Production Line Research Articles

Approach to Sorting Line Control using PLC and Factory I/O

The article presents the possibility of using a 3D environment to simulate the operation of a manufacturing process and control a virtual object with a PLC. A sample application of the Factory I/O environment compatible with TIA Portal software and the real S7-1214 DC/DC/DC controller is presented. Visualization and program code written in LAD language correlated with the 3D environment were presented. The results showed the importance of a dynamic programming environment that reflects the program code designed to sort virtual production line components. The research showed that working with the code and the virtual object experiences students and is closer to industrial solutions than just laboratory ones.

Evaluation of OEM Prioritization to Support the Growth Strategy of PT. Sulzer Indonesia in the >400MW Steam Turbine Product Line

Evaluation of OEM Prioritization to Support the Growth Strategy of PT. Sulzer Indonesia in the >400MW Steam Turbine Product Line

Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel, the operation mode with no communication between production lines will no longer be the optimal production paradigm. This paper describes matrix manufacturing systems (MMS) in a general manner from the perspective of related works, comparing different manufacturing organizational forms and their characteristics. Subsequently, MMS are extracted during the parallel production of multiple surface mount technology (SMT) lines. An overall equipment effectiveness (OEE) online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS. The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS. The efficiency of each matrix unit (MU) was calculated, and a collaborative optimization method was proposed based on an indicator (OEE). In this paper, an example of eight SMT lines is presented. The partitioning of MUs, OEE calculation of each MU, and the low OEE unit collaborative optimization method are described in detail. Through a case study, the architecture of the collaborative optimization model for the MMS was constructed and discussed. Finally, the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

Purchase or generate? An analysis of inter-fuel substitution and electricity generation in Japanese manufacturing plants

As the manufacturing industry is one of the largest contributors to global emissions, decarbonization of the production line is a key aspect in the fight against climate change. In this study, we examine the level of substitutability between fossil fuel and electricity. Using data on Japanese plants from 2004 to 2020, we estimate the elasticity of substitution between the two inputs and find that a 1 % increase in electricity prices results in a 6.55 % increase in fossil fuel consumption. Our paper also contributes to explaining mechanisms behind inter-fuel substitution, with a special focus on electricity and fossil fuel through cogeneration. We find that substitutability is highly sector-dependent, and identify the pulp & paper, iron & steel, chemicals and cement to be sectors with substitution capacity. These sectors see an increase in their electricity generation, the magnitude of which is estimated between 0.004 % (cement) to 0.23 % (iron & steel). Iron & steel and cement also increase their consumption of coal to power generators by 0.06 % and 0.005 %, respectively. Our findings suggest the need for taxation of both fuel and electricity together, to discourage further substitution attempts that would hinder decarbonization efforts.

CFD-DEM simulation and experimental validation of air classification for tobacco particles

In cigarette processing, the challenge is particularly significant during the air classification of tobacco and stems due to their similar characteristics. This paper employs computational fluid dynamics (CFD) combined with the discrete element method (DEM) to analyze factors affecting separation efficiency and improve performance. The results were validated through laboratory and production line experiments at a 1:1 scale. The tobacco (length: 0 mm–4.75 mm, diameter: 0.32 mm) and stem (length: 0–25.145 mm, diameter: 1.51 mm) were modeled based on production samples. Findings suggest that particle feeding speed primarily impacts tobacco loss rate, while inlet air velocity mainly influences stem removal rate. Optimizing the chamber structure in the simulation resulted in a 63.66 % improvement in separation efficiency. Airflow streamlines, particle distribution, trajectory, and collision behaviors were discussed to illuminate motion characteristics. The flexible particle model moderately influenced separation efficiency and collision behaviors. These insights enhance the understanding of particle separation and the design of separation devices.

Analysis of Financial Performance of Yunnan Baiyao Group Co.

Yunnan Baiyao Group Co., Ltd. has been actively expanding its business territory and product lines, led by its diversification strategy, thus achieving remarkable financial results. The company has also used scientific asset management strategies to optimize the allocation of resources and enhance the efficiency of asset utilization, while efficient operations have provided solid support for its diversified growth. However, the implementation of a diversification strategy also introduces specific financial risks and challenges. Increasing financial pressure and intensifying market competition have gradually emerged, which require Yunnan Baiyao Group Co., Ltd. to continuously improve its financial structure and strengthen risk management to ensure the durability and profitability of its strategy. This article not only comprehensively analyzes the financial performance of Yunnan Baiyao Group Co., Ltd. in implementing its diversification strategy, but also deeply discusses the challenges faced by the group and its corresponding response strategies, which provides valuable reference and far-reaching insights for other enterprises in adopting similar strategies.

Establishment of a novel cell line for producing replication-competent adenovirus-free adenoviruses

Adenoviruses are commonly utilized as viral vectors for gene therapy, genetic vaccines, and recombinant protein expression. To generate replication-defective adenoviruses, E1-complementing cell lines such as HEK293A are utilized; however, limitations remain. Repeated passage of E1-deleted virus in HEK293A cells increases the occurrence of replication-competent adenoviruses (RCAs). In the present study, we developed a novel cell line originating from human primary cells. L132 cells were transduced two times with E1-encoded retrovirus and three times with E1A-encoded retrovirus. Finally, we selected the most productive L132 cell line for generation of RCA-free adenovirus, GT541. GT541 can serve as an alternative cell line to HEK293A and other adenovirus-producing cells.

Raman Spectroscopic Analysis of Steviol Glycosides: Spectral Database and Quality Control Algorithms

Besides all sharing an extraordinary high (i.e., up to ~450 times) sweetening power as compared to sucrose and while presenting strong similarities in their molecular structures, molecules belonging to the family of diterpene glycosides (i.e., the secondary metabolites of Stevia rebaudiana) differ in specific structural details that strongly impact on their levels of sweetness and bitter aftertaste. Given the nutritional and pharmacological benefits of steviol secondary metabolites as natural dietetic and anti-diabetic remedies, extraction and purification of steviol glycosides from plant material are nowadays widely spread among many countries. However, an unpleasant bitter aftertaste, which is linked to a genetic variation in human bitter taste receptors, hampers the full exploitation of such benefits and calls for a prompt improvement in organoleptic property control of stevia products. A deeper understanding of the molecular structure of different steviol glycosides and the consequent development of promptly measurable criteria for the organoleptic performance of their mixtures will support processing optimization and control of taste profiles within desired yields. The present research aimed at establishing Raman spectroscopic algorithms for quantitative characterizations of raw stevia-based sweetener products. First, a series of twelve high-purity diterpene glycosides were analyzed by high spectrally resolved Raman spectroscopy and their spectra analyzed in order to establish a complete Raman library of molecular structures. Then, quantitative spectroscopic parameters were built up and applied to characterize the organoleptic property of five different commercially available samples including the recently developed Rebaudioside M isoform. Raman spectroscopy was confirmed as a versatile analytical technique that could be used for quantitative quality control tasks on the production line and for prompt in situ characterizations of purchased products.

Microwave cavity antenna for automatic detection of chicken breast muscles affected by wooden breast defect

Over the past years, artificial selection for meat-type (broiler) chickens has resulted in improved production efficiency, but also in an increased incidence of skeletal muscle abnormalities. In particular, wooden breast (WB) abnormality causes abrupt changes in meat quality and enormous economic losses. Thus, the poultry processing industry requires a reliable method to detect the abnormality in production lines in a non-destructive and contactless way. The present research aimed to evaluate the effect of WB on dielectric properties to develop a potential online technique to distinguish unaffected from affected breasts. Sixty-five pectoralis major muscles were picked 3 h post-mortem from the same flock (42-day-old broilers, 2.8 kg average live weight) and classified by visual inspection according to their phenotype as normal (N, not showing not any signs of the WB condition; n = 33) or WB (exhibiting extensive hardened areas and stiffness perceived by manual palpation throughout the entire fillet; n = 32). WB muscles exhibited remarkably higher values of dielectric constant and loss factor in a wide range of the explored frequencies (200 MHz–14 GHz), suggesting higher water mobility and a higher solvate capacity; this makes the electromagnetic technique for classification promising. Based on the above evidence, a rapid microwave electric technique in the range 1.5 GHz–3 GHz was developed for the online detection of WB. Indeed, combining the use of a cavity antenna (vector network analyzer instrumental chain with partial least square – discriminant analysis), this technique correctly classified 100% of the validation test set.

XGBoost-based prediction of electrical properties for anode aluminium foil

Anode aluminum foil (AAF) shows advantages such as high electrical conductivity, high specific capacitance, and low cost, making it a high-quality electrode material for energy storage. However, the formation of AAF is a material process with multiple influencing factors interacted with each other, and it is difficult to analyze the influencing mechanism of each factor and predict the electrical properties. To solve the aforementioned problem, the process data of AAF is collected from a actual production line as the research subject firstly. The correlation coefficient between the process parameters and withstand voltage (WV) is used for feature selection of the data sets. XGBoost-based prediction model is established to predict the electrical properties of AAF, which is evaluated using R2 and root-mean-square error (RMSE). The results indicate that the process parameter X70 (Voltage of repair 3) shows the most significant effect on the WV. The magnitude of the WV shows a stepwise increase with the increase of X70. The result of prediction model is a R2 of 0.990 and a RMSE of 3.888. The XGBoost-based prediction model for the electrical properties (WV) of AAF shows excellent prediction characteristic, which contributes to the optimization of process parameters and the properties prediction.

Simulation of recrystallization of cold-rolled automotive sheet depending on the degree of deformation, annealing temperature and chemical composition

The article presents the results of modeling the recrystallization of a cold-rolled sheet of 10 grades of automotive steels of various chemical composition. Mathematical models of recrystallization processes in cold-rolled sheets of the studied steels have been developed, which describe with satisfactory accuracy both the kinetics of the process during isothermal annealing and the grain size of recrystallized ferrite. The developed recrystallization model predicts with sufficient accuracy the kinetics of crystallization under conditions of heating at an arbitrary rate and subsequent isothermal exposure, which are used in annealing cold-rolled sheets on industrial production lines. The developed recrystallization model adequately takes into account both the influence of the carbon content and the main alloying elements of substitution (Mn, Si, Nb, Ti) on the kinetics of the process

Outbreak of Listeriosis Likely Associated with Baker's Yeast Products, Switzerland, 2022-2024.

We traced back a nationwide outbreak of human listeriosis in Switzerland to a persisting production line contamination of a factory producing baker's yeast with Listeria monocytogenes serotype 1/2a sequence type 3141. We used whole-genome sequencing to match clinical isolates to isolates from product samples.

Novel Approach of Tackling Wax Deposition Problems in Pipeline Using Enzymatic Degradation Process: Challenges and Potential Solutions

Anthropogenic activities have led to hydrocarbon spills, and while traditional bioremediation methods are costly and time-consuming, recent research has focused on engineered enzymes for managing pollutant. The potential of enzymes for resolving wax flow problems in the petroleum industry remains unexplored. This paper offers a comprehensive review of the current state of research activities related to the bioremediation of petroleum-polluted sites and the biodegradation of specific petroleum hydrocarbons. The assayed enzymes that took part in the degradation were discussed in detail. Lipase, laccase, alkane hydroxylase, alcohol dehydrogenase, esterase, AlkB homologs and cytochrome P450 monooxygenase are among the enzymes responsible for the degradation of more than 50% of the hydrocarbons in contaminated soil and wastewater and found to be active on carbon C8 to C40. The possible biodegradation mechanism of petroleum hydrocarbons was also elucidated. The enzymes’ primary metabolic pathways include terminal, subterminal, and ω-oxidation. Next, given the successful evidence of the hydrocarbon treatment efficiency, the authors analyzed the opportunity for the enzymatic degradation approach if it were to be applied to a different scenario: managing wax deposition in petroleum-production lines. With properties such as high transformation efficiency and high specificity, enzymes can be utilized for the treatment of viscous heavy oil for transportability, evidenced by the 20 to 99% removal of hydrocarbons. The challenges associated with the new approach are also discussed. The production cost of enzymes, the characteristics of hydrocarbons and the operating conditions of the production line may affect the biocatalysis reaction to some extent. However, the challenges can be overcome by the usage of extremophilic enzymes. The combination of technological advancement and deployment strategies such as the immobilization of a consortium of highly thermophilic and halotolerant enzymes is suggested. Recovering and reusing enzymes offers an excellent strategy to improve the economics of the technology. This paper provides insights into the opportunity for the enzymatic degradation approach to be expanded for wax deposition problems in pipelines.

Determinants of Carbon Inventory and Systematic Innovation Methods to Analyze the Strategies of Carbon Reduction: An Empirical Study of Green Lean Management in Electroplating an Factory

This study explored the relationship between lean management, carbon inventory, and carbon reduction, along with the main factors affecting factory carbon inventory and carbon reduction, and proposed a set of carbon emission reduction strategies based on green value stream mapping. An electroplating factory and its production line in Taiwan was selected. Fourteen carbon inventory records, three work circle meetings, and green value flowcharts were utilized to collect and analyze data. Furthermore, this study applied the DMAIC framework and TRIZ to solve the issue of carbon emissions. The key factor affecting factory carbon inventory and carbon reduction was found to be electricity waste, which could be reduced through energy-saving methods. After analyzing the green value stream mapping, effective carbon emission reduction can be implemented in the manufacturing process. The company was found to gradually progress towards carbon reduction after investigating seven major green wastes. This study confirms that lean management can help organizations achieve their carbon emission reduction goals and is valuable in improving the organization’s environmental performance and competitiveness.

Situational control and strategic choices during supply chain disruptions: case studies of SME exporters

PurposeMotivated by the severe impact of global supply chain disruptions and the lack of understanding of supply chain resilience from an SME exporter perspective in the international marketing literature, this paper sets out to explore how SME exporters achieve resilience through strategic choices related to sourcing and manufacturing during global disruptions.Design/methodology/approachThe study adopts a qualitative, multiple-case-studies approach to identify the key strategic drivers and contextual factors influencing SME exporters’ supply chain resilience. Our study adopted an SME exporter perspective and featured an unprecedented global supply chain disruption context. New Zealand (NZ) was chosen as the key home country context for the participants of this study. Five NZ SME exporters were selected for our case studies.FindingsThis study redefines buffering, bridging and a mixed strategy for an SME exporter facing global supply chain disruptions. SME exporters with high situational control are likely to pursue a buffering strategy that reduces their resource dependence on foreign suppliers and their reliance on information support from these suppliers. In contrast, when the firm perceives little control over the supply chain, it will decide upon a bridging strategy that aims to build solid relationships with supply chain partners. Exporters opt for a mixed strategy when they do not see themselves clearly in high or low situational control.Research limitations/implicationsAs global disruptions arise and evolve, supply chain uncertainty and exporters’ sense of situational control manifest differently across industries, product lines, markets and sourcing countries. It is essential to understand that firms can choose a mixed strategy based on these supply chain conditions. While our study was fortuitously timed, conducting multi-country studies could provide more comparative insights that transcend national borders. Additionally, our study did not examine organizational and capability-based factors. Future research may benefit from exploring how an SME exporter develops strategic capabilities to achieve resilience over its lifetime.Practical implicationsBusinesses see survival as the most pivotal concern during a global supply chain disruption. Many companies have had to make on-the-spot decisions about whether they should shift or redesign their supply chains in the middle of a global disruption. There is no “best strategy” for an SME exporter to take. Rather, managers should make strategic decisions based on how much control or influence they have over a particular part of their supply chain. The level of control is determined by the SME exporter’s overall resource dependencies and information needs in particular parts of their supply chain.Originality/valueWe adopt resource dependence theory and information processing theory to guide our study and place exporters' situational control in the centre of drivers to firms' strategic choices during global disruptions. We make a novel attempt to incorporate the contextual conditions of the COVID-19 pandemic into the theorization of supply chain resilience. We make managerial recommendations to help SME exporters navigate global supply chain disruption challenges.

A Nondestructive and Rapid Method for the Determination of Triamterene Content by Transmission Raman Spectroscopy Combined with Chemometrics

Background: Triamterene is a potassium-conserving diuretic mainly used in the treatment of edematous diseases. Although there are some methods to measure the content of triamterene, the existing methods have the characteristics of sample destructiveness and low flux, making it difficult to meet the needs of online monitoring. In recent years, transmission Raman spectroscopy (TRS), as a new technology for the determination of drug content, has emerged as being nondestructive and rapid and provides a new method for the determination of triamterene content. Objective: In this study, we used transmission Raman spectroscopy combined with partial least squares (PLS) approaches to establish a six concentration levels model for measuring the content of triamterene. The model was applied to determine whether tablets are commercially available. Method: Firstly, TRS was used to collect the spectra of the principal components and mixed excipients of triamterene, and the feasibility test was carried out. Secondly, six concentration levels were determined by the design of experimental (DOE), and hand-made tablets were prepared to obtain corresponding spectra. The content prediction model was established by the PLS method, and the content of triamterene was determined by high-performance liquid chromatography (HPLC) to correct the model. Finally, the model was applied to the determination of triamterene in commercially available tablets. Results: The results showed that the established model was successfully applied to the determination of triamterene. The values of RMSEC (0.0089783) and RMSECV (0.0097241) of the final model were very low and close to each other. The relative error of 12 hand-made tablets predicted by this model was less than 5% compared with the results determined by HPLC. In addition, in the process of applying the model to the determination of the content of commercially available tablets, the accuracy of the model can be significantly improved by adding the spectrum of commercially available tablets. The corrected model was used to determine the content of triamterene in two commercially available tablets. The results showed that the relative error was less than 5%. Conclusion: We described a new strategy to analyze the content of active pharmaceutical ingredients (API) in triamterene tablets by TRS with PLS. The established model has the advantage of non-destructive and rapid quantitation, which can provide a new method for real-time monitoring of production lines and promote the development of process analytical technology (PAT).

HubNet: An E2E Model for Wheel Hub Text Detection and Recognition Using Global and Local Features.

Automatic detection and recognition of wheel hub text, which can boost the efficiency and accuracy of product information recording, are undermined by the obscurity and orientation variability of text on wheel hubs. To address these issues, this paper constructs a wheel hub text dataset and proposes a wheel hub text detection and recognition model called HubNet. The dataset captured images on real industrial production line scenes, including 446 images, 934 word instances, and 2947 character instances. HubNet is an end-to-end text detection and recognition model, not only comprising conventional detection and recognition heads but also incorporating a feature cross-fusion module, which improves the accuracy of recognizing wheel hub texts by utilizing both global and local features. Experimental results show that on the wheel hub text dataset, the HubNet achieves an accuracy of 86.5%, a recall of 79.4%, and an F1-score of 0.828, and the feature cross-fusion module increases the accuracy by 2% to 4%. The wheel hub dataset and the HubNet offer a significant reference for automatic detection and recognition of wheel hub text.

Comparative study of caná – a fermented sugarcane beverage produced from culms with and without bark

Cachaça is a Brazilian beverage produced by distilling fermented must from sugarcane juice, with an alcohol content ranging from 38% and 48% v/v. Currently, thousands of “Alembics” (small-scale destilleries located in agricultural environments) produce cachaça at a high standard of quality with both national and international recognition. These producers possess the infrastructure, knowledge, and expertise required to introduce Caná (a fermented sugarcane beverage) into their production line. According to Brazilian legislation, the alcohol concentration for this beverage is defined to be within the range of 4% to 14%. Being only fermented, Caná retains the phenolic compounds from the juice, which have a high nutritional value. Furthermore, following the example of the European Community, there are prospects for Brazil to implement lower tax rates for beverages produced in small-scale factories with a low alcohol content. This context means that Caná is a significant option for enhancing the competitiveness of Alembics in both the Brazilian and international markets. In this work, a comparison was made between Caná produced from juice obtained from intact stalks and the core (after removing the woody wall). The sensory results were markedly different, and there is still a need for some operational adjustments. Both beverages were positively evaluated, which confirms the prospect of a wide range of colors and flavors for Caná resulting from the use of different varieties of sugarcane, and there exists the possibility of numerous adjustments in operational procedures.

Chemical Characterization and Temporal Variability of Pasta Condiment By-Products for Sustainable Waste Management.

Sustainable waste management is an extremely important issue due to its environmental, economic, and social impacts. Knowledge of the chemical composition of the waste produced is a starting point for its valorization. This research focuses, for the first time, on the by-products of pasta condiment production, starting with their characterization. In particular, the presence of potential bioactive compounds and their variability over time have been studied. The latter aspect is crucial for the subsequent valorization of these by-products. In addition to acidity and total phenolic content, an untargeted strategy was adopted, using spectroscopic and chromatographic techniques coupled with chemometrics, to study waste samples coming from four single condiment production lines, i.e., Genoese pesto, tomato, ricotta, and ragù sauces. The presence of lycopene, polyphenols, and several valuable volatile compounds was highlighted. Their presence and relative amounts vary mainly according to the presence of tomatoes in the sauce. The results obtained at different storage times (after 0, 7, 10, and 15 days) showed that the samples studied, despite having similar chemical characteristics, underwent changes after one week of storage and then presented a relatively stable chemical profile. A general decrease is observed after 7 days for almost all the chemical variables monitored, so careful planning within the first days is required to obtain a high recovery.

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.