robot vision defect detection, and explores the main processes of data collection and preprocessing, feature extraction, model training and defect detection in detail. Finally, the results of simulation experiments are as follows: Compared with traditional methods that rely on edge detection or threshold segmentation, the optimized defect detection accuracy rate is increased by 11.4%, and the image detection speed is increased by about 33.9%. The research results have important practical significance for improving the quality control of industrial manufacturing, which can not only improve the quality of products, but also adapt to the constant changes of the production line.

Process Monitoring, Diagnosis and Control of Additive Manufacturing

Process Monitoring, Diagnosis and Control of Additive Manufacturing

Towards Minimizing Domain Gap When Using Synthetic Data in Automotive Vision Control Applications

This paper explores the generation and use of synthetic data in the development of AI-based vision control systems within the automotive industry. Addressing the challenges of collecting diverse and high-quality datasets, we demonstrate the effectiveness of synthetic data in training deep learning models for defect detection and quality inspection. By leveraging controlled randomizations during image dataset generation, we mitigate the domain gap between synthetic and real-world data, enhancing model performance and generalization. Our approach, validated across multiple industrial applications, significantly accelerates the development cycle and improves the accuracy of AI-based inspection systems. This study highlights the potential of synthetic data to overcome data scarcity and improve the efficiency and effectiveness of AI-driven quality control in automotive manufacturing.