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The manufacturing process for corrugated paper products is usually mass production and batch-by-batch production. Therefore, a high-speed mass production is required. Because the conveyer speed of the production line in a corrugated paper plant is very fast, unwanted material loss or even process interruption may occur during the cutting or clipping process if the corrugated paper on the conveyer is slanted. Hence, the paper alignment on a conveyer is important and necessary. In this study, we install photo-couples at both edges directly over the conveyer path to detect whether or not the corrugated paper is slanted from the number difference between non-activated photo-couples of the two edges. The deviated angle is calculated through a computer program using the detected information, and a servomotor-driven wheel aligns the paper in accordance with the calculated angle. This work reduces the material waste and promotes the production reliability and utility rate of the material for a corrugated paper plant. [ABSTRACT FROM AUTHOR]

Inductive contact force sensors, known for their high precision and anti-interference capabilities, hold significant potential applications in fields such as wearable and medical monitoring devices. Most of the current research on inductive contact force sensors employed novel nanomaterials as sensitive elements to enhance their sensitivity and other performance characteristics. However, sensors developed through such methods typically involve complex preparation processes, high costs, and difficulty in biodegradation, which limit their further development. This article introduces a new flexible inductive contact force sensor using paper as a sensitive element. Paper inherently possesses micro- and nanostructures on its surface and interior, enabling it to sensitively convert changes in contact force into changes in displacement, making it suitable for use as the sensor's sensitive element. Additionally, the advantages of paper also include its great flexibility, low cost, wide availability, and biodegradability. Performance testing on this flexible sensor showed good repeatability, hysteresis, sensitivity, and consistency. When used in experiments for monitoring human motion and respiration, this sensor also exhibited great detection performance. The proposed inductive paper-based flexible contact force sensor, with its simple structure, easy manufacturing process, cost-effectiveness, eco-friendliness, and good sensing performance, provides new insights into research for contact force sensors. [ABSTRACT FROM AUTHOR]

This study aims to characterise the so-called Sialkoti paper used to produce Pahari drawings preserved today in the Wereldmuseum in Leiden (Netherlands) (WML). These works originate from the Punjab Hills in India (today Himachal Pradesh) and are commonly known as 'Pahari miniature paintings'. The paper upon which these drawings are executed is said to have been produced in Sialkot during the eighteenth and nineteenth centuries when papermaking was an integral part of an overall regional economy correlated with other sectors such as agriculture and the textile industry. Although the term Sialkoti refers to identified papermills in Sialkot (now Pakistan), the paper from this region is yet to be subjected to a systematic study. This article therefore explores the paper(s) in question through macro- and microscopic observations of structure, pulp and fibres. The study of historical sources, complemented by the analysis of paper samples and fibre identification, has revealed that the pulp would have been prepared from a variety of manufactured products using different technologies, such as rags, cloth, mats, rope and other plant-based materials made from local bast fibres which, once tattered, were recycled to prepare the pulp. Microscopic study reveals that a variety of fibre combinations under different conditions and processing steps were used, in particular sunn hemp fibre (Crotalaria juncea L.), a plant species that has until now rarely been identified in historical paper. In addition, the presence of kenaf and jute fibres, as well as a significant quantity of cotton fibres, have also been demonstrated. To overcome the lack of comparative materials, our work was supported by a comparative study of several materials made from sunn hemp now held in the Ethnobotany Collection at Kew Gardens. Samples were taken from these materials and provided useful reference micrographs for the identification of the WML samples. The quality of the paper used by Pahari artists, as well as the raw materials available and their processing, are discussed in detail. Putting all the data, including technical and historical information into perspective, we conclude that the Pahari artists used a paper of inferior quality compared to other types of paper used for other purposes such as the creation of illuminated manuscripts in the Punjab plains. This study therefore provides useful analytical and material evidence that complements previous historical, technical and anthropological studies. [ABSTRACT FROM AUTHOR]

As increasing fiber hydrophobicity can significantly improve the paper dewatering process, we found that replacing SBKP and HBKP with 0.5% superhydrophobic CPGMA can significantly improve the dewatering of paper sheets. Therefore, it can be concluded that if CPGMA has little effect on paper properties, it will have potential industrial value in the papermaking industry. Consequently, it is necessary to further study the effect of the CPGMAs@CPAM/SiO2/APAM system on paper properties. To evaluate the application potential of the system in the papermaking industry, we investigated the effects of CPGMAs, which replaced the fibers in the stocks, on the paper properties in the CPAM/SiO2/APAM system. The findings demonstrate that as the CPGMA replacement increased, the paper's tensile strength, bursting strength, tear resistance, and folding endurance all declined. The trend can be segmented into two phases: a rapid decrease for substitution amounts below 0.5% and a gradual decline for substitution amounts exceeding 0.5%. When replaced with a small amount of CPGMAs, there was a negligible effect on these properties. Second, the paper air permeability increased with the CPGMA substitution amount in the stock. Furthermore, the trend of paper air permeability can be divided into two stages—a rapid stage with a substitution amount of <0.5% and a slow stage with a substitution amount of >0.5%. A small amount of CPGMAs could distinctly improve the paper's air permeability. Third, CPGMAs, which replaced fibers in the stock, minutely affected the paper formation. A small amount of CPGMAs substantially boosted the efficacy of the process of paper manufacture and certain characteristics of the paper, and it had a negligible impact on the strength of paper. The CPGMAs@CPAM/SiO2/APAM technology has the potential to improve the retention and filtration performance of CPAM/SiO2/APAM. [ABSTRACT FROM AUTHOR]

This study assesses the mechanical properties of mortars incorporating waste paper sludge-derived cellulose fibers. Compression and flexural tests were carried out on specimens prepared with cellulose fibers at different proportions, ranging from 0% to 2% of the total weight of the solid mortar constituents (cement, sand, and lime). In addition, a comparative analysis was carried out to evaluate the influence of the preparation method on the mechanical properties of the mortars. To this end, two series of mortars were studied: one prepared following a rigorous control of the preparation parameters and the other made without systematic parameter control to simulate typical on-site conditions. Finally, the applicability of both traditional and eco-friendly mortars in the construction of small-scale masonry walls was assessed through compression tests. Overall, the mechanical properties of mortars with cellulose fibers were comparable to those with 0% waste material, regardless of the production process. Regarding the compressive behavior of masonry walls, experimental tests showed significant similarities between specimens made with traditional and eco-friendly mortar. In conclusion, incorporating cellulose fibers into cement-based mortar shows considerable potential for building applications, enhancing the environmental benefits without compromising the mechanical behavior. [ABSTRACT FROM AUTHOR]

This article examines blue paper as an ecological solution in drawing practice by investigating the questions of when and under which circumstances German artists in the early sixteenth century decided to use blue paper. Blue paper had several aesthetic, symbolic, and economic functions, often both referencing a geographic relationship to Italy and its tradition of drawing on blue paper, and also engaging in a world of play and imitation in workshop practices. For a better understanding of the early modern paper ecologies within which artists worked, it is necessary to regard paper production within a broader socio-cultural range, thus not only as an exchange between paper makers but also with consideration of other crafts as well as the impact of new technologies. From there we can reconsider our relationship to nature. [ABSTRACT FROM AUTHOR]

Skin-based wearable devices have gained significant attention due to advancements in soft materials and thin-film technologies. Nevertheless, traditional wearable electronics often entail expensive and intricate manufacturing processes and rely on metal-based substrates that are susceptible to corrosion and lack flexibility. In response to these challenges, this paper has emerged with an alternative substrate for wearable electrodes due to its cost-effectiveness and scalability in manufacturing. Paper-based electrodes offer an attractive solution with their inherent properties of high breathability, flexibility, biocompatibility, and tunability. In this study, we introduce carbon nanotube-based paper composites (CPC) electrodes designed for the continuous detection of biopotential signals, such as electrooculography (EOG), electrocardiogram (ECG), and electroencephalogram (EEG). To prevent direct skin contact with carbon nanotubes, we apply various packaging materials, including polydimethylsiloxane (PDMS), Eco-flex, polyimide (PI), and polyurethane (PU). We conduct a comparative analysis of their signal-to-noise ratios in comparison to conventional gel electrodes. Our system demonstrates real-time biopotential monitoring for continuous health tracking, utilizing CPC in conjunction with a portable data acquisition system. The collected data are analyzed to provide accurate heart rates, respiratory rates, and heart rate variability metrics. Additionally, we explore the feasibility using CPC for sleep monitoring by collecting EEG signals. [ABSTRACT FROM AUTHOR]

The potential of paper and paperboard as fiber-based materials capable of replacing conventional polymer-based materials has been widely investigated and evaluated. Due to paper's limited extensibility and inherent heterogeneity, local structural variations lead to unpredictable local mechanical behavior and instability during processing, such as mechanical forming. To gain a deeper understanding of the impact of mechanical behavior and heterogeneity on the paper forming process, the Finite Element Method (FEM) coupled with continuum modeling is being explored as a potential approach to enhance comprehension. To achieve this goal, utilizing experimentally derived material parameters alongside stochastic finite element methods allows for more precise modeling of material behavior, considering the local material properties. This work first introduces the approach of modeling heterogeneity or local material structure within continuum models, such as the Stochastic Finite Element Method (SFEM). A fundamental challenge lies in accurately measuring these local material properties. Experimental investigations are being conducted to numerically simulate mechanical behavior. An overview is provided of experimental methods for material characterization, as found in literature, with a specific focus on measuring local mechanical material structure. By doing so, it enables the characterization of the global material structure and mechanical behavior of paper and paperboard. [ABSTRACT FROM AUTHOR]

Within the domain of starch modification, the study delved into cationization of wheat starch through a laboratory-scale twin-screw extruder, exploring various processing conditions. Cationic starch, a crucial component for enhancing paper attributes like dry strength and printability, took center stage. The focus shifted towards integration into papermaking, investigating the transformative potential of reactive extrusion. By contrasting it with conventional dry-process methodology, innovative strides were unveiled. The study extended to pilot-scale extrusion, bridging the gap between laboratory experimentation and potential industrial implementation. Infused with scientific rigor, the investigation navigated the benefits brought about by reactive extrusion. Empirical insights highlighted a significant reduction in the intrinsic viscosity of extruded starch, decreasing from 170 mL·g−1 (native starch) to 100 mL·g−1 at a specific mechanical energy (SME) input of 800 kWh·t−1, demonstrating remarkable stability despite increased mechanical treatment. Moreover, beyond the critical threshold of 220 kWh·t−1, retention efficiency reached a stable plateau at 78%. The study revealed that utilizing a larger extruder slightly improved the mechanical properties of the paper, emphasizing the advantage of scaling up the production process and the consistency of results across different extruder sizes. [ABSTRACT FROM AUTHOR]

Over the past ten years, microfluidic paper-based analytical devices (micro-PADs) have attracted a lot of attention as a viable analytical platform. It is expanding as a result of advances in manufacturing processes and device integration. Conventional microfluidics approaches have some drawbacks, including high costs, lengthy evaluation times, complicated fabrication, and the necessity of experienced employees. Hence, it is extremely important to construct a detection system that is quick, affordable, portable, and efficient. Nowadays, micro-PADs are frequently employed, particularly in electrochemical analyses, to replicate the classic standard laboratory experiments on a miniature paper chip. It has benefits like rapid assessment, small sample consumption, quick reaction, accuracy, and multiplex function. The goal of this review is to examine modern paper microfluidics-based electrochemical sensing devices for the detection of macromolecules, small molecules, and cells in a variety of real samples. The design and fabrication of micro-PADs using conventional and the latest techniques have also been discussed in detail. Lastly, the limitations and potential of these analytical platforms are examined in order to shed light on future research. [ABSTRACT FROM AUTHOR]

Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry's processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification. [ABSTRACT FROM AUTHOR]

This paper examines the impact of the CO2 laser parameters on the tensile strength, which is one of the most important properties of paper packaging in the process of cutting paper material. The study was performed on a paper material sample Fbb Board/Ningbo Spark C1S Ivory Board by examination of the influence of four independent variables: paper material grammage, cutting speed, laser power, and resolution on the tensile strength by using definitive screening design. Optimum process conditions of four variables that maximize the tensile strength were predicted and validated accordingly. Results confirm that laser power, paper material grammage, and cutting speed are the main process parameters that mostly affect the tensile strength. Besides individual parameters, two statistically significant interactions were obtained: laser power and cutting speed, and cutting speed and laser resolution. Maximum tensile strength values (20.37 N/mm) were achieved using the laser power of 60.6%, cutting speed of 3.24%, resolution of 2500 Hz, and a paper material grammage of 326.85 g/m2. With laser power at middle values and at a lower speed, a maximum tensile strength value can be obtained. Increasing the laser power and cutting speed will produce a slight lowering of tensile strength. [ABSTRACT FROM AUTHOR]

Cellulose-based foams present a high potential for noise insulation applications. These materials are bio-degradable, eco-friendly by both embedded components and manufacturing process, have low density and high porosity, and are able to provide good noise insulation characteristics compared with available petroleum-based foams currently used on a large scale. This paper presents the results of some investigations performed by the authors in order to improve the functional characteristics in terms of free surface wettability and structural integrity. Native xylan and xylan-based derivatives (in terms of acetylated and hydrophobized xylan) were taken into account for surface treatment of cellulose foams, suggesting that hemicelluloses represent by-products of pulp and paper industry, and xylan polysaccharides are the most abundant hemicelluloses type. The investigations were mainly conducted in order to evaluate the level to which surface treatments have affected the noise insulation properties of basic cellulose foams. The results indicate that surface treatments with xylan derivatives have slowly affected the soundproofing characteristics of foams, but these clearly have to be taken into account because of their high decrease in wettability level and improving structural integrity. [ABSTRACT FROM AUTHOR]

The article presents studies on the impact of the significant process parameters of a paper machine with a Yankee cylinder on its production capacity and heat energy consumption for drying the paper web. Parameters such as machine speed, web moisture content before and after pressing, parameters of steam supplied to the cylinder and parameters of hot air flowing from the nozzles of the hood were analyzed. The study's results were used to optimize production to improve the energy efficiency and performance of the machine. In order to use the possible methods of improving the production capacity and heat energy consumption, the parameters of the production process were measured and the basic indicators characterizing the operation of the machine were calculated in the Yankee cylinder–dryer hood system. The correct functioning of the machine components and the possibility of their modernization were also analyzed. Technological and construction changes introduced based on the research results made it possible to increase the production capacity by 10% and to reduce the consumption of heat energy per 1 ton of produced paper by 16.3%. The article presents a description of changes in the technology of paper production and modernization of the tissue machine made in the years 2013–2022. [ABSTRACT FROM AUTHOR]

Anomaly detection of three-dimensional (3D) topographic data is a challenging problem in spatial data analysis. In this paper, we investigate spatial patterns of 3D surface data that exhibit multiple in-control modes. In complex manufacturing processes, surfaces of final products could contain different topographic features from one in-control surface to another, thus making it difficult to monitor the surface with existing approaches, which rely on the assumption of the presence of single mode surface topography. We propose a novel anomaly detection approach for monitoring local topographic variations in the presence of multimode surface topography. We present a binarization model to capture the generic behavior of the multimode surfaces and enhance the representation of the surface. To systematically monitor the surface, we introduce a new probabilistic distance measure (PDM) that quantifies the similarity of spatial patterns between two binarized surfaces. The proposed PDM takes advantage of identifying local variations by utilizing the order neighbor statistics, which captures the local property on the surface. Experimental results with numerical simulation data and real-life paper surface data are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection. [ABSTRACT FROM AUTHOR]

This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]

The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Featured Application: This paper is intended to provide future researchers and users with an overview of concepts and modeling approaches for process-oriented tolerance and variation management. This should facilitate the identification and development of appropriate concepts. In the context of tolerance management, the consideration of manufacturing and assembly processes is becoming increasingly important. The main drivers for this are, above all, short development times and high-quality requirements, leading to tight tolerances. To overcome the resulting challenges, many publications address the process-oriented tolerance management. However, since multiple terms and definitions for describing activities that link tolerance management with the production process exist, it is hard to obtain a comprehensive overview on the topic. Therefore, this paper presents a review of existing approaches. The aim is to identify similarities and differences of existing approaches and present them with the help of a classification. For this purpose, among others, work from the areas of process-oriented tolerance management, stream of variation, state space modeling, and variation propagation in multistation manufacturing and assembly systems is considered. Based on the definition of the summarizing term "process-oriented tolerance and variation management", a classification of this thematic area will be introduced. [ABSTRACT FROM AUTHOR]

Civil aircrafts are highly complex systems. During the manufacturing process, deviations can occur due to cumulative errors in installation, system control, and other factors. These deviations often lead to the phenomenon of aircraft "runaway" during ground taxiing, taking off, and landing. Corrective maneuvers to address this issue not only increase the pilot's workload but also heighten the risk of aircraft deviation from the runway. Therefore, accurately testing and aligning the side deviation angle of an aircraft is crucial for ensuring safe operations. In this paper, we first construct a mathematical model of aircraft dynamics and derive a simplified mathematical model specifically designed for aircraft trimming tests. Next, a ground taxiing trimming test is conducted to verify the accuracy of this simplified model. Additionally, we investigate the crosswind factor, which has the greatest impact on side deviation measurements, to establish the relationship between the crosswind factor and the nose wheel trimming angle. Based on this, we innovatively propose a method for achieving aircraft trimming through the equivalent trimming angle of the nose wheel. Ultimately, this approach allows aircraft trimming to be achieved with a single taxiing side deviation test, which will reduce the cost of flight tests and normal operation of the airplane when finding taxiing deviation. The method innovatively proposed in this paper offers an efficient and accurate solution for aircraft trimming tests and adjustments, significantly reducing the cost of such tests and improving the safety of civil aircraft. [ABSTRACT FROM AUTHOR]

Virtual commissioning (VC), defined as the simulation and testing of systems in a virtual environment before physical implementation, plays a key role in addressing the challenges of integrating and validating complex systems efficiently and effectively. This paper focuses on the topic of virtual commissioning, summarizing and organizing existing research in the field. The paper provides a comprehensive overview of various design methods and technologies currently in use. A case study of virtual commissioning is also presented within the area of the Cyber-Physical Manufacturing Systems Laboratory of the Széchenyi István University, detailing the solution steps taken. Drawing on both research and practical experience, the paper proposes a novel framework to support virtual commissioning design, referred to as the "Virtual Commissioning House" (VCH). The methodology is evaluated through comparisons with existing virtual commissioning solutions, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

The document titled "Nanotechnologies and Nanomaterials: Selected Papers from CCMR" provides an overview of the applications and development of nanomaterial technology. It explores various methods of synthesizing nanomaterials and discusses their use in fields such as the Internet of Things, energy conversion, intelligent sensing, and biomimetics. The document also covers specific topics related to nanomaterials, including their synthesis, characterization, and applications in areas such as energy storage, electronics, and catalysis. Researchers and library patrons interested in nanomaterials and their applications will find this collection of articles useful for their research. [Extracted from the article]

The quest for eco‐friendly materials with anticipated positive impact for sustainability is crucial to achieve the UN sustainable development goals. Classical strategies of composite materials can be applied on novel nanomaterials and green materials. Besides the actual technology and applications also processing and manufacturing methods should be further advanced to make entire technology concepts sustainable. Here, they show an efficient way to combine two low‐cost materials, cellulose and zinc oxide (ZnO), to achieve novel functional and "green" materials via paper‐making processes. While cellulose is the most abundant and cost‐effective organic material extractable from nature. ZnO is cheap and known of its photocatalytic, antibacterial, and UV absorption properties. ZnO nanowires are grown directly onto cellulose fibers in water solutions and then dewatered in a process mimicking existing steps of large‐scale papermaking technology. The ZnO NW paper exhibits excellent photo‐conducting properties under simulated sunlight with good ON/OFF switching and long‐term stability (90 minutes). It also acts as an efficient photocatalyst for hydrogen peroxide (H2O2) generation (5.7 × 10−9 m s−1) with an envision the possibility of using it in buildings to enable large surfaces to spontaneously produce H2O2 at its outer surface. Such technology promise for fast degradation of microorganisms to suppress the spreading of diseases. [ABSTRACT FROM AUTHOR]

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Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development. [ABSTRACT FROM AUTHOR]

In the tobacco industry, impurity detection is an important prerequisite for ensuring the quality of tobacco. However, in the actual production process, the complex background environment and the variability of impurity shapes can affect the accuracy of impurity detection by tobacco robots, which leads to a decrease in product quality and an increase in health risks. To address this problem, we propose a new online detection method of tobacco impurities for tobacco robot. Firstly, a BCFormer attention mechanism module is designed to effectively mitigate the interference of irrelevant information in the image and improve the network's ability to identify regions of interest. Secondly, a Dual Feature Aggregation (DFA) module is designed and added to Neck to improve the accuracy of tobacco impurities detection by augmenting the fused feature maps with deep semantic and surface location data. Finally, to address the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes, this paper proposes an optimized loss function to more accurately assess the quality of the bounding boxes. To evaluate the effectiveness of the algorithm, this paper creates a dataset specifically designed to detect tobacco impurities. Experimental results show that the algorithm performs well in identifying tobacco impurities. Our algorithm improved the mAP value by about 3.01% compared to the traditional YOLOX method. The real-time processing efficiency of the model is as high as 41 frames per second, which makes it ideal for automated inspection of tobacco production lines and effectively solves the problem of tobacco impurity detection. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements. Methods: Informed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications. Results: We propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages. Conclusion: In this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites. [ABSTRACT FROM AUTHOR]

C4F7N/CO2 gas mixture, as the main new environmentally friendly gas insulation medium, is now being widely used in the ring network switchgear and other gas electrical equipment. In this paper, a test platform was constructed to simulate partial discharge in a gas medium, the decomposition characteristics of the C4F7N/CO2 gas mixture during suspended potential discharge were studied under four different metal electrode materials: stainless steel, purple copper, tungsten copper, and aluminum alloy. The results show that the main decomposition products of the C4F7N/CO2 gas mixture during suspended discharge are CO, CF4, C4F8, C3F8, C3F6, CF3CN, and C2F4 gases, which are independent of the metal material. On the whole, when the metal electrode material is stainless steel, the highest amount of decomposition products are generated from the C4F7N/CO2 gas mixture; when the metal electrode material is aluminum alloy, the amount of decomposition products produced by C4F7N/CO2 gas mixture is the least, and the content difference of some decomposition products between these two metal materials is as high as 70%. The decomposition products of the C4F7N/CO2 gas mixture decreased gradually with the increase in the gas pressure. Finally, the influence mechanism of different metal materials on the decomposition process of the C4F7N/CO2 gas mixture was analyzed from the microscopic perspectives of charge transfer between gas molecules and molecular structural characteristics. In general, the research results can provide technical reference for the design, selection, and optimization of environmental protection gas insulation equipment. [ABSTRACT FROM AUTHOR]

Metal matrix composites (MMCs) epitomize a promising class of resources in modern manufacturing, offering an enhanced strength-to-weight ratio and high-temperature performance which make them ideal for applications demanding over conventional metals. However, their machining presents significant challenges due to their inherent material properties. The conventional machining methods including turning, milling, drilling, shaping, and the grinding of MMCs pose several challenges, facing limitations in terms of sustainability and efficiency. This paper explores the current perspective and prospects of the conventional machining techniques applied to MMCs, emphasizing sustainable manufacturing practices. Key aspects include the challenges posed by MMCs' inherent heterogeneity, the MMC materials used, the MMC manufacturing process, the cutting constraints employed, tool wear, surface unevenness, surface integrity, and high energy consumption throughout machining. The study also explores promising advancements in tooling materials, cutting parameters' optimization, innovative machining techniques aimed at minimizing the environmental impact and maximizing material utilization, and the strategies developed to overcome these challenges. The paper concludes by highlighting optimizing tools, and processes, and adopting emerging optimization techniques and opportunities for further research aimed at the industry, allowing it to move towards more efficient, eco-friendly production methods. [ABSTRACT FROM AUTHOR]

This paper is devoted to Petri net (PN)-based models of automated manufacturing systems (AMSs) with resources in order to prevent deadlocks in them. Their sustainability can be seen as the result of their deadlock freeness, leading to correct and fluent production, because AMSs with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. The paradigm of such PN models, S3PRs (systems of simple sequential processes with resources), is well known from the deadlock prevention point of view. Here, an extended S3PR (ES3PR) will be explored, with respect to its modelling and deadlock prevention. While in the case of S3PRs, ordinary Petri nets (OPNs) are used for these aims, here, for ES3PRs, generalized Petri nets (GPNs) are used. The reason for such a procedure is the possible presence of multiplex-directed arcs in the structure of PN models of AMSs. The significant alternation is that while, in the former case, the elementary siphons and dependent ones are sufficient for supervisor synthesis, here, in the later case, the GPNs and their siphons have to satisfy the max cs property. [ABSTRACT FROM AUTHOR]

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts. [ABSTRACT FROM AUTHOR]

In oxygen production on plateaus, pressure swing adsorption (PSA) oxygen production is currently the most commonly used oxygen production method. In plateau regions, low pressure leads to a decrease in adsorbent nitrogen–oxygen separation performance, which affects the performance of PSA oxygen production, so it is particularly important to enhance adsorbent nitrogen–oxygen separation performance. In this paper, Li-LSX (lithium low-silicon aluminum X zeolite molecular sieve) adsorbents were modified using the liquid phase ion exchange method, and five kinds of modified adsorbents were obtained, namely AgLi-LSX, CaLi-LSX, ZnLi-LSX, CuLi-LSX, and FeLi-LSX, respectively. The influences of different metal ions and modification time lengths on the adsorbent nitrogen adsorption and nitrogen–oxygen separation coefficients were analyzed. Through theoretical calculations, the nitrogen and oxygen adsorption and separation performances of the modified adsorbents at different altitudes and low adsorption pressures were investigated. It is shown that the nitrogen adsorption capacity of the AgLi-LSX-1 adsorbent obtained from the modification experiment reaches 27.92 mL/g, which is 3.24 mL/g higher than that of Li-LSX; the nitrogen–oxygen separation coefficients of S1 and S2 are 19.24 and 7.54 higher, respectively; and the nitrogen–oxygen separation coefficients of S4 are 20.85 and 7.54 higher than those of Li-LSX, respectively. With the increase in altitude from 50 m to 5000 m, the nitrogen–oxygen separation coefficient of the AgLi-LSX-1 adsorbent increased rapidly from 20.85 to 57, and its nitrogen–oxygen separation coefficient S4 exceeded that of the Li-LSX adsorbent to reach 47.61 at an altitude of 4000 m. Therefore, the modified adsorbent AgLi-LSX-1 in this paper can enhance the performance of the PSA oxygen process for oxygen production in plateau applications. [ABSTRACT FROM AUTHOR]

This paper investigates hydrogen's potential to accelerate the energy transition in hard-to-abate sectors, such as steel, petrochemicals, glass, cement, and paper. The goal is to assess how hydrogen, produced from renewable sources, can foster both industrial decarbonization and the expansion of renewable energy installations, especially solar and wind. Hydrogen's dual role as a fuel and a chemical agent for process innovation is explored, with a focus on its ability to enhance energy efficiency and reduce CO2 emissions. Integrating hydrogen with continuous industrial processes minimizes the need for energy storage, making it a more efficient solution. Advances in electrolysis, achieving efficiencies up to 60%, and storage methods, consuming about 10% of stored energy for compression, are discussed. Specifically, in the steel sector, hydrogen can replace carbon as a reductant in the direct reduced iron (DRI) process, which accounts for around 7% of global steel production. A next-generation DRI plant producing one million tons of steel annually would require approximately 3200 MW of photovoltaic capacity to integrate hydrogen effectively. This study also discusses hydrogen's role as a co-fuel in steel furnaces. Quantitative analyses show that to support typical industrial plants, hydrogen facilities of several hundred to a few thousand MW are necessary. "Virtual" power plants integrating with both the electrical grid and energy-intensive systems are proposed highlighting hydrogen's critical role in industrial decarbonization and renewable energy growth. [ABSTRACT FROM AUTHOR]

This paper explores the role that 5G, WiFi 7, and Time-Sensitive Networking (TSN) play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. It provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, this paper includes a comparative study of these technologies, analyzing them based on several industrial use cases, supported secondary applications, industry adoption, and current market trends. This paper concludes by highlighting the challenges and future directions for adopting these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing. [ABSTRACT FROM AUTHOR]

Due to their excellent light transmission, heat resistance, corrosion resistance, high mechanical strength, and other characteristics, transparent materials have been widely used in emerging industries such as aviation, aerospace, microelectronics, interconnected communication, etc. Compared with the traditional mechanical processing and chemical processing of transparent materials, laser processing, with such characteristics as a high peak power, high energy density, and non-contact processing, has a lot of obvious advantages in processing efficiency and accuracy. In this paper, some of the recent research advancements concerning the laser processing of transparent materials are introduced in detail. Firstly, the basic mechanism of the interaction between the laser and material is briefly summarized on the time scale. The differences in principle between nanosecond, picosecond, and femtosecond pulse laser processing are analyzed. Then, the main technical means of the nanosecond laser processing of transparent materials are summarized. Next, the main application directions of the ultrafast laser processing of transparent materials are discussed, including the preparation of optical waveguide devices, periodic structure devices, micropores, and microchannels. Finally, this paper summarizes the prospects for the future development of laser processing transparent materials. [ABSTRACT FROM AUTHOR]

The out-of-control action plan (OCAP) is crucial in the wafer probing process of semiconductor manufacturing as it systematically addresses and corrects deviations, ensuring the high quality and reliability of semiconductor devices. However, the traditional OCAP involves many redundant and complicated processes after failures occur on production lines, which can delay production and escalate costs. To overcome the traditional OCAP's limitations, this paper proposes a novel OCAP aimed at enhancing the wafer probing process in semiconductor manufacturing. The proposed OCAP integrates proactive measures such as preventive maintenance and advanced monitoring technologies, which are tested and verified through a comprehensive experimental setup. Implementing the novel OCAP in a case company's production line reduced machine downtime by over 24 h per week and increased wafer production by about 23 wafers per week. Additionally, probe test yield improved by an average of 1.1%, demonstrating the effectiveness of the proposed method. This paper not only explores the implementation of the novel OCAP but also compares it with the traditional OCAP, highlighting significant improvements in efficiency and production output. The results underscore the potential of advanced OCAP to enhance manufacturing processes by reducing dependency on human judgment, thus lowering the likelihood of errors and improving overall equipment effectiveness (OEE). [ABSTRACT FROM AUTHOR]

Featured Application: A possible application of the machine learning-based data imputation framework presented in this paper is deployment in condition-based maintenance (CBM) systems for advanced manufacturing organizations. Missing data is a commonly encountered issue in the smart sensor networks that enable CBM, and our adaptive imputation framework could be beneficial to addressing this problem. In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue. [ABSTRACT FROM AUTHOR]

Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples. [ABSTRACT FROM AUTHOR]

During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers' average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, production scheduling as a critical part of manufacturing system should be smarter. Smart scheduling agent is required to be real-time autonomous and possess the ability to face unforeseen and disruptive events. However, traditional methods lack adaptability and intelligence. Hence, this paper is devoted to proposing a smart approach based on proximal policy optimization (PPO) to solve dynamic job shop scheduling problem with random job arrivals. The PPO scheduling agent is trained based on an integration framework of discrete event simulation and deep reinforcement learning. Copies of trained agent can be linked with each machine for distributed control. Meanwhile, state features, actions and rewards are designed for scheduling at each decision point. Reward scaling are applied to improve the convergence performance. The numerical experiments are conducted on cases with different production configurations. The results show that PPO method can realize on-line decision making and provide better solution than dispatch rules and heuristics. It can achieve a balance between time and quality. Moreover, the trained model could also maintain certain performance even in untrained scenarios. [ABSTRACT FROM AUTHOR]

Rare events are occurrences that take place with a significantly lower frequency than more common, regular events. These events can be categorized into distinct categories, from frequently rare to extremely rare, based on factors like the distribution of data and significant differences in rarity levels. In manufacturing domains, predicting such events is particularly important, as they lead to unplanned downtime, a shortening of equipment lifespans, and high energy consumption. Usually, the rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events affects the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. This paper evaluates the role of data enrichment techniques combined with supervised machine learning techniques for rare event detection and prediction. We use time series data augmentation and sampling to address the data scarcity, maintaining its patterns, and imputation techniques to handle null values. Evaluating 15 learning models, we find that data enrichment improves the F1 measure by up to 48% in rare event detection and prediction. Our empirical and ablation experiments provide novel insights, and we also investigate model interpretability. [ABSTRACT FROM AUTHOR]

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal "Processes" focused on micro/nano manufacturing processes and optimization techniques. The articles in this issue cover a range of topics including the fabrication of micro/nano-structured surfaces, simulation and experimental investigation of laser manufacturing, and optimization techniques in microscale machining. The articles provide insights into various methods and technologies used in these areas and discuss their advantages, disadvantages, and potential applications. The authors also propose new approaches and techniques to address challenges and improve performance in micro/nano manufacturing processes. [Extracted from the article]

The work presents an experimental evaluation of the bonding strength of materials commonly used in printing industry, using double-sided adhesive tape and thermal adhesives. The study revealed changes in adhesive strength when bonding various types and structures of paper and cardboard surfaces, as well as laminated glossy and matte surfaces. The experimental study aims to identify surfaces that exhibit optimal adhesive tape adhesion. Moreover, there are also surfaces that should not be used with double-sided tape, but instead should be glued with thermal adhesives. In addition, the study examines the effect of different separation rates on the bond strength of these materials. The results provide valuable information about the adhesive properties of materials in the printing industry, providing useful information for professionals to optimize printing processes and ensure the production of high-quality and durable printed products. The data presented in this article can assist in selecting appropriate adhesives for a variety of printing applications, which will achieve consistent and reliable results and can greatly affect their quality and durability. [ABSTRACT FROM AUTHOR]

The fluidisation of spherocylindrical particles is often involved in the fields of biomass combustion, drug delivery and food drying. By spherocylindrical particles, we mean elongated spherical particles like non-spherical capsule pills and pulverised biomass fuels. Mastering the fluidisation state of spherocylindrical particles can improve the related industrial processes. In this paper, a coupled CFD-DEM method is used and the reliability of the model in this paper is verified with the help of high-speed photography experiments. Subsequently, the fluidisation characteristics of spherocylindrical particles in a spouted bed were investigated under different operating conditions and compared with the simulation results of spherical particles. The results show that the gas velocity influences the average bed height, axial velocity, volume fraction, orientation angle, and mixing mass of the particles. There are also significant differences in the fluidisation characteristics of particles at different bed heights. The particles in the higher bed region are more dispersed, the gas-solid mixing is more intense, and the gas velocity has a strong influence on the volume fraction/axial velocity of the spherocylindrical particles in the higher bed. In addition, this work also found significant differences in the fluidisation characteristics of different shaped particles. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal Machines titled "Advances in Computer-Aided Technology II." The special issue focuses on computer-aided technologies (CAx) and their integration into the design, analysis, and production of products. The issue features eight papers covering topics such as additive manufacturing, neural networks and IoT in manufacturing, production system simulations, robotics, advanced finite element analysis (FEA) systems, material engineering, digitization, and virtual and augmented reality. The papers discuss advancements in these areas and their industrial applications. The editorial expresses gratitude to the authors and reviewers for their contributions to the special issue. [Extracted from the article]

Billions of costless near‐field communication (NFC) sensor labels per day are demanded to practically enable edge computing between smartphones and everyday objects. However, to activate the billions of NFC sensor labels daily, providing an inexpensive manufacturing method for billions of wireless energy‐harvesting labels (WeHL) per day will become a decisive issue for realizing the practical applications. Herein, a roll‐to‐roll (R2R) gravure, a typical high‐throughput additive manufacturing method, is explored to print WeHLs where six diodes and six capacitors are integrated. To meet the high‐throughput manufacturing speed (90 mm s−1) of the R2R gravure system, six different electronic inks are formulated to print the WeHLs to harvest ±10 V from the smartphone's NFC carrier. To attain a practical device yield under the given printing speed, the web tension, nip force, doctor blade angle, and overlay printing registration accuracy are well controlled and optimized to print six different layers within a high overlay printing accuracy, while printing patterns to connect two electrodes with a height difference greater than 3 μm. The fully R2R‐printed WeHLs can successfully harvest energy from the smartphone's NFC carrier with the conversion efficiency of 50%. [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]