From Experiment Design to Data-Driven Modeling of Powder Compaction Process

The Open Platform Communication Unified Architecture (OPC UA) is a platform-independent service-oriented architecture, it has been widely used in industrial field network communication for its open, cross-platform, security, and extensible information model characteristics. The OPC UA supports clients/server (C/S) and publisher/subscriber (Pub/Sub) communication modes, using the Pub/Sub mode with the Time-Sensitive Networking (TSN) technology can greatly improve the real-time capability of the OPC UA. In factory field automation communication systems, various OPC UA servers are embedded in controllers, sensors, and actuators to enable the reliable exchange of information among each system with different data streams (i.e., real-time and non-real-time). It is hard to configure and manage such a large number of heterogeneous OPC UA servers. In this work, we propose an OPC UA multi-server aggregation and management architecture for industrial field networks, which supports the automatic discovery and aggregate of OPC UA servers, and a centralized configuration management method for OPC UA communication data streams is also supported. A laboratory-level OPC UA multi-server aggregation management platform is implemented to validate the proposed architecture. The test results demonstrate that the platform can support centralized management and configuration of multiple OPC UA servers and their corresponding data streams through a uniform OPC UA interface.

Investigations on the residence time distribution of a three-chamber feed frame with special focus on its geometric and parametric setups

Objective: The purpose of this study was to investigate the effect of mechanical shear on hydrophobicity of pharmaceutical powder blends as a function of composition and particle size, and to determine the impact on drug release from tablets. Methods: Four powder formulations were subjected to three different shear strain conditions (40 rev, 160 rev, and 640 rev) in a controlled shear environment operating at a shear rate of 80 rpm. A total of 12 blends were tested for hydrophobicity. Subsequently, sheared blends were compressed into tablets at 8 kN and 12 kN in a rotary tablet press. During tablet compression, powder samples were collected after the feed frame and their hydrophobicity was again measured. Results: Results indicated that increase in shear strain could significantly increase hydrophobicity, predominantly as an interacting function of blend composition. Blends with both colloidal silica and magnesium stearate (MgSt) were found to show higher hydrophobicity with shear than other blends. Additional shear applied by the tablet press feed frame was found to change the powder hydrophobicity only in the absence of MgSt. Conclusions: Studies showed that the drug release rates dropped with shear more for the blends with both colloidal silica and MgSt than the other blends. Furthermore, the rate of drug release dropped with a decrease in particle size of the main excipient. Surprisingly, the relationship between the relative increase in hydrophobicity and a corresponding drop in the drug release rate was not found when either MgSt or colloidal silica was mixed alone in the blends.

Open Platform Communications Unified Architecture (OPC UA) is an increasingly important standard for industrial communication, which brings up the need for testing. Still, functional or formal verification of the implementation relies on conformance testing or custom solutions. Model-based testing has been studied and applied for decades, targeting many different technologies. OPC UA already provides a defined serialization format for its interfaces, representing a simple model of the application that could be utilized for testing.In this paper, we present our work on applying model-based testing to OPC UA server applications using the serialized interface description as the model. We introduce a possible toolchain for that approach and, based on that, discuss the possibilities and shortcomings of the interface description as a model for testing.

This study presents optimizations to the Massive Temperature Readout System (MTRS), a cost-effective alternative to traditional Programmable Logic Controller(PLC)-based systems for large-scale temperature monitoring. The improved MTRS design streamlines hardware by eliminating intermediate microcontrollers and communication modules, reducing complexity and potential failure points. A unified multi-threaded C++ application replaces the original multi-process software, enhancing performance and managing sensor data acquisition and Open Platform Communications Unified Architecture(OPC UA) server functions within a single process. Furthermore, a novel sensor reading strategy is implemented, overlapping conversion times to achieve higher system performance and increased channel capacity. The optimized system demonstrates the feasibility of a cost-effective, high-performance temperature monitoring solution for integration with OPC UA based industrial communication infrastructures.

The High Pressure Die Casting (HPDC) process is characterized by a high degree of automation and therefore represents a data rich production technology. From concepts such as Industry 4.0 and the Internet of Production (IoP), it is well known that the utilization of process data can facilitate improvements in product quality and productivity. In this work, we present a concept and its first steps of implementation to enable data management via a data lake for HPDC. Our goal was to design a system capable of acquiring, transmitting and storing static as well as dynamic process variables. The measurements originate from multiple data sources based on the Open Platform Communication Unified Architecture (OPC UA) within the HPDC cell and are transmitted via a streaming pipeline implemented in Node-Red and Apache Kafka. The data are consecutively stored in a data lake for HPDC that is based on a MinIO object store. In initial tests the implemented system proved it to be reliable, flexible and scalable. On standard consumer hardware, data handling of several thousand measurements per minute is possible. The use of the visual programming language Node-Red enables swift reconfiguration and deployment of the data processing pipeline.

In this study, 15 kinds of powders with different compression mechanisms were used in the process of filling-binding substances in tablets with pellets. Applied substances possessed dominant brittle time-independent mechanism or time-dependent viscoplastic, viscoelastic mechanism of compression.Using 6 kN compression force in a single-stroke tablet press during 150 ms of compression, damage to the polymer film and pellet core was found in all formulations. As a result, the authors observed an increase of releasing rate of verapamil hydrochloride (VH). A larger contact area between powders and pellets and connected with this better protective properties were ensured by powders with time-independent compression mechanism (eg, D-sorbitol or D-mannitol). Unsymmetrically applied compression force was a reason for inconsistent densification and insufficient protection of the pellets.Taking into consideration the low rotation speed of the turret (10 rpm) in the rotary tablet press, the total compaction time was much longer than in the single-stroke tablet press. The compression time in the case of the rotary tablet press should be considered as the sum of the precompression (about 130 ms) and main compression (about 280 ms) phase times. Compression force applied by upper and lower punch in the precompression and main compression phase was affected uniformly on the pellets' surface, and when protected against fragmentation, allowed only some slight deformation. The powders in tablet formulation were fragmentized and rearranged independent of their compression mechanisms.It was found that the releasing rate of VH from pellets compressed by rotary tablet press with 6, 12, and 18 kN of compression force was similar to the releasing rate from uncompressed pellets.

Coordination is an important aspect of large scale distributed systems. The Open Platform Communications Unified Architecture (OPC UA) standard series presents an important challenge for coordination in its specifications for server redundancy. This paper discerns the coordination needs of OPC UA server redundancy and presents a solution based on the integration of OPC UA and ZooKeeper. A detailed description of the architecture, data model, and components of the resulting system is given. This is accompanied by a discussion of an implementation based on the open source ZooKeeper and open62541 libraries. The resulting system is shown to be capable of meeting the coordination needs of OPC UA redundancy.

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

Since the latest downturn of the Oil and Gas (O&G) sector, productivity growth has become the priority, which led to the adoption of new key technologies for better performance and lower cost. As part of the 4th Industrial Revolution, these technologies are taking the O&G by storm, forcing hardware manufactures to innovate quickly and create devices with more capabilities. What was impossible a few short years ago is reality today. Managed pressure drilling (MPD) systems are taking advantage of this development by implementing advanced data analytics and hydraulic modeling in programable automation controllers. This approach has several benefits, reliability and full real-time deterministic operation being the major ones. Previously, controlling bottomhole pressure (BHP), the required modeling and analysis were executed in a networked computer at a different physical location. Now it is executed locally in a fully deterministic control system. This includes data acquisition (DAQ), signal conditioning, well bore modeling, analytics and data visualization. This approach mitigates the risk of connectivity issues and makes the system more robust in case of network failure. Furthermore, it simplifies the deployment of the system, as all critical functions are at a single physical location. Another major benefit is an increased resolution of the model in areas of specific interest. For example, a zoom in on the openhole section and a lower resolution in the cased hole section. The flexibility of this approach manifests itself on the deployment side. This system can be installed in several different scenarios, for example as a full system with controls and manifolds, or as a control system only, and integrated into existing rig MPD manifolds and drilling controls. This level of integration creates robust and flexible systems while reducing downtime during operations. This new approach includes the integration of cyber secure communications, an upcoming standard in the O&G industry. The system communicates via the open platform communications unified architecture (OPC UA) protocol – recommended by the Cyber Security community. This new platform integrates advanced data analytics and hydraulic modeling in real-time, coupled with cyber security features via OPC UA protocol directed to automation controllers for managed pressure drilling applications.

With the boom of Industry 4.0 for industrial automation, the data exchange between the different industrial devices has tremendously increased through local (edge) and cloud computing. Nowadays, several communication protocols are available to realize data acquisition in the industry. As a result, understanding the practical capabilities of each communication protocol arises as an essential issue in optimizing the data acquisition and storage in industrial plants. This study aims to develop a software-based test environment to evaluate the performance of two widely used communication protocols within the scope of the Industrial Internet of Things (IIoT). These contemplated protocols are Open Platform Communications Unified Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT). With the help of the developed test environment, several performance metrics, like packet overhead, latency, packet loss ratio, and CPU utilization, are evaluated for different application scenarios where the varying number of clients and subscribers are considered. Derived results indicate the stronger parts of each protocol under varying communication configurations.

The production process chain of lithium-ion battery (LiB) cells consists of several interconnected process steps with many parameters that can influence the final properties and quality of the cells. In the development of cylindrical LiB cells, steps such as winding, cell assembly, filling, and formation play an important role in determining the properties of the intermediate product and the final performance of the battery cell. Continuous and low scrap production in the experimental stages of LiB battery cell production is challenging due to the limited understanding of cause-and-effect relationships. For example, the machine parameters that need to be configured depending on the properties of the input material. As a result, individual tuning of production machine settings for different materials is required. This is particularly critical in research pilot production environments, such as the BMW Battery Cell Competence Centre, where different materials are tested and experimental approaches are required to optimize the manufacturing processes. This paper presents an approach for utilizing data analytics and machine learning with Open Platform Communications Unified Architecture (OPC UA) and Internet of Things (IoT) data pipeline to better understand the relationships between process parameters, input material, intermediate product features and final quality in the production of cylindrical LiB battery cells. In this study, different machine learning algorithms were tested to predict the initial setpoints of the routing path and the gripper values from the parameters of the winding process and the characteristics of the input material in order to obtain an intermediate product with the desired quality. The best results were obtained using Random Forest Regression with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.976.

Using a physical laboratory for Industrial Internet of Things (IIoT) demands significant allocation of financial resources fo r supervision and maintenance post-installation. Consequently, its utilization is often confined to highly guided laboratory practices to safeguard the integrity of industrial controllers, limited to a few workstations within the facilities. These re strictive constraints create a bottleneck for undergraduate students to engage in practical exercises related to automation processes. Our main goal is to provide an open-source, low-cost educational platform for resource-constrained universities which can be easily implemented, configured and connected to mobile networks. The proposed coursework chooses text-based programming to familiarise first-semester technical/college students with programming for industrial automation, and the physical testbed integrates a legacy programmable logic controller (PLC) with an Open Platform Communications Unified Architecture (OPC UA) server, alongside text-based programming to configure mobile communication modems supporting multiple Radio Access Technologies (RATs). The combined approaches serve as a safe option for simple industrial automation trainin g, including access to mobile services.

The integration of Open Platform Communications Unified Architecture into the field level holds potential to reduce manual configuration efforts by consistent information modelling and a uniform communication protocol. This paper discusses initiatives that are active in this context and reflects the status of the current specification work. The review focuses on the achievability of Quality of Service requirements, the adaption of established automation system concepts and the introduction of new concepts for automation systems at the field level. Using a remote input/output application as an example, we investigate the impact of new concepts for low-level system engineering. It is shown that besides a possible reduction of configuration steps, a loose coupling between the automation application, the used network infrastructure and the used devices can be achieved.

Development of a tablet press feed frame lead lag determination model using in-line and off-line NIR measurements