Production Line Research Articles

Evaluation of Quality for Flow Drill Screw (FDS) Joining Process Based on Loosening Torque

Although the SPR process is widely used for joining of multi-materials, the FDS process is used when one-way joining is required, such as space constraints or closed cross-sectional parts. The quality inspection for the FDS process is basically an appearance inspection or cross-sectional inspection. There is no non-destructive test method that can be used in the actual mass production line, and a method of utilizing loosening torque is being studied. In this study, the factors affecting the loosening torque are analyzed, and an experimental equation that can predict the loosening torque is derived. Since friction force between the screw and the upper plate and sticking on the screw thread resist against loosening torque, the prediction equation for each factor is considered and the least square method is used to obtain the coefficients of the experimental equation. The prediction equation of the loosening torque derived through experiments on 36 material combinations predicts the experimental values well. When a quality requirement value with a given margin is applied, it can be used as a non-destructive test method.

I3oT (Industrializable Industrial Internet of Things) Tool for Continuous Improvement in Production Line Efficiency by Means of Sub-Bottleneck Detection Method

The present paper shows how to develop an I3oT (Industrializable Industrial Internet of Things) tool for continuous improvement in production line efficiency by means of the sub-bottleneck detection method. There is a large amount of scientific literature related to the detection of bottlenecks in production lines. However, there is no scientific literature that develops tools to improve production lines based on the bottlenecks that go beyond rebalancing tasks. This article explores the concept of a sub-bottleneck. In order to detect sub-bottlenecks in a massive way, the use of one of the I3oT (Industrializable Industrial Internet of Things) tools developed in our previous work, the mini-terms, is proposed. These mini-terms use the existing sensors for the normal operation of the production lines to measure the sub-cycle times and use them to predict the deterioration of the machine components found in the production lines. The sub-bottleneck algorithms proposed are used in two real twin lines at the Ford manufacturing plant in Almussafes (Valencia), the (3LH) and (3RH), to show how the lines can be continuously improved by means of sub-bottleneck detection.

Vehicular Mini-LED backlight display inspection based on residual global context mechanism

Mini-LED backlight has emerged as a promising technology for high performance LCDs, yet the massive detection of dead pixels and precise LEDs placement are constrained by the miniature scale of the Mini-LEDs. The high-resolution network (Hrnet) with mixed dilated convolution and dense upsampling convolution (MDC-DUC) module and a residual global context attention (RGCA) module has been proposed to detect the quality of vehicular Mini-LED backlights. The proposed model outperforms the baseline networks of Unet, Pspnet, Deeplabv3+, and Hrnet, with a mean intersection over union (Miou) of 86.91%. Furthermore, compared to the four baseline detection networks, our proposed model has a lower root-mean-square error (RMSE) when analyzing the position and defective count of Mini-LEDs in the prediction map by canny algorithm. This work incorporates deep learning to support production lines improve quality of Mini-LED backlights.Graphical abstract

A Systematic Mapping of Configuration Management Activities in Software Product Line

In the software product line (SPL), configuration management (CM) is a multidimensional problem that is taking great attention in software development.  Although much research has been developed on this topic, there is no clear view of the current state of it. In this study, we used a systematic method to develop a map of configuration management across product lines to classify relevant literature. The resulting map provides an overview of this research through the identification of the main activities of the CM, the types and trends of research as well as the maturity of existing contributions. Because the CM in SPL is still in its formative stage, we believe that this work will contribute to the process of providing a more common and coherent conceptual basis for its understanding. In addition, it can help to detect important research problems and gaps.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Investigation of Staphylococcus Spp and Coliform Bacteria Contamination Sources after Cleaning-in-Place in Production Lines of Dairy Factories in Türkiye.

Microorganisms detected on dairy factory surfaces after disinfection can cause product contamination, leading to economic losses and health hazards for consumers. In this study, the presence of Staphylococcus spp. and Coliform in a total of 450 samples taken from food-contact and non-contact surfaces (stainless steel, plastic, cloth, and tiles surfaces), raw milk and final product (white cheese, kashar cheese, butter, yogurt, and cream) samples in five dairy factories was investigated by conventional techniques. The isolates obtained were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry. In this study, a total of 54 Staphylococci (16.7% S. aureus and 81.5% coagulase-negative Staphylococci [CNS]) and 44 coliform isolates were identified at the species level. The most common CNS isolated by samples was S. epidermidis followed by S. saprophyticus, S. capitis, S. succinus S. carnosus. S. xylosus, S. sciuri, S. equorum, S. warneri, and S. hominis. Eighteen of the coliform isolates (41%) were identified as E. coli; 13 (29.5%) as E. cloacae; 3 (6.9%) as E. kobei, C. freundii, and K. oxytoca; 2 (4.5%) as K. pneumoniae; 1 (2.3%) as E. ludwigii and C. farmeri. The contamination rate of non-food contact surfaces (71.3%) was found to be higher than food contact surfaces (10.4%), and contaminated surfaces were found to be effective in product contamination. Study results show that some bacterial species obtained from raw milk, surfaces, and final products are factory specific and surface-associated bacteria are prominent in the product microbial profile.

Recent Advancements in Proteomic Sample Preparation from Recombinant Chinese Hamster Ovary Cells.

Chinese hamster ovary (CHO) cells are the most commonly used mammalian host cell line for biopharmaceutical production because of their ability to correctly fold and post-translationally modify recombinant proteins that are compatible with human use. Proteomics, along with other "omic platforms," are being used to understand the biology of CHO cells with the ultimate aim to enhance CHO cell factories for more efficient production of biopharmaceuticals. Liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis has become the standard technique for profiling proteomes. There are three stages to a typical bottom-up approach, namely, sample preparation, LC-MS/MS, and data analysis. Although there have been major advances in LC-MS/MS instrumentation and data analysis tools, sample preparation is still the crucial stage that affects the overall efficiency of any proteomic study. In this chapter, we present a comparison of a number of widely used sample preparation methods for proteomic applications, including in-solution digestion and commercially available device-based kits. All methods performed well; however, each method has inherent advantages and disadvantages.

Survival 1.0: A Novel Autonomous Industrial Mobile Warehouse (AIMW) for Industrial Environments

This article presents the design, implementation and start-up of the first AIMW (Autonomous Industrial Mobile Warehouse) in the industry, in particular, in the factory located at Ford España S.L. in Almussafes (Valencia). This first version has been given the name of “Survival”, due to its ability to survive the industrial environment commonly known as “the jungle”. The motivation for the design of the first AIMW was due to the management of spare warehouses for maintenance tasks. In all factories there is a central warehouse where replacement materials with a higher probability of use are placed near the warehouse exit, thus reducing the time to obtain the replacement. However, in large factories, such as automobile manufacturing, the distance between the central warehouse and the place where the breakdown occurs may cause very long delays. For this reason, there are smaller warehouses distributed throughout the plant that replicate the part of the central warehouse where the most used materials are kept. The objective of our research is to make that part of the central warehouse mobile with the AIMW design, thus eliminating the fixed warehouses. In the factory at Ford España S.L. in Almussafes there are 170 of these warehouses distributed in a homogeneous way. In this first proposal of an AIMW, and with the primary objective of reducing costs, Survival 1.0 has been designed based on a commercial AMR. For its choice, commercial AMRs were tested in real situations within the Ford factory (Almussafes). As a result, none of them were able to pass all the tests. In addition, the current regulations for AMRs, the ANSI/ITSDF B56.5:2019 and the EN 1525:1997 standard, only define minimum test standards to describe how manufacturers and users must carry out the safety measurements of AGVs and AMRs. However, from an industry point of view, these regulations are not adequate. Therefore, there are extensions of these regulations generated by the companies themselves that imply additional developments of commercial AMRs. This paper shows the developments made to be able to integrate the first AIMW into the production line, but it also shows the gap between research and standardization of AMRs with respect to the demands and needs of the industry. There are currently three Survival 1.0 AIMWs in operation in the factory at Ford España S.L. in Almussafes.

An Analysis of Tesla's Disruptive Innovation Strategy

With the rapid development of new energy vehicles, governments and automobile manufacturers are generally aware that environmental protection is an important direction for the development of the future automotive industry, and Tesla, as a representative of the global new energy electric vehicles, has great research significance. In the fierce environment of the automobile market, Tesla has won the leadership position with many innovations. Therefore, the company is considered a disruptive innovation by the public. This paper focuses on whether Teslas innovation is disruptive innovation, and studies Teslas disruptive innovation strategy through literature reading and qualitative analysis. This paper discusses the definition of disruptive innovation, Teslas current innovation status and explores the difference between it and incremental innovation. This paper draws the conclusion that Tesla is a disruptive innovation by considering whether it creates a value network as a breakthrough point. Finally, the paper puts forward some suggestions, including extending the product line, adjusting its price to maintain its competitive advantage and innovation, adjusting the channel level based on local markets, provide more extreme and detailed experiential marketing and after-sales service, so as to promote Tesla's sustainable innovation.

Testing a nutrient composition threshold model to classify brands for marketing restrictions.

Food marketing restrictions often apply nutrient profile models (NPM) to distinguish unhealthy products that should not be advertised, however brand-only marketing remains largely unaddressed. We sought to test a threshold method for classifying packaged food, beverage, or fast-food brands as (non)permitted for marketing, based on the nutrient profile of their product-lines. We retrieved nutrient information from the Nutritrack databases for all products sold by the top 51 packaged food, beverage and fast-food brands in New Zealand, selected by market share. All products under each brand were classified as permitted (or not) to be marketed to children, using the NPM for WHO Western Pacific. The 25%, 50%, 75% and 90% threshold of brands' products permitted to market were compared. The 50% and 75% thresholds were compared to the WHO CLICK method, which is based on assessment of the brand's leading product. The 90% threshold permitted 13% of the brands to be marketed to children. The 25% threshold permitted the marketing of 62% of brands. The 50% and 75% thresholds remained highly sensitive in identifying brands that should not be marketed to children. Comparison to the WHO CLICK method identified that a threshold method is more comprehensive and less arbitrary. A threshold model based on product-line nutrient profiling provides a robust and option for brand classification. The 50% and 75% thresholds may be the most politically preferred options for use in regulation, while remaining highly effective. Brand marketing (e.g. sponsorship) remains largely unaddressed in existing restrictions on unhealthy food and beverage marketing to children.An established Nutrient Profile Model can be applied to a brand's entire product line, allowing calculation of the proportion of products that would not be permitted to be advertised to children.Restricting brand marketing for food and beverage brands with less than 50% or 75% of their products classified as 'permitted to be marketed to children' is a robust and evidence-based method that can be applied in regulation, with potential to mitigate industry challenges.

Relationship Between Organisational Innovation and Organisation Performance: A Study of Nigeria Bottling Company

In an increasingly competitive business environment, the role of organisational innovation as a driver of performance is more crucial than ever. This study investigates the relationship between organisational innovation and organisational performance with a specific focus on Nigeria Bottling Company. Using a mixed-methods approach, the study explores how innovations in product development, processes, and management systems contribute to enhanced operational efficiency, market share, and financial outcomes. The study adopts a descriptive cross-sectional survey. The target population for this study consisted of two hundred and forty-five (245) staff members from Nigeria Bottling Company. Data was collected through structured questionnaires with key personnel across various departments within the organisation and analysed using descriptive statistics and regression analysis for the hypotheses. The study revealed that film size innovation is vital for a company's success in today's highly competitive business environment and leads to higher productivity, meaning that the same input generates a greater output. Innovatiocontributeste to firms’ success in terms of better performance in the form of higher profits, lower production costs, more efficient working procedures, and better relationships with customers. Furthermore, finding shows that creativity and innovation helps and improves the work of employees together with improving organisational performance. The study concludes that fostering a culture of continuous innovation is essential for sustaining competitive advantage in the beverage industry. It is therefore recommended that technological innovation be incorporated at all levels. The management of the organisation has to design an effective and timely market innovation strategy and communicate it to all staff members to enhance organisational performance and productivity. Organisations should consider innovation that is either market, product or organisation-based to improve and increase the organisational performance as innovation is significant to new product development, product modification and creation of a new market as experienced in new product lines like increase in online marketing, online purchase of different products and global connection of markets into one.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Compact and Simple Antenna Inspection Testing Range for 5G Smartphones

Abstract Over-the-air (OTA) antenna performance evaluation in the millimeter-wave (mmWave) band often relies on Compact Antenna Test Range (CATR) systems, which require large reflectors and a substantial measurement range volume. Such setups are impractical for rapid testing in space-constrained environments such as antenna fault diagnosis in smartphone manufacturing facilities. This paper presents a novel design methodology for highly compact antenna inspection testing range utilizing offset parabolic reflectors specifically designed for 5G smartphones. The proposed design system employs three small offset parabolic reflectors configured to account for beam tilting caused by smartphone back covers, eliminating the need to physically reposition the smartphone during testing. By directing electromagnetic fields toward a fixed receiver antenna from multiple angles corresponding to different antenna module positions, the system streamlines the test procedure, significantly reducing measurement time and measurement range volume. The proposed antenna inspection testing range provides a cost-effective and efficient solution for 5G antenna fault diagnosis in manufacturing facilities, ensuring rapid and reliable inspection while minimizing space requirements on the production line.

Optimizing Pricing Strategies for Product Lines and Value‐Added Services: Accounting for Reference Prices and Network Effects

ABSTRACTCustomer choice behavior is increasingly influenced by the reference price effect and negative network effect as customers become more rational and personalized. This study, considering customers as pivotal stakeholders for enterprise development, employs the multinomial logit (MNL) model and develops pricing and strategy optimization models for pure bundling and mixed bundling strategies of an enterprise's product line. The main focus is to analyze how these effects impact optimal pricing and strategy selection. The results indicate that stronger effects lead to lower enterprise profits, and ignoring them can cause substantial losses. Generally, mixed bundling strategies yield higher profits.

A Context-Based Perspective on Frost Analysis in Reuse-Oriented Big Data-System Developments

The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.

Engineering the Future: Robotics in the Automotive Industry Explained

The study provides an overview of the potential applications of industrial robots in the automobile sector, which is now the market's most significant client. An overview of the use of robots in global industry is provided in the first section of the article, which also discusses the state of industrial robot utilization. The most popular uses of robots, particularly in the automotive sector, are covered later, along with developments and outlooks for the field's future. The essay concluded with a specific project overview regarding robotized screw tightening on the car seat production line, which served as a model task from actual industrial practice.The many uses of robots in the automobile industry are examined in this review, including material handling, welding, painting, assembly line automation, and quality control. It emphasizes how collaborative robots (cobots) enhance human-robot interaction and how advances in machine learning and artificial intelligence maximize robotic performance. Keywords: Industrial robot; robotics in automotive; production of passenger cars; transport and production; automated assembly line

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Exploring the potential of triticale lines for bioethanol production

Aim: Triticale is a well adaptable crop, tolerant of disease and abiotic stresses, and able to grow with good yields even in poor soil, thus representing a good choice to develop a new industrial agri-chain in Italy in a sustainability contest, to cope with its soil problems due to incoming desertification. Methods: Two triticale elite lines were grown in marginal lands in controlled field experiments. The lines were harvested at two different development stages, namely green mass and seeds, and suitable standard protocols were applied to test their potential to produce bioethanol in line with the emerging bioenergy processes. Results: The protocols applied were able to obtain bioethanol with a good yield from both feedstocks. In particular, very efficient fermentation kinetics was observed using seed feedstock, with a sharp curve between 15 h and 24 h, reaching 84% of the total alcohol obtained (final time 72 h). Conclusions: Therefore, the results of this research point to new sustainable potential for industrial applications of triticale crops in Italy. Furthermore, the high activity of the endogenous amylolytic enzymes, mainly α-amylase, and the high starch content suggest the potential use of triticale in other industrial applications, like the brewing industry.

An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.

Assessing the spread of sulfachloropyridazine in poultry environment and its impact on Escherichia coli resistance

Sulfachloropyridazine (SCP) is an antimicrobial (AM) commonly used in the poultry industry. This drug is excreted as the original compound, which may accumulate in litter. This work was done to assess whether SCP residues from droppings of broiler chickens that were treated with therapeutic doses of this drug spread into the production environment and to determine if these events were associated with the selection of resistant bacteria. To this end, broiler chickens were raised under controlled conditions, and their droppings and litter were processed to detect and identify SCP residues using an HPLC-MS/MS technique. This study selected Escherichia coli as an indicator bacterium for AM resistance. Its phenotypic resistance was determined using the Kirby-Bauer disk diffusion method, and its genotypic resistance was determined by performing a conventional PCR test. Our results showed that SCP residues did spread from the treated group to untreated sentinel groups because SCP residues in the litter reached levels up to 43.05 µg·kg−1 in a group placed immediately adjoining to the treated group, while another group placed 30 cm away showed a concentration of 29.79 µg·kg−1. Meanwhile, only trace concentrations were detected in droppings collected from sentinel groups. Of 239 strains of E. coli isolated from droppings, 12.13 % were resistant to sulfonamides, whereas 23.91 % of 92 E. coli isolated from broiler litter were resistant. The most prevalent resistance gene was the sul2 gene, both in droppings and litter, followed by the sul1 gene. The SCP concentrations were associated with the probability of E. coli being resistant to sulfonamides (p-value = 0.01). A Multiple Correspondence Analysis (MCA) also showed that phenotypic and genotypic resistances were associated and that both genes sul1 and sul2 would determine phenotypic resistance to sulfonamides in E. coli. The results presented in this study show that inedible by-products of the poultry industry are potentially a source of drug resistance that can spread from the animal production line to the environment, so awareness of the correct use of antimicrobials is essential to combat antimicrobial resistance.