Production Line Research Articles

Upgrade and Renovation of Steel Billet Scale in front of High-speed Wire Furnace

This article briefly introduces the weighing device for steel billets in front of the heating furnace of the high-speed wire rod unit, and analyzes and summarizes the problems existing in the original weighing device for steel billets in production and use. Based on on-site installation conditions, design a new weighing method, match a large range weighing sensor, upgrade the automation control of the weighing device, and remotely transmit the billet weighing data to the MES system of the group. The automatic, stable, reliable, and accurate measurement of steel billet raw materials has been achieved, providing important guarantees for the accurate measurement of production line billet and product yield.

Design and application of virtual simulation teaching platform for intelligent manufacturing

Aiming at the practical teaching of intelligent manufacturing majors faced with lack of equipment, tense teachers and other problems such as high equipment investment, high material loss, high teaching risk, difficult to implement internship, difficult to observe production, difficult to reproduce the results, and so on, we take the electrical automation technology, mechatronics technology and industrial robotics technology majors of intelligent manufacturing majors as an example, and design and establish a virtual simulation teaching platform for intelligent manufacturing majors by using the cloud computing platform, edge computing technology, and terminal equipment synergy. The platform includes six major virtual simulation modules, including virtual simulation of electrician electronics and PLC control, virtual and real combination of typical production lines of intelligent manufacturing, dual-axis collaborative robotics workstation, digital twin simulation, virtual disassembly of industrial robots, virtual simulation of magnetic yoke axis flexible production line. The platform covers the virtual simulation teaching content of basic principle experiments, advanced application experiments, and advanced integration experiments in intelligent manufacturing majors. In order to test the effectiveness of this virtual simulation platform for practical teaching in engineering, this paper organizes a teaching practice activity involving 246 students from two parallel classes of three different majors. Through a one-year teaching application, we analyzed the data on the grades of 7 core courses involved in three majors in one academic year, the proportion of participation in competitions and innovative activities, the number of awards and certificates of professional qualifications, and the subjective questionnaires of the testers. The analysis shows that the learners who adopt the virtual simulation teaching platform proposed in this paper for practical teaching are better than the learners under the traditional teaching method in terms of academic performance, proportion of participation in competitions and innovative activities, and proportion of awards and certificates by more than 13%, 37%, 36%, 27% and 22%, respectively. Therefore, the virtual simulation teaching platform of intelligent manufacturing established in this paper has obvious superiority in solving the problem of "three highs and three difficulties" existing in the practical teaching of engineering, and according to the questionnaire feedback from the testers, the platform can effectively alleviate the shortage of practical training equipment, stimulate the interest in learning, and help to broaden and improve the knowledge system of the learners.

The effect of combining primers and cements from different cement systems on the bond strength between zirconia and dentin

ObjectiveThe aim of this study was to evaluate the influence of combining primers and cements from two different resin cement systems on the microtensile bond strength (μTBS) between zirconia and human dentin.Materials and methodsA total of 120 specimens of zirconia cemented to dentin were allocated into eight groups based on cement type (RelyX Ultimate or Panavia V5) and primers (Tooth Primer, Clearfil Ceramic Primer and Scotchbond Universal Adhesive) combinations, applied to dentin or ceramic surfaces. Following artificial aging with 5000 thermocycles, μTBS tests were conducted. Statistical analysis was performed using One-way ANOVA and Tukey’s post hoc tests (p ≤ 0.05), and failure modes were assessed.ResultsThe Panavia V5 cement system demonstrated the highest bond strength (19.4 ± 4.4 MPa), significantly higher than the other groups except when RelyX cement was used with Panavia primers (16.9 ± 3.7 MPa). Cohesive fractures within the cement layer were the predominant failure mode.ConclusionsThe combination of primers from different adhesive cement system brands may significantly affect the bonding effectiveness. Therefore, using products from a single product line of the same adhesive cement system, and following the manufacturer’s recommendations for indications and use, is crucial for a more predictable clinical outcome.

A Seq2Seq transformation strategy for generalizing a pre-trained model in anomaly detection of rolling element bearings

The monitoring of the health of Rolling Element Bearings (REBs) in the rolling mill process was recently automated through signal processing and machine learning technologies. However, large data sets are required, and existing methods require precise labels. Moreover, the scarcity of equipment anomalies challenges their practical identification. In addition, machine learning approaches require human resources to manage machine learning models because all REBs in the production line are not identical. Thus, we propose herein a Seq2Seq Transformation Strategy inspired by the accurate performance of such the methods in Natural Language Processing (NLP) and Computer Visioning (CV). Our strategy aims to use an anomaly detection model trained on one signal to detect anomalies in other signals, reducing the need for model management in practical scenarios. Specifically, we considered time series vibration signals collected from the REBs to be analogous to linguistic data, which are sequential and time variant. In essence, our strategy generates synthetic data from a vibration signal represented as the target domain by using a transformer. Subsequently, a model trained by another signal called the source domain is employed to detect the conditions of the target domain. The goal of transformation is to minimize the differences between the distributions of the synthetic and source data. We assumed that the target conditions could be identified from the synthetic data by the source model if the detection model trained on the source domain was precise. Our strategy was experimentally evaluated on six large, unlabeled data sets collected over 1 year from a rolling mill production line in a steel factory. The strategy significantly improved the accuracy of anomaly detection, which had initially been unacceptable when the source model was directly applied to the detection of the target conditions.

Evaluation of Copper Sulfate Electroplating Solution and Film Using a New Method of Fluorescence Measurement

Control of plating bath conditions is an important issue on production lines. In this research, we developed a method for measuring and evaluating plating solutions using Janus Green B (JGB), a common additive, as a probe. It was discovered that when electricity was applied to a copper sulfate electroplating solution containing JGB as an additive, red fluorescence was generated. This is because the azo group of JGB is reductively cleaved by monovalent copper (Cu(I)) to produce a phenazine dye with red fluorescence. The fluorescence intensity depended on the energization current and time and was proportional to the concentration of JGB added. The fluorescence intensity indicates the rate of decrease in the concentration of JGB and can predict the deterioration of the quality of the plated film produced with the solution in a very early stage, and especially the occurrence of unevenness. Fluorescence is a simple and sensitive method of measurement that facilitates on-time and in situ measurement and evaluation of plating baths at the production site and is a useful method of bath management.

NON-ANALYTICAL DESIGN OF THE PI CONTROLLER OPTIMAL PARAMETERS FOR A CONTINUOUS LINE

The multiple motor drives are complex and coupled MIMO nonlinear systems. Due to the complexity of their mathematical models the development of effective control systems is quite complicated task. This paper presents the design of optimal control based on the quadratic optimality criterion for the central section of the continuous production line using soft computer methods when fuzzy model of the controlled system has been used. In this case, the proposed method demonstrates non-analytical design of the optimal parameters of PI controller with emphasis on minimal knowledge on the controlled system. The realized experimental measurements on a continuous line laboratory model confirmed the effectiveness and the good dynamic properties of the optimal continuous line controller and also its applicability to others MIMO nonlinear dynamic systems.

Modeling of Automobile Assembly Line Performance Using ARENA Simulation Software

Due to the intense competition in today's business environment, companies must continuously analyze and enhance their existing manufacturing systems. Discrete event simulation, which is especially helpful for simulating queuing systems, involves describing the system as it evolves. The building and study of a simulation model of an existing production line are the main topics of this paper. The findings demonstrate that the current system's throughput is low due to bottlenecks, extended processing times at workstations, and inefficient resource utilization, all contribute to productivity losses in automotive assembly lines. This paper aims to evaluate the efficiency of the production line performance of automobile assembly lines using Arena modeling and simulation. Data provided by the company's management is utilized to calculate the processing time and standard time for each step in the production line. Additional information is gathered through direct observation of the assembly line. A car assembly manufacturing line was selected as a case study, and Arena 16.0 software was employed for basic modeling and analysis to achieve these objectives.

Code Generator Development to Transform IFML (Interaction Flow Modelling Language) into a React-based User Interface

Model-Driven Software Engineering (MDSE) is a software development approach that uses the Model to be the main actor of the development. MDSE can be applied to User Interface (UI) Development so that a model for the UI can be built, and then a transformation can be made to turn it into a running application. In this research, we develop UI Generator to support UI Development with the MDSE approach. This UI Generator can also support UI Development in Software Product Line Engineering (SPLE) paradigm. The UI is modeled with Interaction Flow Modeling Language (IFML) diagram. Then The IFML diagram is transformed into React-Based UI by the UI Generator. The UI Generator is developed with Acceleo on Eclipse IDE to transform IFML into React Code with the transformation rules defined in this research. The UI generator is also enriched with display settings and static page management to address user customization needs. The experimental results show that the UI Generator can generate a functional website. Besides evaluating the working product, UI Generator is evaluated qualitatively well based on six quality criteria as an SPLE supporting tool.

Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network

The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing.

Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.

Quantitative Measurement and Evaluation of High-Resolution Ultrasonic Sound Fields using a Novel Automated Ultrasonic Immersion Scanner

Ultrasonic probes are an integral part of the automated ultrasonic non-destructive testing and evaluation (NDT&E) systems to detect and size the defects in a wide range of structural materials in production lines. A complete quantitative assessment of the performance characteristics of an application-specific UT probe is needed to be evaluated on the basis of the international standard DIN EN ISO 22232-2. This requirement of full assessment not only improves the quality assurance of the manufactured probes by evaluating the acceptance criteria but also provides the useful technical information to the end-user to optimize the automated ultrasonic testing on-site. In addition, the evaluation of probe characteristics should be carried periodically throughout their service life. In this work, we developed a novel high-precision ultrasonic immersion scanner to evaluate the full ultrasonic probe characteristics which include the squint angle measurement in three different reference planes (XY, XZ, and YZ), RF-signal and its frequency spectrum at water-steel interface, focal distance, focal range, focal width, focal length and also the angle of beam divergence in different planes of the sound field distribution. In the newly developed UT immersion scanner, for the first time, we successfully integrated the high-precision Hexapod with six axes to achieve a few micrometer (~15 µm) spatial-resolution in the measured sound field patterns and a good angular resolution (~0.05°) in the squint angle measurement. In addition, we used a verified UT instrument in accordance with the standard DIN EN ISO 22232-1 to obtain more accurate amplitude values (±0.5 dB) while scanning a test block. The automated scanning, data acquisition, evaluation, visualization and the automated test report generation are carried based on the standard DIN EN ISO 22232-2. The measured sound beam characteristics of both focused and non-focused application-specific ultrasonic probes with center frequencies ranging from 0.2 - 15 MHz using the pulse-echo technique on a 3 mm half-ball steel reflector are presented. In addition, the measured sound beams of an application-specific 5 MHz multi-element transmit-receive (TR) probe using the 3 mm flat-bottomed-hole (FBH) reflector in a steel reference plate are discussed. Finally, the quantitative analysis of the measured sound field results and the acceptance criteria in accordance with DIN EN ISO 22232-2 are discussed.

On Liangzhu Culture Tremolite-Tempered Pottery: Social complexity, logistical networks and cross-craft interaction in Neolithic China

This investigation delves into the utilization of tremolite, a rare mineral, as a pottery temper for cooking vessels of the late Neolithic lower Yangtze Liangzhu culture. Raman spectroscopy analysis of 165 mineral-tempered pottery sherds from the Dazemiao site revealed that tremolite was the predominant tempering material for tripods (ding), whereas a mixture of feldspar and quartz was used for open-topped vats (da-kou-gang). SEM images indicate that the tremolite temper exhibits relatively coarse fiber structure, corresponding to one of the nephrite materials used for the Liangzhu jade. It is suggested that the Liangzhu miners reallocated materials according to nephrite quality, formulating different product lines, including different types of jade objects and pottery temper. Additionally, the utilization of tremolite temper persisted throughout the entire Liangzhu culture. The presence of tremolite-tempered pottery in regions without indigenous tremolite mines necessitated robust logistical networks for the transportation of these bulky materials. The tremolite temper not only reconfirms the strong ‘jade culture’ of Liangzhu, but also provides evidence of extensive socio-economic exchanges between different geographical regions on the Yangtze River delta.

Controllable Fabrication of Silicon Solar Cells with Inverted or Upright Pyramid Structures by a One-Step Copper Catalysis Method

Silicon has garnered significant attention as the primary material for solar cell preparation. Traditional alkaline etching solutions are limited to creating an upright pyramid structure on monocrystalline silicon surfaces. However, research indicates that an inverted pyramid structure exhibits superior light-trapping properties compared to the upright pyramid structure. In this study, we employed a one-step copper ion metal-assisted chemical etching process to fabricate an inverted pyramid structure on monocrystalline silicon wafers. This method allows for the customization of either inverted or upright pyramid structures by adjusting the concentration of specific solution components. Characterization of the textured silicon wafers reveals that the inverted pyramid structure exhibits lower reflectivity than both the upright pyramid structure and polished silicon. By integrating this texturing technique into the solar cell production line, we successfully produced solar cells with both inverted and upright pyramid structures. Evaluation of various solar cell parameters demonstrates that the inverted pyramid structure outperforms the upright pyramid structure, showcasing lower reflectivity and higher photoelectric conversion efficiency.

Dynamic synchronization optimization of beef atmosphere packaging system

Modified atmosphere packaging (MAP) of beef is a key tool to ensure its post-production shelf life. However, it is still a technical challenge to realize intelligent, dynamic, modified-atmosphere packaging of beef products on the production line to improve production and packaging efficiency. In this paper, we propose a dynamic and synchronized modified atmosphere packaging optimization strategy for beef based on admittance control. The purpose of synchronized, accurate, and efficient modified-atmosphere packaging of beef is achieved. Firstly, particle swarm optimization was designed to optimize the conductance parameters, which did not achieve good optimization results, so a whale optimization algorithm based on inertia weights as well as teaching strategy improvement was designed for optimization, and simulation analysis was carried out. The most important parts of the packaging process were also tested, such as the effect of synchronous following motion, the stability and ability to block interference, the accuracy of the Cartesian Coordinate Robot's repeated positioning, and the system's overall operation performance. The results show that the synchronous following error of the system is ±1%, and after the optimization of the improved whale optimization algorithm, the conduction model is reduced in both overshooting amount and regulation time, which improves the performance of the closed-loop following motion model. And the beef dynamic modified atmosphere packaging system, with the application of the algorithm, can complete the modified atmosphere packaging work stably, accurately, continuously, and efficiently.

Building Breastfeeding Research Relations and Beyond: An Interview With Fiona Dykes.

Professor Fiona Dykes is Professor Emerita of Maternal and Infant Health at the University of Central Lancashire in the United Kingdom (UCLAN). Fiona has a particular interest in the global, sociocultural, and political influences upon infant and young child feeding practices; her methodological expertise is in ethnography and other qualitative research methods. She founded the Maternal and Infant Nutrition and Nurture Unit (MAINN) in 2000 which she led until she retired from her full-time professorship in 2020. Fiona established the associated MAINN Conference in 2007. The MAINN conference is a 3 day, international, peer reviewed event held bi-annually in the United Kingdom and, more recently, in alternate years overseas (Sydney, Australia; Falun, Dalarna, Sweden; and Florida, United States). The conference draws together key researchers in the field of infant and young child feeding from around the world. Fiona was a founding member of the journal Maternal and Child Nutrition. She is author of Breastfeeding in Hospital: Mothers, Midwives and the Production Line (Routledge) and co-author, with Dr Tanya Cassidy, of Banking on Milk: An Ethnography of Donor Human Milk Relations (Routledge). She is also joint editor of several books including Infant and Young Child Feeding: Challenges to Implementing a Global Strategy (Wiley-Blackwell) and Ethnographic Research in Maternal and Child Health (Routledge). This interview was conducted on April 20, 2023, by Dr. Tanya Cassidy, and is based on a verbatim transcription and edited for readability.TC = Tanya Cassidy; FD = Fiona Dykes.

The Impact of Serialisation on Operational Efficiency and Productivity in Irish Pharmaceutical Sites.

Technology enabling drug serialisation technology was introduced by regulators to enhance security in pharmaceutical supply chain and protect drugs from infiltration by falsified and substandard medicines. The introduction of systems for serialisation required huge financial outlays manufacturers of pharmaceuticals. This study investigated the impact of serialisation on the operational efficiency and productivity in Irish pharmaceutical sites. A qualitative study was conducted with 11 manufacturing sites in Ireland. The participating companies operated a total of 114 pack-lines, representing approximately 65% of the automated packing lines in the country. The study found that serialisation had a negative effect on packaging production line OEE and line availability and on the individuals cost per unit of packaged pharmaceuticals. The research results estimated that the capital costs of serialisation were four times greater than those estimated by the regulators. There was a 4.1 cents average cost per pack for serialisation with high volume sites reporting an annual cost of serialisation of up to €4.5m per annum and a 2.7% increase in the average cost of goods sold. A pattern whereby where many pharmaceutical manufacturers are transitioning from smaller batch production and moving toward larger batch production sizes in order to increases efficiencies was identified. The research also proposed the use of a serialisation depreciation factor as a method to determine the impact of serialisation on the cost of goods sold. This is the first study of its kind into the cost of serialisation from a manufacturer's viewpoint and studying the effects of serialisation on productivity, line availability and operational efficiency.

Modular language product lines: concept, tool and analysis

AbstractModelling languages are intensively used in paradigms like model-driven engineering to automate all tasks of the development process. These languages may have variants, in which case the need arises to deal with language families rather than with individual languages. However, specifying the syntax and semantics of each language variant separately in an enumerative way is costly, hinders reuse across variants, and may yield inconsistent semantics between variants. Hence, we propose a novel, modular and compositional approach to describing product lines of modelling languages. It enables the incremental definition of language families by means of modules comprising meta-model fragments, graph transformation rules, and rule extensions. Language variants are configured by selecting the desired modules, which entails the composition of a language meta-model and a set of rules defining its semantics. This paper describes: a theory for checking well-formedness, instantiability, and consistent semantics of all languages within the family; an implementation as an Eclipse plugin; and an evaluation reporting drastic specification size and analysis time reduction in comparison to an enumerative approach.

Parametric investigation of the effects of variables controlling thermal characteristics during continuous and high-speed cold stamping processes with active cooling structures

Knowledge of the thermal phenomena that occur during stamping is important for improving the design and performance of stamping die thermal structures. A new mathematical full-scale model for energy and carbon flows is established based on operational data from a cold stamping production line in China. Moreover, numerical simulations of the thermal-fluid–solid coupling field for a cooling medium in an asymmetrically heated rectangular channel with high heat flux and strong wall superheating are carried out with a suitable CFD solver. The results are discussed in detail and compared with theoretical calculations, and the deviation of the surface heat transfer coefficient is ± 11.9 %. Emphasis is placed on parametric effects of variables controlling thermal characteristics on cold stamping with an active cooling structure. The effects of heat flux, and ambient temperature on temperature rise, pressure drops, and heat transfer coefficients are limited less than 7 °C, 167 Pa and 45.11 % under specific operating conditions, respectively. While the heat transfer coefficient and pressure drops are increased hundreds of times with inlet velocity. The fluid cooling performance, threshold value, and approaches for heat transfer enhancement are obtained. Finally, the CO2 emissions rates is assessed as approximately 1.8 g per piece associated with stamping operations, as well as major contributors, CO2 reduction potential, and economic impacts. Negative energy consumption can be achieved with active cooling. These results can help to improve production quality in the future application and provide theoretical support for determining the causes of metal sheet tensile cracking accidents.

Enhancing tissue wet pressing performance and dry end material efficiency for cost savings

The steadily growing global tissue paper demand lays a foundation for new technologies targeting tissue production optimization, as well as improved material and time efficiency. Tissue making is an especially energy-intensive field of paper industry, creating unique demands for performance in wet pressing and drying processes to cut energy usage. Intelligent roll solutions offer new tools for tissue making to achieve these goals. These tools aim at improving press nip, moisture, and tension profiles; reeling nip and parent roll hardness profiles; rewinder runnability; and end product web handling characteristics in converting. Intelligent rolls can be utilized in all the main processes and positions on tissue production lines. With these tools, production cost reductions and energy savings can be obtained by optimizing the press nip-to-Yankee cylinder contact, avoiding moisture profile errors requiring overdrying with the Yankee hood and reducing reeling/winding broke under low nip load conditions typical to tissue windups. The intelligent roll system consists of a helically mounted force or temperature sensors, roll covers, measurement electronics, digital radio transmission, and a receiver system connected to a user interface or the mill automation system. What distinguishes these implementations is that no layout changes or added external measurement devices are required, helping to fit into compact tissue machine environments, regardless of the equipment type – traditional, hybrid, or through-air drying (TAD) concept. In tissue processing equipment, the optimal positions for these rolls are press nip rollers, reeling cylinders, rewinder or converting line paper lead rolls, or rewinder winding drums. In addition to these, temperature profile measurements are utilized, with the main application being the sheet temperature profile detection after Yankee drying for moisture profile and drying process optimization.

Active Learning in Feature Extraction for Glass-in-Glass Detection

In the food industry, ensuring product quality is crucial due to potential hazards to consumers. Though metallic contaminants are easily detected, identifying non-metallic ones like wood, plastic, or glass remains challenging and poses health risks. X-ray-based quality control systems offer deeper product inspection than RGB cameras, making them suitable for detecting various contaminants. However, acquiring sufficient defective samples for classification is costly and time-consuming. To address this, we propose an anomaly detection system requiring only non-defective samples, automatically classifying anything not recognized as good as defective. Our system, employing active learning on X-ray images, efficiently detects defects like glass fragments in food products. By fine tuning a feature extractor and autoencoder based on non-defective samples, our method improves classification accuracy while minimizing the need for manual intervention over time. The system achieves a 97.4% detection rate for foreign glass bodies in glass jars, offering a fast and effective solution for real-time quality control on production lines.