Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0

Large-Scale Cellular Vehicle-to-Everything Deployments Based on 5G-Critical Challenges, Solutions, and Vision towards 6G: A Survey

The proliferation of fifth-generation (5G) networks has opened up new opportunities for the deployment of cellular vehicle-to-everything (C-V2X) systems. However, the large-scale implementation of 5G-based C-V2X poses critical challenges requiring thorough investigation and resolution for successful deployment. This paper aims to identify and analyze the key challenges associated with the large-scale deployment of 5G-based C-V2X systems. In addition, we address obstacles and possible contradictions in the C-V2X standards caused by the special requirements. Moreover, we have introduced some quite influential C-V2X projects, which have influenced the widespread adoption of C-V2X technology in recent years. As the primary goal, this survey aims to provide valuable insights and summarize the current state of the field for researchers, industry professionals, and policymakers involved in the advancement of C-V2X. Furthermore, this paper presents relevant standardization aspects and visions for advanced 5G and 6G approaches to address some of the upcoming issues in mid-term timelines.

Intelligent Warehouse in Industry 4.0-Systematic Literature Review

The development of Industry 4.0 (I4.0) and the digitization and automation of manufacturing processes have created a demand for designing smart warehouses to support manufacturing processes. Warehousing is one of the fundamental processes in the supply chain, and is responsible for handling inventory. Efficient execution of warehouse operations often determines the effectiveness of realized goods flows. Therefore, digitization and its use in exchanging information between partners, especially real-time inventory levels, is critical. For this reason, the digital solutions of Industry 4.0 have quickly found application in internal logistics processes and enabled the design of smart warehouses, also known as Warehouse 4.0. The purpose of this article is to present the results of the conducted review of publications on the design and operation of warehouses using the concepts of Industry 4.0. A total of 249 documents from the last 5 years were accepted for analysis. Publications were searched for in the Web of Science database using the PRISMA method. The article presents in detail the research methodology and the results of the biometric analysis. Based on the results, a two-level classification framework was proposed, which includes 10 primary categories and 24 subcategories. Each of the distinguished categories was characterized based on the analyzed publications. It should be noted that in most of these studies, the authors' attention primarily focused on the implementation of (1) Industry 4.0 technological solutions, such as IoT, augmented reality, RFID, visual technology, and other emerging technologies; and (2) autonomous and automated vehicles in warehouse operations processes. Critical analysis of the literature also allowed us to identify the current research gaps, which will be the subject of further research by the authors.

BelBuk System—Smart Logistics for Sustainable City Development in Terms of the Deficit of a Chemical Fertilizers

Purpose: This paper presents an aspect of asset tracking and storage conditions. This paper aims to fill the gap in the development of Industry 4.0 in terms of fully digital asset tracking to be implemented by medium and large-size manufacturing and logistics facilities. The article presents an innovative technology for the remote monitoring of chemical raw materials, including fertilizers, during their storage and transport from the place of manufacture to the local distributor or recipient. Methods: The method assumes the monitoring and identification of special transport bags, so-called “big-bags,” through embedded RFID tags or LEB labels and monitoring the key parameters of their content, i.e., temperature, humidity, insolation, and pressure, using a measuring micro-station that is placed in the transported raw material. Results: The automation of inference based on the collected information about the phenomenon in question (the distribution of parameters: pressure, temperature, and humidity), and expert knowledge, allows the creation of an advisory system prototype indicating how to manage the measuring devices. Conclusions: No similar solution in the field of monitoring environmental parameters has been implemented in the Polish market. The developed system enables the monitoring of 10,000 pieces of big bags in at least 30 locations simultaneously.

Positioning in 5G and 6G Networks—A Survey

Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.

A Sustainable Productive Method for Enhancing Operational Excellence in Shop Floor Management for Industry 4.0 Using Hybrid Integration of Lean and Smart Manufacturing: An Ingenious Case Study

In industry 4.0, industry individuals implement lean and smart manufacturing to improve shop floor management systems. Shop floor management is used to control operational performance and enhance production within limited constraints. Various shop floor management approaches are used in the present scenario of industry 4.0, and mainly include value stream mapping, total productive maintenance, Internet of Things, artificial intelligence, machine learning, and fuzzy logic. The present research aims to develop an open innovation method to achieve sustainability in shop floor management systems in industry 4.0 by using lean and smart manufacturing concepts. The proposed method has been validated by an enhancement obtained in a real case of the shop floor management system in industry 4.0. The authors are confident that the proposed method would provide sustainability in the shop floor management system within limited constraints in industry 4.0.

A Conceptual Framework for Estimating Building Embodied Carbon Based on Digital Twin Technology and Life Cycle Assessment

Low-carbon building design requests an estimation of total embodied carbon as the environmental performance metric for comparison of different design options in early design stages. Due to a lack of consensus on the system boundaries in building life cycle assessment (LCA), the carbon estimation results obtained by the current methods are often disputable. In this regard, this paper proposes a method for estimating building embodied carbon based on digital twin technology and LCA. The proposed method is advantageous over others by providing (1) a cradle-to-cradle LCA and (2) an automated data communication between LCA and building information modelling (BIM) databases. Because data for the processes in the life cycle are collected via digital twin technology in a standard and consistent way, the obtained results will be considered credible. So far, a conceptual framework is developed based on a comprehensive literature review, which consists of three parts. In the first part, formulas for LCA are given. In the second part, a hybrid approach combining semantic web with a relational database for BIM and radio-frequency identification (RFID) integration is described. In the third part, how to design the LCA database and how to link LCA with BIM are described. The conceptual framework proposed is tested for its reasonableness by a small hypothetical case study.

A Systematic Modelling Procedure to Design Agent-Oriented Control to Coalition of Capabilities—In the Context of I4.0 as Virtual Assets (AAS)

Manufacturing systems need to meet Industry 4.0 (I4.0) guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements’ behavior.

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

This article presents a control system for a cutting tool condition supervision, which recognises tool wear automatically during turning. We used an infrared camera for process control, which—unlike common cameras—captures the thermographic state, in addition to the visual state of the process. Despite challenging environmental conditions (e.g., hot chips) we protected the camera and placed it right up to the cutting knife, so that machining could be observed closely. During the experiment constant cutting conditions were set for the dry machining of workpiece (low alloy carbon steel 1.7225 or 42CrMo4). To build a dataset of over 9000 images, we machined on a lathe with tool inserts of different wear levels. Using a convolutional neural network (CNN), we developed a model for tool wear and tool damage prediction. It determines the state of a cutting tool automatically (none, low, medium, high wear level), based on thermographic process data. The accuracy of classification was 99.55%, which affirms the adequacy of the proposed method. Such a system enables immediate action in the case of cutting tool wear or breakage, regardless of the operator’s knowledge and competence.

A New RFID-Identification Strategy Applied to the Marble Extraction Industry

This paper aims to improve the marble industry production chain by proposing new technological approaches using the Radio Frequency Identification (RFID) systems. The dynamic capabilities of the RFID read-write tags allow the storage of physical characteristics of stone blocks, according to electrical, ultrasound and three-dimensional image characterization tests. These characterization non-destructive tests allow the evaluation of important parameters of the original stone blocks, by analyzing the internal structure of the rocks. Then, these parameters can be stored in databases through RFID-tags, in order to optimize their subsequent cutting and transformation processes. RFID identification technology when integrated into an ethernet communication network enables automatic communication with cutting and processing equipment, building an intelligent industrial platform, integrating PCs (Personal Computers) and PLCs (Programmable Logic Controllers) within an Industry 4.0 environment. Another huge advantage of RFID technology is that it allows full product traceability, namely by enabling the end consumer to reverse the production path. A laboratory prototype was implemented and a detailed analysis and discussion of the obtained functionalities is shown at the end of this paper.

Real-Time Locating System in Production Management

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.

Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review

Green processes are very important for the implementation of green technologies in production to achieve positive sustainability outcomes in the Industry 4.0 era. The scope of the paper is to review how conventional green processes as a part of Industry 4.0 provide sustainability outcomes in manufacturing. The paper is based on the methodology of systematic literature review through the content analysis of literary resources. Twenty-nine studies were included in our content analysis. The results show the main focus of current literature related to Industry 4.0, sustainability outcomes and green processes. The authors present a conceptual Sustainability Green Industry 4.0 (SGI 4.0) framework that helps to structure and evaluate conventional green processes in relation to Industry 4.0 and sustainability. The study summarizes which technologies (big data, cyber-physical systems, Industrial Internet of Things and smart systems) and green processes (logistics, manufacturing and product design) are important for achieving a higher level of sustainability. The authors found that the most often common sustainability outcomes are energy saving, emission reduction, resource optimalization, cost reduction, productivity and efficiency and higher economic performance, human resources development, social welfare and workplace safety. The study suggests implications for practice, knowledge and future research.