Filling Production Line Research Articles

System architecture for small‐scale personalized production lines based on industrial Internet

AbstractThe rapid development of industrial Internet has resulted in significant changes in industrial manufacturing system architecture. In this study, a system architecture suitable for small‐scale personalized production lines is proposed. The core of this architecture is a three‐layer structure comprising the Internet layer, data layer, and field control layer. The system facilitates the transmission of user‐specific product demands from the Internet to the production line, where the production equipment achieves personalized production through motion control. This system architecture for production lines integrates industrial Internet and smart manufacturing technologies, reducing the complexity of the enterprise system architecture and enhancing the flexibility of the system compared to large‐scale personalized production systems. The effectiveness of this system architecture was validated using a flexible yogurt filling production line and lays a foundation for the transformation of industrial manufacturing from the tradition system to a personalized production.

Machine Vision-based Integrated Inspection System for Beverage Bottles with Multiple Defects

Currently, in the beverage filling production line, the corresponding detection device is usually installed after each production process to detect whether the beverage is defective. This method of installing testing devices in different working positions can classify and eliminate defects of defective products, but it needs to manufacture multiple sets of different types of defective testing machines, which has problems such as high resource consumption and waste of installation space. In order to solve these problems, an expandable multi-functional integrated inspection system has been designed in this paper, which can synchronize defects detection in different processes. The system includes a multitasking detection network based on multi-class decoders. By jointly training the detection network with the collected data set, the results of beverage bottle label segmentation, region of interest (ROI) detection, and code recognition can be obtained simultaneously, thus the system can effectively realize the synchronous detection of different defects such as label and spray code missing, inversion and so on. The system also provides an expandable multitasking interface, which can be extended according to the other actual requirements of the production line. Experimental results show that the system achieves 96% detection accuracy, 18000 bottles/h detection speed and good robustness.

Geometric Feature Mining for the Residual Oxygen Measurement of Pharmaceutical Glass Vials on a Filling Production Line

Oxygen invasion into pharmaceutical glass vials directly affects the quality of medicine, and noncontact inversion of the residual oxygen concentration can effectively determine whether the encapsulated medicine is qualified. Different from studies in an airtight chamber, it is impractical to build a linear inversion model of an open-path optical environment, but this scenario is inevitable for the online residual oxygen measurement of glass-bottled sterile preparations. In this paper, the novel concept of a harmonic characteristic triangle (HCT) is proposed. Then, four groups of available geometric features are discovered in unstable 2^nd harmonic signals, and the optimal features are selected after a quantitative evaluation of their discriminative abilities. Based on the HCT concept, 2-D, 3-D and 4-D nonlinear models are constructed for residual oxygen concentration inversion. Experimental results prove that the proposed multidimensional nonlinear models achieved better oxygen inversion effects, with at least a 20% accuracy increase over traditional models.

Presence of Microbial Contamination and Biofilms at a Beer Can Filling Production Line

Contamination of beer arises in 50% of all events at the late stages of production, in the filling area. This is where biofilms, a consortia of microorganisms embedded in a matrix composed of extracellular polymeric substances, play a critical role. To date, most studies have focused on the presence of (biofilm-forming) microorganisms in the filling environment. Our aim was to characterize the microbial status as well as the presence of possible biofilms at a can filling line for beer by determining the presence of microorganisms and their associated matrix components (carbohydrates, proteins and extracellular DNA [eDNA]). For 23 sampling sites, targeted quantitative PCR confirmed the presence of microorganisms at 10 sites during operation and at 3 sites after cleaning. The evaluation of carbohydrates, eDNA, and proteins showed that 16 sites were positive for at least one component during operation and 4 after cleaning. We identified one potential biofilm hotspot, namely the struts below the filler, harboring high loads of bacteria and yeast, eDNA, carbohydrates, and proteins. The protein pattern was different from that of beer. This work deepens our understanding of biofilms and microorganisms found at the filling line of beer beverages at sites critical for production.

A Novel Methodology for Process Monitoring by Using Deep Belief Network

In recent years deep learning has been broadly used for process monitoring, fault diagnosis, pattern recognition, and image classification. In industrial systems there are many process systems that require ore advanced control technologies. Filling error control is an important but challenging problem in most production systems for a wide spectrum of liquid products. The major challenges behind this control problem include uncontrollable ambient factors, highly diversified products in modern manufacturing plants, limited budget for complex feedback control schemes, etc. To devise a low cost solution that is suitable massive replication, this paper proposes a data-driven approach for filling error control by using Just-In-Time Deep Belief Networks (JIT-DBN). The proposed method aims to construct a local DBN model based on historical data to suggest stopping time for the filling process by collectively considering the fluid viscosity, filling temperature and many other affecting factors. Based on the proposed method, a systematic framework for implementation is further devised. The proposed framework leverages the advantages of edge computing and cloud platforms to present a scalable solution with guaranteed computation efficiency and excellent adaptiveness to highly diversified products. In the validation experiments, both the proposed method and the proposed implementation framework are tested in the real-world filling production line for massive production. It is found that the proposed method can effectively reduce the mean filling errors and the filling uncertainties.

Application of AHP and EIE in Reliability Analysis of Complex Production Lines Systems

It is necessary to grasp the operation state of the production system for scientific scheduling, process improvement, fault analysis, equipment maintenance, or replacement. The matter-element information entropy is proposed to evaluate the health index of the product line, and the parameter self-optimization support vector machine is used to predict the future health index. A new type of three-dimensional cross compound element is established by synthesizing the operation state of equipment, energy consumption, production efficiency, and human factors. The subjective, objective, and joint weights are determined by the analytic hierarchy process (AHP) method, entropy, and the combination weighting method, respectively. The health index is calculated by complex element correlation entropy. The calculations of the beer filling production line show that the combined weighting method is an effective method on the health index calculation and can accurately reflect the actual operation state of the production. Support vector machine (SVM) optimized by multiparameters is established to predict the health index; the simulation shows that Least Squares Support Vector Machine (LSSVM) based on radial basis function (RBF) has prominent prediction effect. It can provide accurate data support for the production and management of enterprises.

A Hybrid Simulation System for Juicing and Filling Production Line

As one kind of high quantity standardized commodities, the fruit juice product is an essential part to our daily life and overall economy. Therefore, it is necessary and important to design an efficient juicing and filling production line. According to deficiencies of traditional design methods, we propose a new design method by introducing virtual design technologies to develop a hybrid simulation system for juicing and filling production line, based on modeling software SolidWorks, virtual reality software EON Studio and application development software C++ Builder. We also describe our design method from aspects of process design and layout design, scene modeling, model import and route configuration, and interactive simulation control. Besides, we compare our design method with one traditional design method. Results show that our method can not only shorten effectively the design period but also improve greatly the design quality of the juicing and filling production line.

Design of the Production Line Control System of Beverage Filling Based on PLC

PLC control program is simple and easy to use, reliable and so on. In the field of industrial automatic control has been widely used. Therefore, some characteristics of using PLC to design the beverage filling production line control system, in order to meet the requirements of control system. Through the introduction of automatic control of beverage filling, made us to have a deeper understanding for the beverage filling industry, also have a further understanding of the word for automatic control.

Identification of Filamentous Fungi Isolated from Aseptic Filling System for Tea Beverages and Their Microbiological Characteristics.

Two filamentous fungi isolated from contaminated samples which were thought to have occurred stochastically in the medium fill test in the aseptic filling production line for tea beverage were identified and studied their microbiological characteristics. The test strains were identified as Arthrinium sacchari (Speg.) Ellis and Chaetomium funicola Cooke. The optimum growth temperature was 20-25°C for A. sacchari M001 and 25°C for C. funicola M002. A. sacchari was able to grow at 5°C. The optimum growth pH was 4.0-8.0 for A. sacchari M001 and 5.0-8.0 for C. funicola M002. Because both strains had low heat resistance, it was suggested that the contamination had occurred after the UHT sterilization process in the medium fill test. When growth behavior was examined in green tea, oolong tea, barley tea and blended tea, both strains showed a delayed or inhibited growth in the catechin-containing green and oolong teas. It was suggested that tea catechin in the beverages could inhibit growth of filamentous fungi for a certain period of time. On the other hand, both strains grew rapidly in barley tea and blended tea that were low in tea catechin content.