Production Line Research Articles

Green credit and firms’ span of global production stages

This paper investigates the impact of the green credit policy on firms' span of global production stages. We leverage a comprehensive dataset, including customs trade data, industrial firm data, and Chinese Input-Output tables, and employ a difference-in-differences design that accounts for the pollution spillover effect within the production network. Our findings indicate that firms in pollution-intensive industries tend to move downstream in the global production line, accompanied by an expansion in the span of production stages. We present evidence that the broader range of production stages can be attributed to increased firm productivity. The effect is more pronounced for hybrid processing firms, private firms, foreign firms, and those in industries that rely on external finance dependence. Our study provides valuable insights into the impact of green credit policies and sheds light on firms' strategies in the context of green development.

Efficient Busbar Slip Defects Detection in Photovoltaic Cell Electroluminescence Images

PV panel quality control is crucial for their efficient and long-lasting operation. Detecting defects in PV panels during production is essential. Electroluminescence imaging is a commonly used method for fault detection in PV panels. This study focuses on detecting busbar slippage, a specific PV panel malfunction. Automatic error detection was researched using machine learning methods on a dataset of 500 EL images taken from the production line. Feature extraction was performed using two pre-trained deep learning architectures: ResNet and SqueezeNet. Additionally, the study aimed to observe the impact of combining features from different deep learning architectures on success parameters. The highest accuracy rate of 0.9920 was achieved using deep features extracted by Relu34 and Relu25+Conv10 layers.

Applying optical coherence tomography to inline quality monitoring of unidirectional glass-fiber-reinforced thermoplastic tapes

Originally developed for biomedical applications and diagnosis, optical coherence tomography (OCT) has recently been demonstrated to be a powerful non-destructive and non-invasive measurement method for detecting defects in glass-fiber reinforced polymer composites. While previous studies have focused mainly on the use of OCT in the analysis of thermoset composites, we were able to show in offline experiments that OCT can be used to quickly detect typical defects (e.g., dry fiber regions, gaps and fiber breakage) in thermoplastic unidirectional (UD) tapes at high resolution. To investigate the applicability of OCT to inline monitoring, we advanced our previously published approach in two major steps: First, we incorporated the OCT system into an industrial-scale UD-tape production line, and derived optimal settings for inline detection of dry region defects from a comprehensive design of experiments (DoE) to find an optimal balance between accuracy and data size for a stationary tape sample by varying A-scan sampling rate, A-scan averaging and OCT transverse travel velocity. Second, using these optimal settings, we went on to investigate moving tapes over a range of industrially relevant take-off speeds. Microscopy was used for validation in both cases. We developed a fast and robust statistical analysis of B-scans that visualizes the quality of full cross-sections in an interpretable manner for potential use in a real-time setting. Within an industrially relevant production speed range of up to 15 m/min, we are thus now able to investigate 120 mm wide (and potentially wider) UD tapes inline at a transverse resolution of 22 µm, producing only 21 MB of data per measurement.

A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8

During the drying and processing of raisins, the presence of foreign matter such as fruit stems, branches, stones, and plastics is a common issue. To address this, we propose an enhanced real-time detection approach leveraging an improved YOLOv8 model. This novel method integrates the multi-head self-attention mechanism (MHSA) from BoTNet into YOLOv8’s backbone. In the model’s neck layer, selected C2f modules have been strategically replaced with RFAConv modules. The model also adopts an EIoU loss function in place of the original CIoU. Our experiments reveal that the refined YOLOv8 boasts a precision of 94.5%, a recall rate of 89.9%, and an F1-score of 0.921, with a mAP reaching 96.2% at the 0.5 IoU threshold and 81.5% across the 0.5–0.95 IoU range. For this model, comprising 13,177,692 parameters, the average time required for detecting each image on a GPU is 7.8 milliseconds. In contrast to several prevalent models of today, our enhanced model excels in mAP0.5 and demonstrates superiority in F1-score, parameter economy, computational efficiency, and speed. This study conclusively validates the capability of our improved YOLOv8 model to execute real-time foreign object detection on raisin production lines with high efficacy.

On-Line Detection Method of Salted Egg Yolks with Impurities Based on Improved YOLOv7 Combined with DeepSORT.

Salted duck egg yolk, a key ingredient in various specialty foods in China, frequently contains broken eggshell fragments embedded in the yolk due to high-speed shell-breaking processes, which pose significant food safety risks. This paper presents an online detection method, YOLOv7-SEY-DeepSORT (salted egg yolk, SEY), designed to integrate an enhanced YOLOv7 with DeepSORT for real-time and accurate identification of salted egg yolks with impurities on production lines. The proposed method utilizes YOLOv7 as the core network, incorporating multiple Coordinate Attention (CA) modules in its Neck section to enhance the extraction of subtle eggshell impurities. To address the impact of imbalanced sample proportions on detection accuracy, the Focal-EIoU loss function is employed, adaptively adjusting bounding box loss values to ensure precise localization of yolks with impurities in images. The backbone network is replaced with the lightweight MobileOne neural network to reduce model parameters and improve real-time detection performance. DeepSORT is used for matching and tracking yolk targets across frames, accommodating rotational variations. Experimental results demonstrate that YOLOv7-SEY-DeepSORT achieves a mean average precision (mAP) of 0.931, reflecting a 0.53% improvement over the original YOLOv7. The method also shows enhanced tracking performance, with Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) scores of 87.9% and 73.8%, respectively, representing increases of 17.0% and 9.8% over SORT and 2.9% and 4.7% over Tracktor. Overall, the proposed method balances high detection accuracy with real-time performance, surpassing other mainstream object detection methods in comprehensive performance. Thus, it provides a robust solution for the rapid and accurate detection of defective salted egg yolks and offers a technical foundation and reference for future research on the automated and safe processing of egg products.

Oil and gas flow anomaly detection on offshore naturally flowing wells using deep neural networks

The oil and gas industry is changing. The drive towards cleaner and safer operations is becoming increasingly important. Researchers are looking for more efficient and accurate ways to detect faults that could lead to environmental and sustainability issues. This study aims to enhance the safety and sustainability of the oil and gas industry by improving existing artificial intelligence approaches to automate monitoring and detection of malfunctions. This article explores the application of deep neural networks for anomaly detection in monitoring oil and gas flow in natural flow offshore wells, proposing an innovative approach that takes advantage of the power of Genetic Algorithms and Gated Recurrent Units (GRU). The study aims to enhance the safety and sustainability of the oil and gas industry by leveraging artificial intelligence to automate the monitoring and detection malfunctions. Utilizing a comprehensive dataset from the 3W Petrobras project, which includes real-time data from 21 wells collected between 2012 and 2018, the research focuses on detecting various anomalies such as abrupt increases in basic sediment and water, spurious closures of downhole safety valves, severe slugging, flow instability, rapid productivity loss, quick restrictions in the production choke, scaling, and hydrate formation in production lines. The methodology integrates Long Short-Term Memory (LSTM) networks and GRU backbones with genetic algorithms to optimise model performance. Several hyperparameter optimisation tools were explored innovatively, focusing mainly on Genetic Algorithms, and it was possible to obtain an algorithm with 2 stacked GRU with better comparative performance compared to what is reported in the literature and producing an F1 equal to 0.97. The findings demonstrate the potential of AI to improve real-time anomaly detection, thereby reducing operational risks and contributing to the industry's transition towards greener practices. It also underscores the importance of open data and collaborative efforts in advancing AI applications in the oil and gas sector, aligning with the United Nations' Sustainable Development Goals to mitigate climate impact and promote responsible consumption and production.

Improvement of 3D printing and subsequent microwave freeze-drying solidification accuracy of pineapple gel-based inks: Infill percentage control and internal models design

The purpose of this study was to improve the 3D printing accuracy of pineapple gel and its subsequent microwave freeze drying (MFD) solidification precision based on infill percentage control (30%, 50%, 70%, and 90%) and internal models design (Hilbert curve, honeycomb, and rectilinear). Through comparing the designed models with the physical dimensions of the printed and dehydrated products, the optimal regulation strategy was obtained. Results showed that the printing deviation of samples decreased initially and then increased with higher infill percentages, with the 70% infill percentage having the lowest deviation. Among all internal models, the rectilinear infill pattern showed the best printing accuracy. Porosity and the shape deformation rate of MFD solidification products initially decreased and then increased with higher infill percentages. The 70% infill percentage products had the lowest shape deformation rate. The honeycomb infill pattern performed best in reducing solidification sample shrinkage and deformation. Additionally, micro-CT scans revealed that the honeycomb infill pattern helped maintain the integrity of each layer's lines of MFD solidification products, closely matching the model's line distribution. In the 50% and 70% infill percentage, honeycomb and rectilinear samples exhibited better crispness. A 70% honeycomb infill pattern was recommended for optimal printing and post-solidification accuracy.

Collaborative Approach for Feature Models in Software Product Lines

Collaborative Approach for Feature Models in Software Product Lines

Capability Indices for Digitized Industries: A Review and Outlook of Machine Learning Applications for Predictive Process Control

Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market.

Supervisory Control for Dynamic Feature Configuration in Product Lines

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems.

The Fourth Industrial Revolution and Its Impacts on Production Processes and Efficiency Enhancements Through Automation and Data Networking

Industry 4.0, also known as the Fourth Industrial Revolution, recalls a period of change, knocking complex human activities into production lines by implementing innovative technologies in automation, artificial intelligence, and data networking in manufacturing. Its relevance is found in the capacity to cause significant shifts in the characteristics of industries by improving product output, effectiveness, and adaptability. This paper seeks to review the effects of Industry 4.0 on manufacturing, emphasizing enhancing those technologies in production processes. The first research question is whether implementing Industry 4.0 technologies would lead to enhanced productivity and efficiency of production. To this extent, qualitative research tools, such as case studies and interviews, were used alongside quantitative approaches like surveys and statistical analysis. Some of the conclusions highlighted include finding a cause to believe that Industry 4.0 technologies lead to increased productivity, decreased time lost, and flexibility in operations. Based on these findings, Industry 4.0 is becoming an essential factor in current processes of production system renovation and offers valuable information for industries interested in the successful application of such developments

Analysis of the Impact of Luxury Marketing Methods on Customer Consumption Psychology

Our research object is Herms, a world-renowned luxury brand, especially its brand strategy, distribution and purchase system, and the psychological characteristics of brand consumers. We use a variety of research methods for analysis, including an in-depth study of Herms' brand history, product lines, and marketing strategies through literature. Using the 4P marketing theory model of product, price, location and promotion, it makes a comprehensive analysis of Hermes' marketing strategy. Finally, a questionnaire about Hermes consumers was used to conduct detailed data analysis and interpretation. It is found that the success of Hermes is largely due to its unique brand strategy and marketing strategy. Although the distribution system is controversial, for Herms, its existence is exactly in line with its brand positioning, and it has successfully attracted some wealthy consumers who are willing to pay for luxury experiences.

Building information modeling-based production process optimization model

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency.

A Bayesian hypothesis testing model for quality control of mass customized cabinet manufacturing process

The demand for customization of cabinets and homes has increased, and customized product components are cumbersome in size and time-consuming. In order to realize the mass production of customized cabinet furniture, research from the custom design, optimization of factory machine production process and quality inspection, as well as the flow of the way, is to realize the fundamental method of large-scale production of cabinet furniture. Based on modern intelligent information processing functions instead of manual ordering, order splitting, and unit parts for reasonable division of classification, optimize the topology of the production line, to achieve modern intelligent production of panels. However, in the production process, there is a lack of on-line inspection of some links, and the yield of the product is affected. This thesis establishes a Bayesian hypothesis testing model based on the topological model of the production line process, and through the actual production data, the defective nodes of the manufacturing process are retraced to achieve the purpose of optimizing the customized production.

A Multi-stakeholder Study on the Effect of Layoffs on Separated Employees, Survivors and Organizations: What Needs to be Done?

Organizations attempt to keep pace with the dynamic, ever-changing environment through technology up-gradation, human resource restructuring, diversification of product or service lines, and other similar actions. In most cases, these actions have taken the form of organizational downsizing, leading to the loss of jobs. The primary purpose of this study is to propose an approach that organizations may adopt against the cascading impact of layoffs on separated employees (victims), associates and co-workers (survivors). Online platforms were used for the collection of data. A total of 864 statements were analysed. Sentiment analysis was conducted using the SEMRUSH software. Miles and Huberman’s (1994, Qualitative data analysis: An expanded sourcebook) method was used to categorize the data obtained into meaningful categories to provide results that would support the research objectives. The analysis shows a considerable cascading psychological impact on the human resource ecosystem. This article puts forward the causal model, capturing the interplay of the effects of the stakeholders on one another and their social well-being. Further, the article proposes that the downsizing cycle be constituted such that it minimizes the impact of layoffs. Extant research on job loss has mostly considered one or two stakeholders at a time. We have attempted a multi-stakeholder approach. The study also suggests changes in the human resource (HR) cycle that organizations must examine to mitigate the adverse effects of layoffs.

Burn Injury Results in Myeloid Priming during Emergency Hematopoiesis.

Hematopoiesis proceeds in a tiered pattern of differentiation, beginning with hematopoietic stem cells (HSC) and culminating in erythroid, myeloid, and lymphoid lineages. Pathologically altered lineage commitment can result in inadequate leukocyte production or dysfunctional cell lines. Drivers of emergency hematopoiesis after burn injury are inadequately defined. Burn injury induces a myeloid predominance associated with infection that worsens outcomes. This study aims to further profile bone marrow HSCs following burn injury in a murine model. C57BL/6 mice received burn or sham injury with ~12% total body surface area (TBSA) scald burn on the dorsal surface with subsequent sacrifice at 1, 2, 3, 7 and 10 days post-injury. Bone marrow (BM) from hindlimbs were analyzed for HSC populations via flow cytometry and analyzed using FlowJo Software (version 10.6). Event counts and frequencies were analyzed with multiple unpaired t-tests and linear mixed-effect regression. RT-PCR performed on isolated lineage negative BM cell RNA targeted PU.1, GATA-1, and GATA-3 with subsequent analysis conducted with QuantStudio 3 software. Statistical analysis and representation were performed on GraphPad software (Prism). Flow cytometry revealed significantly elevated proportions of Long-Term HSCs at 3 days post-injury (p < .05) and Short-Term HSCs at days 2, 3, and 10 (all p < .05) in burn-injured mice. There was a sustained, but not significant, increase in proportions in the multi-potent progenitor (MPP) 2 and 3 subpopulations in the burn cohort compared to sham controls. The common myeloid progenitor (CMP) proportion was significantly higher on days 3 and 10 (both p < .01), while the granulocyte-macrophage progenitor (GMP) proportion increased on days 1, 2, and 10 (p < .05, p < .01, p < .01). Although the megakaryocyte-erythrocyte progenitor (MEP) proportion appeared consistently lower in the burn cohort, this did not reach significance. mRNA analysis resulted in a downregulation of PU.1 on day 1 (p = 0.0002) with an upregulation by day 7 (p < 0.01). GATA-1 downregulation occurred by day 7 (p < 0.05), and GATA3 showed downregulation on days 3 and 7 (p < 0.05). Full-thickness burn results in an emergency hematopoiesis via proportional increase of Long Term-HSC and Short Term-HSC/MPP1 subpopulations beginning in the early post-injury period. Subsequent lineage commitment displays a myeloid predominance with a shift toward myeloid progenitors with mRNA analysis corroborating this finding with associated upregulation of PU.1 and downregulation of GATA-1 and GATA-3. Further studies are needed to understand how burn-induced emergency hematopoiesis may predispose to infection by pathologic lineage selection.

Technology Innovation Is the Key to Triumph for the Glaring Future of the Fashion Industry

Over the last decade, the fashion industry has been at the forefront of the latest technology. From the introduction of sewing machines for stitching fashion products to biodegradable fabrics to fashion renting apps to needle-free sewing, the fashion industry has seen a lot of changes over the last few years. Evolution in fashion begins with the introduction of artificial intelligence. Technology in fashion is at its profuse self in the current sphere. Technology comprises Artificial intelligence that is enhancing the production capacity of the fashion manufacturing industry, secondly, it comprises Augmented reality which positively boosts the customers' shopping experience and lastly, the virtual outlook increases the remunerative economy in the fashion industry. When one thinks of evolution, artificial intelligence is the latest introduction to the world of technology. Technology in fashion has never been more abundant than it is now. Artificial intelligence is optimizing vast production lines, augmented reality is enhancing the customer experience, and the virtual world is creating lucrative new revenue streams [1]. In the fashion industry, one of the most observable transposes is due to e-commerce. The convenience of online shopping is becoming very appreciated. Fashion companies have started incorporating innovative digital ways to reach their targeted audience. The advanced technologies include virtual fitting rooms which help customers in making more spontaneous decisions in their purchase as they can preview the product and exactly see how that product looks on their body type [2]. Artificial intelligence is used in the design process in a fashion that makes it much easier, like using AI to resize a garment digitally without actually making the garment, digitally changing the color, and also AI can create a visual background to give the buyer the precise judgment about the overall look of the garment. This saves time and resources, which in turn helps in reaching a wider range of customers [3] Another evolution with technology is the introduction of 3d printing and robotics, which cannot accelerate the rate of production. Design software, computerized laser-cutting machines, and digital pattern-making are some of the new manufacturing techniques in the fashion industry.

An autonucleolytic suspension HEK293F host cell line for high-titer serum-free AAV5 and AAV9 production with reduced levels of DNA impurity

We sought to engineer mammalian cells to secrete nuclease activity as a step toward removing the need to purchase commercial nucleases as process additions in bioprocessing of AAV5 and AAV9 as gene therapy vectors. Engineering HeLa cells with a serratial nuclease transgene did not bring about nuclease activity in surrounding media whereas engineering serum-free, suspension-adapted HEK293F cells with a staphylococcal nuclease transgene did result in detectable nuclease activity in surrounding media of the resultant stable transfectant cell line, "NuPro-1S." When cultivated in serum-free media, NuPro-1S cells yielded 3.06×1010 AAV5 viral genomes (vg)/mL via transient transfection, compared with 3.85×109 vg/mL from the parental HEK293F cell line. AAV9 production, followed by purification by ultracentrifugation, yielded 1.8×1013 vg/mL from NuPro-1S cells compared with 7.35×1012 vg/mL from HEK293F cells. AAV9 from both HEK293F and NuPro-1S showed almost identical ability to transduce cells embedded in a scaffold tissue mimic or cells of mouse neonate brain tissue invivo. Comparison of agarose gel data indicated that the DNA content of AAV5 and AAV9 process streams from NuPro-1S cells was reduced by approximately 60% compared with HEK293F cells. A similar reduction in HEK293F cells was only achievable with a 50U/mL Benzonase treatment.

CarryPro Backpacks: Creating a Niche in Backpacking

The backpacking market in the Indian luggage industry is growing steadily, driven by the increasing popularity of adventure travel and backpacking among the younger generation. In 2018, CarryPro entered the Indian market with the intention of focusing on the need for lightweight, durable backpacks. For ultralight luxury travel, CarryPro backpacks were superior. Ankit Agarwal (founder and CEO) has highlighted the competition-related challenges to CarryPro’s growth. Wildcraft, Quechua and Decathlon dominate the backpack market in India. Other brands are also gaining popularity in India, with brands like Thule, Peak Design, and Patagonia becoming more widely available. The competition has heated up because of the growing interest among our rivals in backpacks’ utility. CarryPro, which entered the market with just three products, faced severe competition from established companies with extensive product lines. The company contemplated raising backpack sales by 20%–30% annually. However, they knew carving a niche among backpackers would be challenging without using sound distribution, branding and positioning strategies. The brand strategy and sales and distribution network operated by CarryPro proved to be the company’s deciding elements in the long run. Hence, the founder contemplated various branding, positioning and distribution strategy options.

The Influence of Inclusive Brand Image in Indonesian Cosmetic Products on Customer Loyalty

In recent years, cosmetic brands in Indonesia have shifted their marketing strategies to emphasize inclusivity, aiming to foster stronger customer loyalty. This shift includes the portrayal of diverse beauty standards and the inclusion of a wide range of skin tones and types in their product lines. Prior studies have demonstrated that an inclusive brand image significantly boosts brand trust and customer satisfaction. The research investigates the influence of an inclusive brand image in Indonesia’s cosmetic products on customer loyalty, focusing specifically on Generation Z consumers. By employing a quantitative research methodology, data was collected using online surveys distributed to 201 respondents aged 12-27 years. The study aims to explore the relationship between inclusive brand image and brand trust, customer satisfaction, and loyalty within the context of the Indonesian cosmetics market. Data analysis was conducted using descriptive statistics and PLS-SEM. The findings indicate that an inclusive brand image positively influences brand trust and customer satisfaction, which subsequently strengthen customer loyalty. Findings from this research are anticipated to provide insights that can help local cosmetic brands enhance their inclusive marketing strategies, thereby fostering a sense of acceptance and belonging among their diverse customer base and driving customer loyalty.