Product Manufacturing Process Research Articles

Analysis Use of PERT/CPM: Optimizing Manufacturing Production Process Resources for Building Construction Projects on Kunjang River Region, East Kalimantan

Analysis Use of PERT/CPM: Optimizing Manufacturing Production Process Resources for Building Construction Projects on Kunjang River Region, East Kalimantan

Optimization of the FDM Processing Parameters on the Compressive Properties of ABS Objects for the Production of High-Heeled Shoes

The influence of 3D printing parameters on compressive properties is an important factor in the application of additive manufacturing processes for products subjected to compressive loads in use. In this study, the compressive strength and compressive modulus of acrylonitrile/butadiene/styrene (ABS) test specimens fabricated using the fused deposition modeling (FDM) process were investigated with the aim of producing products of high-heeled shoes for women. The experimental part of the study includes a central composite experimental design to optimize the main 3D printing parameters (layer thickness, infill density, and extrusion temperature) and the infill geometry (honeycomb and linear at a 45° angle—L45) to achieve maximum printing properties of the 3D-printed products. The results show that the infill density has the greatest influence on the printing properties, followed by the layer thickness and, finally, the extrusion temperature as the least influential factor. The linear infill at a 45° angle resulted in higher compressive strength and lower compressive modulus values compared to the honeycomb infill. By optimizing the results, the maximum compressive strength (that of L45 is 41 N/mm2 and that of honeycomb 35 N/mm2) and modulus (that of L45 is 918 N/mm2 and that of honeycomb is 868 N/mm2) for both types of infill is obtained at a layer thickness of 0.1 mm and infill density of 40%, while the temperature for L45 can be in the range of 209 °C to 254 °C, but for the honeycomb infill, the processing temperature is 255 °C. Additionally, the study highlights the potential for sustainable manufacturing practices and the integration of advanced 3D printing technologies to enhance the efficiency and eco-friendliness of the production process.

New limits proposed for the management of non-mutagenic impurities

Multiple international guidelines exist that describe both quality and safety considerations for the control of the broad spectrum of impurities inherent to drug substance and product manufacturing processes. However, regarding non-mutagenic impurities (NMI) the most relevant ICH Q3A/B guidelines are not applicable during early phases of drug development leading to confusion about acceptable limits at this stage. Thus, there is need for more flexible approaches that ensure that patient safety remains paramount, while taking into consideration the limited duration of exposure. An EFPIA survey, which collected quantitative data from different types of studies applied to qualify impurities in accordance with ICH Q3A, shows that no toxicities could be attributed to any of the 467 impurities at any tested level in vivo. This data combined with earlier published toxicological datasets encompassing drug substances and intermediates, food related substances and chemicals provide convincing evidence that for NMIs, the application of a generic 5 mg/day limit for an exposure duration <6 months, and a 1 mg/day generic limit for life-long exposure, provides sufficient margins to ensure patient safety. Hence, application of these absolute limits to trigger qualification studies (instead of the relative limits described in Q3A/B), is considered warranted. This approach will prevent conduct of unnecessary dedicated impurity qualification studies and the resulting use of animals.

Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process

AbstractAlthough the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments.

Product Quality Anomaly Recognition and Diagnosis Based on DRSN-SVM-SHAP

Conventional quality control methodologies are inadequate for fully elucidating the aberrant patterns of product quality. A multitude of factors influence product quality, yet the limited number of controlled quality characteristics is insufficient for accurately diagnosing quality abnormalities. Additionally, there are asymmetries in data collection, data pre-processing, and model interpretation. In this context, a quality anomaly recognition and diagnosis model for the complex product manufacturing process is constructed based on a deep residual network, support vector machine (SVM), and Shapley additive explanation (SHAP). Given the numerous complex product quality characteristic indexes and unpredictable accidental factors in the production process, it is necessary to mine the deep relationship between quality characteristic data and quality state. This mining is achieved by utilizing the strong feature extraction ability of the deep residual shrinkage network (DRSN) through self-learning. The symmetry of the data within the model has also been taken into account to ensure a more balanced and comprehensive analysis. The excellent binary classification ability of the support vector machine is combined with the DRSN to identify the quality anomaly state. The SHAP interpretable model is employed to diagnose the quality anomaly problem of a single product and to identify and diagnose quality anomalies in the manufacturing process of complex products. The effectiveness of the model is validated through case analysis. The accuracy of the DRSN-SVM quality anomaly recognition model reaches 99%, as demonstrated by example analysis, and the model exhibits faster convergence and significantly higher accuracy compared with the naive Bayesian model classification and support vector machine classification models.

Quantitative shear stress imaging in high throughput in-line Si wafer inspection

Defect inspection methods are basic steps in electronic chip manufacturing and power device production processes in the semiconductor industry. Based on stress induced photoelastic anisotropy, in-line polarized stress imaging (PSI) and quantitative determination of the local shear stress component are possible in single crystalline silicon wafers. Hereafter, such polarization stress images are compared and validated quantitatively with results of finite element simulations based on different 3D elastic models. Uniquely, high resolution at high throughputs is achieved for 300 mm wafer size in the present quantitative stress imaging study.

Иерархическая модель оптимизации технологических параметров комплекса рабочих переходов для процесса механической обработки

Optimization of the parameters of the product manufacturing process is one of the key tasks of technological preparation of production. The technological process of mechanical processing has a complex hierarchical struc-ture. The effectiveness of optimizing the manufacturing process of a product directly depends on the level of its detail and the optimal choice of targets and control parameters. In this case, the technological process of mechanical processing, as an object of control, can be described in the form of a hierarchical model. Thus, the task of optimizing the technological process of mechanical processing is to determine the optimal values of control parameters for each structural element (intermediate state of the control object) of the hierarchical control model. The aim of the work is to develop a hierarchical model for optimizing the parameters of a complex of working transitions for machining operations. The structure of the hierarchical model of the product manufacturing process on metal-cutting machines is described. Within the framework of the developed model, five control levels are identified, control parameters for individual structural elements of the model are defined, as well as the relationship between them. For the intermediate states of the control object (structural elements), a description of single and vector optimization criteria is presented. The practical implementation of the developed control model is presented using the example of optimizing the technological parameters for the “Bushing” part. The application of the developed control model will increase the efficiency of the technological process of manufacturing products on metal-cutting machines by optimizing technological parameters based on a multi-criteria analysis at the stage of technological preparation of production.

An enhanced EWMA chart with variable sampling interval scheme for monitoring the exponential process with estimated parameter

Control charts have been used to monitor product manufacturing processes for decades. The exponential distribution is commonly used to fit data in research related to healthcare and product lifetime. This study proposes an exponentially weighted moving average control chart with a variable sampling interval scheme to monitor the exponential process, denoted as a VSIEWMA-exp chart. The performance measures are investigated using the Markov chain method. In addition, an algorithm to obtain the optimal parameters of the model is proposed. We compared the proposed control chart with other competitors, and the results showed that our proposed method outperformed other competitors. Finally, an illustrative example with the data concerning urinary tract infections is presented.

Abstract 3210: Raptamer-drug conjugates as molecularly targeted cancer therapeutics

Abstract The use of aptamers as a therapeutic targeting moiety holds the promise of building on the success of antibody-drug conjugates (ADCs). While ADCs are an attractive modality, they face limitations, including limited penetration into solid tumors and a complicated production process for large-scale manufacturing. Aptamers have several potential advantages over antibodies as a targeting moiety including: (i) a smaller size that enables better tumor penetration; (ii) chemical synthesis which allows rapid scale-up and ease of incorporating modifications; (iii) a superior safety profile from lack of immunogenicity and (iv) room temperature stability. Therefore, the use of aptamers in conjunction with drugs (similar to ADCs) holds promise for development of targeted therapeutics. We have identified a novel therapeutic target for certain cancers (COF-01) using proteomics data and immunohistochemistry from patient-derived tumors. Both the COF-01 protein and related mRNA expression was shown to be significantly increased in non-small cell lung cancer, head and neck cancer and pancreatic cancer. Here, we present a new therapeutic approach that targets COF-01-positive cancer cells using next generation aptamer conjugates. We performed a cell- and protein-based selection using our proprietary bead-based combinatorial libraries to generate next-generation base-modified ssDNA aptamers (Raptamers) against COF-01. Binding affinities of the COF-01 Raptamer candidates were determined by concentration curves using biolayer interferometry. The dissociation constants for the COF-01 Raptamers ranged from 80 nM - 200 nM. COF-01-specific Raptamer-drug conjugates (Rap-DCs) were developed by conjugating the Raptamers to monomethyl auristatin E via a cleavable linker. In vitro efficacy data showed the COF-01 Rap-DC can selectively induce cytotoxicity in different types of COF-01-positive human cancer cells (A431, CAL 27, BxPC-3). There was a significant decrease in cell viability of COF-01-positive cells when compared to COF-01-negative control cells (IC50 values ranged from 5.19 nM - 14.11 nM). In vivo efficacy testing in a COF-01-postive cell-derived xenograft mouse model showed a significant decrease in tumor volume in animals treated with the COF-01 Rap-DC when compared to saline- and erlotinib-treated controls. This provides proof-of concept for the use of high-affinity Raptamers against novel molecular targets, such as COF-01. This platform can be used to develop Rap-DCs against emergent neo-antigens on cancer cells that have escaped chemotherapy. Citation Format: Stephanie P. Vega, Nancy E. Ward, Uksha Saini, Leniher Castan-Chibas, Garima Kaushik, Daniel Ciznadija, Dev Chatterjee, Michael J. Heffernan, Atul Varadhachary, Michael Ritchie. Raptamer-drug conjugates as molecularly targeted cancer therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3210.

Combining Active Learning and Self-Paced Learning for Cost-Effective Process Design Intents Extraction of Process Data

Abstract With the widespread use of computer-aided technologies like CAD/CAM/CAPP in the product manufacturing process, a large amount of process data is constantly generated, and data-driven process planning has shown promising potentials for effectively reusing the process knowledge. However, a lot of labeled data are needed to train a deep learning model for effectively extracting the embedded knowledge and experiences within these process data, and the labeling of process data is quite expensive and time-consuming. This paper proposes a cost-effective process design intents extraction approach for process data by combining active learning (AL) and self-paced learning (SPL). First, the process design intents inference model based on Bi-LSTM is generated by using a few pre-labeled samples. Then, the prediction uncertainty of each unlabeled sample is calculated by using a Bayesian neural network, which can assist in the identification of high confidence samples in SPL and low confidence samples in AL. Finally, the low confidence samples with manual-labels and the high confidence samples with pseudo-labels are incorporated into the training data for retraining the process design intents inference model iteratively until the model attains optimal performance. The experiments demonstrate that our approach can substantially decrease the number of labeled samples required for model training, and the design intents in the process data could be inferred effectively with dynamically undated training data.

Bioaerosol Emission from Vortex Liquids Contaminated with Escherichia coli Bacteria

Vortex fluids are often present in natural and artificial aquatic environments and are also widely used in industrial water treatment and product manufacturing processes. Vortex processes have been studied quite extensively; however, little attention has been paid to the potential release of biological aerosols to the ambient air in common situations involving microbial-contaminated vortex liquids. The model organism was Escherichia coli, a common Gram-negative coliform bacterium widely present in the aquatic and air environments. This study examines the influence of various parameters, including liquid rotation speed, column height, temperature, surface tension and vessel size, on the rate of bioaerosol formation. A commonly used single-stage bioaerosol impactor was employed to collect microbial aerosols at different process parameters under controlled laboratory conditions. The main results show that bioaerosol production increases markedly with increasing rotation speed, reaching a maximum rate at the highest value used in this project (1300 rpm). The tallness of the liquid column is strongly responsible for the bioaerosol production efficiency reaching a difference of almost one order of magnitude along the range between 45 mm (highest bioaerosol release) and 110 mm used in this research. Fluid temperature and surface tension are also very influential parameters responsible for bioaerosol generation during fluid vortex motion; corresponding results are discussed in this manuscript.

Particle-Based Artificial Antigen-Presenting Cell Systems for T Cell Activation in Adoptive T Cell Therapy.

T cell-based adoptive cell therapy (ACT) has emerged as a promising treatment for various diseases, particularly cancers. Unlike other immunotherapy modalities, ACT involves directly transferring engineered T cells into patients to eradicate diseased cells; hence, it necessitates methods for effectively activating and expanding T cells in vitro. Artificial antigen-presenting cells (aAPCs) have been widely developed based on biomaterials, particularly micro- and nanoparticles, and functionalized with T cell stimulatory antibodies to closely mimic the natural T cell-APC interactions. Due to their vast clinical utility, aAPCs have been employed as an off-the-shelf technology for T cell activation in FDA-approved ACTs, and the development of aAPCs is constantly advancing with the emergence of aAPCs with more sophisticated designs and additional functionalities. Here, we review the recent advancements in particle-based aAPCs for T cell activation in ACTs. Following a brief introduction, we first describe the manufacturing processes of ACT products. Next, the design and synthetic strategies for micro- and nanoparticle-based aAPCs are discussed separately to emphasize their features, advantages, and limitations. Then, the impact of design parameters of aAPCs, such as size, shape, ligand density/mobility, and stiffness, on their functionality and biomedical performance is explored to provide deeper insights into the design concepts and principles for more efficient and safer aAPCs. The review concludes by discussing current challenges and proposing future perspectives for the development of more advanced aAPCs.

Five-Dimensional Modeling Technology for Intelligent Manufacturing Lines Oriented Towards Digital Twins

Intelligent manufacturing lines constitute a complex system characterized by the integration of multiple technologies, the intersection of various disciplines, multi-dimensional integration, and the interaction of numerous components. Currently, the internal environment of intelligent manufacturing workshops, processing technologies, and production processes all exhibit a high degree of complexity, and the diversity of underlying mechanical equipment is particularly notable. Compared to traditional manufacturing assembly lines, the process coupling between equipment in flow-type production lines is tighter and more significant. Therefore, this paper undertakes digital twin modeling of intelligent manufacturing lines from five aspects: geometry, motion, data, communication, and control. This approach aims to achieve precise and detailed mapping of the physical entity production line's processing operations.

Modernizing non-classical protein crystallization through industry 4.0: Advanced monitoring and modelling utilizing process analytical technology

Modernizing manufacturing processes of pharmaceutical drug products with advanced monitoring and modelling can aid in the transition towards Industry 4.0 with the benefit of increased productivity. This study investigated the use of process analytical technology in combination with partial least squares (PLS) regressions to create two soft sensors to predict the mass fraction of crystallised active pharmaceutical ingredient (API) and mass fraction of dissolved API during a non-classical protein crystallization with amorphous precursors. The PLS model for predicting the amount of crystalline API was based on Raman spectra, chord length distributions and turbidity data using small-angle X-ray scattering as a reference method. The model had a root mean square error on cross-validation (RMSECV) of 5 %. The model predicting mass fraction of dissolved API was based only on the Raman spectra and used high performance liquid chromatography as reference method. This model had a RMSECV of 3 % A two-step nucleation model was fitted to the predictions from the sensors and showed good agreement between data and model with a root mean square error of 2 %.

Sustainable manufacturing production process monitoring and economic benefit analysis based on IoT technology

Sustainable manufacturing production process monitoring and economic benefit analysis based on IoT technology

Research on identification of key processes in machining process based on PageRank algorithm

Aiming at the difficulty of quantitatively evaluating the critical processes in the manufacturing process of complex mechanical products, a critical process identification method based on the PageRank algorithm is proposed with the goal of identifying key processes in the machining process. Based on the complex network theory, the error transfer network model of the machining process is established in this paper. Adopting the actual machining process as the data set of the complex network, the weights of the machining feature nodes are calculated by the PageRank ranking algorithm, and the nodes are ranked according to the weight values to assess the influence and importance of the nodes in the network model. Finally, taking the connecting rod machining process of a medium-speed marine diesel engine as an example, the results show that the method can quickly and effectively identify the key processes in the machining process.

Task incremental learning-driven Digital-Twin predictive modeling for customized metal forming product manufacturing process

Task incremental learning-driven Digital-Twin predictive modeling for customized metal forming product manufacturing process

Development trend and Challenge Analysis of Manufacturing Industry Under the Background of Artificial Intelligence

Artificial Intelligence (AI), as a new technology emerging in recent years, has received great attention in various fields. For the manufacturing industry, the ability to integrate artificial intelligence technology into the production process of manufacturing can improve the productivity of manufacturing enterprises. This paper discusses the development trend of the manufacturing industry in the context of artificial intelligence technology. It analyses several advantages of AI technology applied to the manufacturing industry, including innovation in technology, helping enterprises to achieve the optimization of human resources as well as improving the matching degree of the market. Based on the above advantages, the integration of artificial intelligence in the manufacturing industry can be further developed. At the same time, this paper also analyses the current integration of artificial intelligence technology problems and difficulties and puts forward suggestions. The results of this paper can be proved for the integration of artificial intelligence into the manufacturing industry, confirming that the integration of artificial intelligence plays a great role in the development of the manufacturing industry.

Developing a pharmaceutical fluid bed granulation & drying process via design of experiments based on a multivariate model

This manuscript presents a lean industrial workflow to establish an operating design space for a commercial scale fluid bed granulation and drying process. The workflow involves confirming the operating design space by demonstrating that the drug product critical quality attributes are met at the most extreme processing conditions within the granulation design space. This eliminates the need to execute a large experimental design, since any processing condition bound by the adequately demonstrated extreme conditions should also be acceptable, assuming linearity and continuity. A PLS (partial least squares) model was used to illustrate the multivariate relationship among the important process outputs and inputs (material properties and operating control variable settings). Since the moisture content of the fluidized bed influences granulation, the design space was evaluated based on the moisture accumulation and removal rates (output of the PLS models). The PLS model identified the specific experiments needed to confirm the operating design space. These specific experiments were executed, and the design space was confirmed experimentally. The PLS-model guided experimentation resulted in a lean experimental study while delivering a robust drug product manufacturing process.

Association rules combined fuzzy decision quality control technology in intelligent manufacturing

In the context of the rapid development of intelligent manufacturing, effective quality control has become the key to improving manufacturing efficiency and product quality. Traditional quality control methods are often inadequate when faced with complex production data and changing manufacturing environments. Therefore, exploring new intelligent quality control technologies to cope with these challenges in intelligent manufacturing has become an important research direction. In view of this, the study proposed a quality control technology that combines association rules and fuzzy decision-making. Firstly, association rule mining methods are used to analyze production data and extract the relationships between key quality factors. Secondly, based on these association rules, fuzzy decision technology is used to adjust and optimize the production process, ultimately achieving quality control of products in the intelligent manufacturing production process. The data showed that when running on the training set and validation set, the research method reached a stable state at 18 and 46 iterations of the system, respectively, with a minimum cost loss function value. In both batch production lines, the detection efficiency under the operation of the research method remained at 2200 units per minute. During the process of repeating the system for 6 times, the research method consistently achieved maximum control accuracy and minimum time consumption. The satisfaction of the four experts with the operation of the research method in small batch production and large batch production was 9.32 and 9.2, respectively, significantly higher than other algorithms. The above results indicated that the proposed method can effectively improve the efficiency of product quality control, reduce production costs, and ultimately reduce the rate of defective products in the production process.