Industrial Automation

Collaborative robots are machines that work hand in hand with humans; or as the name suggests, collaborate with them in a specific workspace. These robots are not enclosed in confined safety zones like traditional robots, as they interact very closely with humans. Though this is the case, appropriate measures are captivated while designing these robots considering human safety. These robots are well-versed in adapting to changes and frequent upgrades. They are flexible enough to carry out complex tasks. Due to these abilities, they become a significant asset in the manufacturing field. It’s been many years now since cobots are introduced in the industry sector. So, this is the right time to review various applications of cobots in manufacturing. First, the paper starts with a brief introduction followed by an extensive literature review which was structured after reviewing 76 research papers and articles. It ends with some essential conclusions. This paper discusses the diverse appl...

This essay explores the evolving relationship between automation, artificial intelligence, and human labor, proposing a paradigm shift from efficiency to harmony. Drawing on examples from Romanian industry and global technological trends, the text reframes automation as a collaborative infrastructure that augments human agency rather than replacing it. ERP systems, conversational AI, and robotics are presented as instruments in a contrapuntal score of productivity, where adaptability and meaning become central metrics. Romania is positioned as a strategic actor in this transition, capable of cultivating a civic proof-of-work ethos grounded in trust, competence, and creative foresight.

An advertisement on the lifestyle of youths in Jos metropolis. The study employed quantitative research design method and the data were gathered using a structured questionnaire. Simple random sampling technique was used in selecting the respondents for this study. Bavarians' correlation and linear regression were used to test the impact. The finding of the relationship reveals that there is a positive relationship between unethical practice in advertisement and lifestyle of youths in Jos metropolis as reveals by the r - value of 0.618 and p < 0.01. The result also shows that unethical practices in advertisement have negative impact on the lifestyle of youths in Jos metropolis. Based on the finding, the study recommended that: Unethical practices in advertising such as deceptive, untruthful, sex appeal products, and stereotype should be evaded by the advertising agencies. Regulatory bodies such as the Advertising Practitioners Council of Nigeria (APCON) and other stakeholders in advertising industries should frequently checkmate the use of unethical practices in product advertising. The study suggested that relevant authorities such as, Advertising Practitioners Council of Nigeria (APCON) should emphasize on the use of the APCON Act so as to regulate the excesses of advertisement targeted at youths.

Keyword: advertising, deceptive, puffery, sex appeal, stereotype. unethical

This publication presents an in-depth study on the efficiency, applications, and technological innovations of industrial electric heating elements, focusing on plastic injection molding machines, extruders, and high-performance gastronomic equipment. The research combines a literature review, comparative analysis of technologies (mica, ceramic, infrared, and tubular sheath), real-world case studies from Brazil , energy-saving simulations. Key industrial efficiency problems-such as losses through conduction, convection, and radiation-are discussed, and technical solutions for their mitigation are proposed. The study includes thermal power calculations, performance tables, economic projections, payback analysis, and strategic implications for industrial competitiveness. Results indicate that modernizing heating elements can reduce energy consumption by up to 18 %, increase component lifespan, and improve product quality, making it an essential strategy for industries seeking sustainability and high operational performance.

Artificial Intelligence (AI) has moved beyond being just a buzzword to becoming one of the
most powerful drivers of change in almost every industry. In accounting, an industry historically
associated with numbers, rules, and accuracy, AI is proving to be both a challenge and an
opportunity. This paper explores the growing role of AI in the accounting profession, focusing on its
present applications, future scope, challenges, and implications for professionals and businesses. At
present, AI is already reshaping core accounting tasks. Activities such as bookkeeping, invoice
processing, and expense categorization, which once took hours of manual work, can now be
performed within minutes using AI- powered software. AI also reduces human error, improves
compliance with regulatory requirements, and enables accountants to access real-time financial data.
Additionally, advanced AI systems can detect anomalies and fraud patterns far more efficiently than
traditional methods. The major benefits of AI in accounting include efficiency, cost reduction, better
decision-making through predictive analytics, and improved fraud detection. However, these benefits
come with challenges such as job displacement fears, high implementation costs, cyber-security
risks, and the fact that machines cannot replace human judgment, ethical reasoning, and professional
skepticism. Looking forward, the future of accounting is not about AI replacing accountants, but
about AI and humans working together. The role of accountants is expected to shift from performing
repetitive, clerical tasks to becoming strategic advisors who can interpret AI-generated insights and
help organisations make informed decisions. The integration of AI with other technologies such as
block chain will bring real-time auditing, improved transparency, and more reliable Financial
reporting. This study relies on secondary research from academic articles, professional Accounting
reports, and industry surveys to analyse how AI is shaping the accounting profession. It also provides
suggestions for accountants, firms, and educational institutions to prepare for this transition, such as
developing AI literacy, updating curricula, and adopting cost-effective AI tools. In conclusion, AI represents a turning point in the accounting industry. It is not a threat but an
enabler, provided professionals adapt and learn to collaborate with technology. The future belongs
not to AI alone, but to accountants who embrace AI as a partner in enhancing efficiency, accuracy,
and value creation.

This project employs the FLIR A70 thermal camera to control the temperature within an industrial chamber designated for cooling a CNC lathe. The selected configuration strikes an optimal balance between high thermal resolution (640 × 480 pixels) and adequate field of view, enabling uniform and accurate monitoring of critical thermal zones, which is vital for maintaining machining quality and thermal stability in CNC processes. The camera's compatibility with industrial communication protocols and seamless integration with PLC / Ladder logic enables precise, responsive, and reliable automation of the cooling system. This sensor selection underscores the strategic role of thermal measurement in enhancing operational efficiency, dimensional stability, and process safety.

The article presents an innovative prototype of a bottle packaging machine, framed in the context of industrial development and process automation. The system is designed based on requirements that allow addressing common challenges in small and medium-sized business environments. The proposal stands out for its efficiency and affordability. The system offers an economical alternative to expensive commercial production models. The implementation and testing of the system have shown 100 percent accuracy in counting and packaging bottles, leading
to a reduction of operating costs. The results show that the prototype, which has the capacity to process 16 bottles per minute, through the development of a PID controller synchronizes the arrival time of the bottles in the unloading area. The development cycle of the mechatronic system is complemented by software that, through a communication
application, generates the user interface and production statistics through a client-server architecture using the HTTP communication protocol. The system highlights a comprehensive solution, which has been developed, meeting the specific requirements of industrial automation in bottle packaging.

The Internet is expanding with concepts such as digital technology, the IOT (Internet of things), the 4th Industrial Revolution. This has greatly affected the current automation field, requiring remote monitoring and control over smartphones, tablets and web browsers. The application of SCADA remote control for the production line is one of the practical support tools for companies in operating the production system. This paper is about building up a supervised and controlled SCADA for the production line over the Internet based on PLC S7-1200.

Redundancy techniques based on the combination of multiple diverse communication channels are an established countermeasure to improve performance characteristics of wireless communication systems. Besides parallel redundancy in the space and frequency domain, serial redundancy in the time domain can be utilized. It is known that the parallel approaches can significantly improve performance characteristics like jitter and reliability, when applied for wireless packet-based data transmission. In this work, an investigation on the performance impact of additional time diversity on top of the parallel redundancy approaches is performed. An OPNET simulation model is created and analysed for its performance characteristics.

This study investigates the revolutionary effects of combining PLCs (programmable logic controllers) and artificial intelligence (AI) in industrial automation. The primary goals are to discover how artificial intelligence (AI) improves productivity, sustainability, and quality control in industrial processes and identify problems and potential solutions related to AI-driven PLC systems. Methodologically, the applicability and advantages of AI algorithms, such as machine learning and deep learning, in PLC systems were examined through a thorough analysis of recent literature and case studies. Important conclusions show that integrating AI significantly improved process efficiency, real-time adaptation, and energy optimization. The accuracy of AI-driven solutions in defect identification helps quality control, while AI-driven solutions support cybersecurity measures. The policy implications highlight the necessity for robust regulatory frameworks to direct the moral use of AI in industrial contexts. According to the study's findings, AI-enhanced PLC systems are a significant development in industrial automation that promises increased manufacturing productivity, sustainability, and innovation in the future.

Many different proprietary fieldbuses, each one supporting different standards and targeting particular market requirements, have been installed in the industry in different time periods under different circumstances. The alwaysgrowing need to integrate the control applications of the above fieldbuses makes interoperability in device and fieldbus level, as well as real-timelines, the key issues in the area of control applications. In this paper we consider the use of real-time Linux for the development of an interworking unit that ...

A predictive maintenance approach has developed as a revolutionary means to minimize downtime and operational costs in industrial systems. Machine learning techniques are used in this study to design a robust predictive maintenance framework for potential equipment failures based on relevant operational parameters. Random Forest and XGBoost models were trained and evaluated using the AI4I 2020 dataset and the machine failure prediction is highly accurate. Based on domain expertise, these key features, namely Torque [Nm] and Tool Wear [min], were identified as pivotal indicators since they can provide actionable insights for maintenance teams within the domain. The analysis of the two models shows XGBoost performing better on imbalanced data and predicting minority failure cases. In addition to providing predictive accuracy, the study offers practical deployment strategies, including saving models with joblib, implementing Java GUIs for real-time user interaction and automating workflows using APIs and task schedulers. Moreover, this research bridges the gap between theoretical machine learning models and practical applications and provides a scalable, user-friendly framework for industrial predictive maintenance. These insights and methodologies can optimise resource allocation, enhance decision-making and transition the industry from reactive to proactive maintenance practices. Future work involves integrating real-time data streams from IoT and advanced neural networks to increase system scalability and precision.

Precision agriculture can be revolutionized by incorporating internet of things (IoT) technologies to maximize crop yield, especially for key crops like wheat. The creation and application of an IoT-enabled monitoring system intended especially for wheat farming is presented in this study. The system provides real-time data on important agronomic characteristics, such as soil health, temperature, humidity, and crop growth stages, by integrating a network of soil moisture sensors, weather stations, and remote sensing devices. With the help of the monitoring system, field conditions can be continuously and remotely observed, giving farmers the ability to make datadriven decisions that improve crop output and resource efficiency. The system can reduce input waste and increase output by optimizing irrigation schedules, adjusting fertilizer applications, and detecting early signs of crop stress or disease through real-time data analysis. Significant gains have been shown in production results, farm sustainability overall, and water usage efficiency in field studies carried out in different wheat-growing locations. According to the research, IoT-based monitoring systems can be extremely helpful in modernizing wheat production by offering useful information that results in more exact and environmentally friendly farming methods.

This article presents a practical analysis of electric heating and industrial automation technologies applied to small and medium-sized enterprises (SMEs) in Brazil. Based on over 20 years of hands-on experience, it outlines solutions that enhance energy efficiency, reduce operational failures, and improve competitiveness. The paper also explores digital innovation tools, such as mobile technical applications for thermal calculations, and the integration of automation into diverse production lines.

Automating code generation in manufacturing systems requires Artificial Intelligence (AI) models capable of interpreting textual requirement specifications. One of the main challenges is the absence of publicly available, domain-specific datasets suitable for training such models. This article presents AutoFactory, an open-source dataset that includes manually written and LLM-augmented requirement specifications, annotated by domain experts for Named Entity Recognition (NER) tasks using the BIO format.
AutoFactory enables the extraction and classification of key industrial components, including actuators, pre-actuators, sensors, effectors, and others. These elements correspond to real-world input and output signals in manufacturing systems. The dataset is grounded in realistic scenarios built with Factory I/O and includes 3D scenes, tag tables, and screenshots captured from multiple camera perspectives. To enhance linguistic diversity while preserving semantic intent, all specifications were expanded using large language models and evaluated through semantic similarity analysis (average cosine similarity above 0.92), complemented by manual validation to ensure consistency.
To facilitate the annotation process, a custom labeling tool was developed. It runs locally to preserve data privacy and provides a user-friendly interface. The tool is adaptable to a wide range of NER tasks and allows researchers to customize labels for different domains by adding or modifying tags.
To assess the practical value of the dataset, transformer-based models such as BERT and DistilBERT were fine-tuned on the annotated requirement specifications. The highest-performing model reached an F1-score of 0.95, confirming the dataset’s ability to support accurate identification of key components in manufacturing systems.
This combination of human expertise and AI-based augmentation provides a robust foundation for training AI systems to interpret manufacturing behaviors from text. AutoFactory contributes to ongoing research efforts aimed at enabling intelligent systems to assist in generating control code from requirement specifications, reducing reliance on manual programming in industrial automation.

This article explores the evolution of management theory across three pivotal eras-classical, neoclassical, and modern-and examines how each has influenced contemporary business practices. Drawing on historical examples and personal insights, the piece highlights the enduring relevance of foundational theories and their impact on productivity, leadership, and organisational culture.

Peningkatan produktivitas sangatlah penting bagi perusahaan untuk memperoleh keberhasilan pada proses usahanya. Salah satu contoh peningkatan produktivitas adalah dengan mengevaluasi kinerja fasilitas produksi pada perusahaan. Untuk itu perlu adanya pemantauan terhadap suatu proses produksi tersebut untuk mengetahui kualitas kinerjanya. Salah satu metode untuk memantau hal tersebut adalah Overall Equipment Effectiveness. Overall Equipment Effectiveness atau OEE adalah suatu perhitungan yang dilakukan untuk menentukan nilai efektivitas mesin atau peralatan yang tersedia serta mengevaluasi kualitas proses produksi yang ada di suatu perusahaan serta sebagai indikator performa mesin atau sistem dalam kurun waktu tertantu.
Projek OEE tersebut dilakukan atas banyaknya permintaan klien di PT. Beckhoff Indonesia, sebuah perusahaan internasional yang bergerak di bidang penjualan part elektronik dan sistem otomasi yang mencakup berbagai aspek kehidupan. Perancangan diawali dengan membuat konsep dari sistem OEE yang akan diterapkan. Setelah itu, konsep tersebut diaplikasikan kedalam program bernama Twincat XAE Shell. Dan terdapat beberapa troubleshoot untuk memperbaiki bug dan error pada program tersebut. Variabel dalam rumus OEE diperoleh dari research melalui internet dan hasil diskusi dengan pihak PT. Beckhoff.
Berdasarkan hasil permasalahan yang ada, dihasilkan Sistem berupa untuk monitoring Overall Equipment Effectiveness dengan menggunakan software TwinCAT XAE Shell yang dilengkapi dengan HMI (Human Machine Interface). Sistem monitoring tersebut dapat mendeteksi tingkat OEE, Availibility, Performance, dan Quality dalam suatu perusahaan.

This paper aims to provide arguments for reconsidering recognition of prior learning (RPL) assessment methods. In doing so, theories and arguments around academic and workplace knowledge, the boundaries of disciplines and its perceived supremacy are discussed, which culminate into an RPL model: the third space. The third space aims to be a transdisciplinary and boundary-crossing mechanism to facilitate transfer of knowledge between academic institutions and workplaces. This RPL model takes the reader to the point that two questions are asked: Does the notion of a third space require a change in assessment practices? If so, to what extent should RPL assessment practices change? By utilizing activity theory (Engestrom) and critical theory (Bhaskar), an argument is made that two different activity system such as the academic institution and the workplace could have the same learning objectives, which challenge traditional RPL assessment practices. Two RPL assessment approaches are disc...

This research presents a comprehensive comparative study between ladder logic (LD) and structured text (ST), two prominent programming languages standardized under IEC 61131-3 for programmable logic controllers (PLCs). The study investigates their performance in terms of execution efficiency, readability, debugging time, and maintainability across diverse industrial applications. Data was collected over 12 months from 15 real-world installations using platforms like Siemens S7-1500 and Allen-Bradley Control Logix, and software environments such as TIA Portal and Studio 5000. The findings indicate that LD excels in simple logic operations and rapid development, particularly for electrical professionals familiar with relay logic, whereas ST demonstrates superior performance in complex computations, memory efficiency, and structured data manipulation. ST also offers better maintainability through modularity and code reusability, making it more suitable for large-scale or algorithm-intensive projects. In debugging scenarios, LD proves more effective for logic and I/O issues due to its visual nature, while ST provides better tools for diagnosing communication and calculation errors. A hybrid programming strategy is recommended, leveraging LD for I/O control and safety interlocks, and ST for data processing and algorithmic functions. The study further emphasizes the importance of team composition and training, suggesting that project success often hinges on aligning language choice with developer background and application complexity. By evaluating both qualitative and quantitative parameters, this research provides actionable insights into optimizing PLC programming practices for modern industrial automation.

CAD models using a boundary representation (BREP) are widely used across industrial applications. However, extracting and interpreting information from these models typically requires expert knowledge. In this paper, we present a GraphRAG-based system that lowers this barrier by enabling natural language access to detailed geometric and topological information. The approach builds upon an ontological representation of geometries, which is accessible via the semantic query language SPARQL. Large language models are used to translate user questions into formal queries and generate natural language replies, allowing for flexible, domain-aware interaction with the geometric data. The effectiveness of the system is demonstrated through a systematic evaluation using diverse CAD models and a range of natural language queries. Our system empowers domain experts to explore and analyze complex CAD models without requiring expertise in query languages or ontology design. By reducing the complexity of geometric information retrieval, the GraphRAG-based system enables more accessible, automated, and intelligent use of CAD data in industrial applications.

A predictive maintenance approach has developed as a revolutionary means to minimize downtime and operational costs in industrial systems. Machine learning techniques are used in this study to design a robust predictive maintenance framework for potential equipment failures based on relevant operational parameters. Random Forest and XGBoost models were trained and evaluated using the AI4I 2020 dataset and the machine failure prediction is highly accurate. Based on domain expertise, these key features, namely Torque [Nm] and Tool Wear [min], were identified as pivotal indicators since they can provide actionable insights for maintenance teams within the domain. The analysis of the two models shows XGBoost performing better on imbalanced data and predicting minority failure cases.

In addition to providing predictive accuracy, the study offers practical deployment strategies, including saving models with joblib, implementing Java GUIs for real-time user interaction and automating workflows using APIs and task schedulers. Moreover, this research bridges the gap between theoretical machine learning models and practical applications and provides a scalable, user-friendly framework for industrial predictive maintenance. These insights and methodologies can optimise resource allocation, enhance decision-making and transition the industry from reactive to proactive maintenance practices. Future work involves integrating real-time data streams from IoT and advanced neural networks to increase system scalability and precision.

Currently available toolsets for the development of embedded systems adopt traditional architectural styles and do not cover the whole requirements of the development process, with extensibility being the major drawback. In this paper, a service-oriented architectural framework that exploits semantic web is defined. Features required in the development process are defined as web services and published into the public domain, so as to be used on demand by developers to construct their projects' specific integrated development environments (IDEs). The infrastructure required to build a web service-based IDE is presented. Specific web services are defined and the way these services affect the development process is discussed. Special focus is given on the device model and the means that such a modelling can significantly improve the development process. A prototype implementation demonstrates the applicability and usefulness of the proposed demand-led development process in the industrial automation domain.

The progressive industrialization of composite-built aircraft is putting manufacturing engineers on a steep learning curve. An opportunity exists to use knowledge management tools to capture, share and reuse knowledge over multiple aircraft programs and maintain the constant flow of learning gained during aircraft program delivery. This paper reports on research to develop a knowledge-based cost modelling capability for composite part concept evaluation. The approach integrates a knowledge management environment with a processbased cost estimation tool encoded in very large spreadsheets. In order to achieve this, the complexity of the cost model is managed by modularizing it into simple, re-useable components. The knowledge management environment is then used to manage the knowledge lifecycle, knowledge exploitation and knowledge visibility of those components. The result is a flexible cost modeling tool composed of traceable, verifiable and reliable knowledge elements that provides through-life knowledge support for cost engineering. Validation is achieved through functional application of the solution for composite wing top cover cost modelling.

The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing. The publications within "Advances in Intelligent Systems and Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.

Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables (P = 87%) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.

This practical laboratory report focuses on the automation of traffic lights at a four-lane intersection using a Programmable Logic Controller (PLC). Students are required to connect traffic lights and sensors, as well as develop the control logic for operating the light bulbs. The report outlines the key implementation steps, including identifying inputs and outputs, creating an I/O Allocation Table, and wiring the PLC panel to the traffic light panel. The tasks involve configuring an I/O address table, programming the traffic light switching sequence, incorporating vehicle detection, and integrating pedestrian crossing features. The primary objective of the practical is to provide students with hands-on experience in PLC programming and control system design.

Ultra-wideband (UWB) communication has been the subject of extensive research due to its unique capabilities and potential applications. Some see UWB as an enabling technology for new wireless applications that span from high-data-rate transmission of raw multimedia video to new location-aware, low-data-rate, and low-power communication of sensor and actuator data. Underground mines, which are characterized by their tough working conditions and hazardous environments, require foolproof mine-wide communication systems for smooth functioning of mine workings and ensuring better safety. During the last years, a number of Canadian mining companies have started to deploy modern communications systems in order to automate processes, reduce operational costs and with the objective of increasing safety and productivity. However, many important aspects of UWB communication systems have not yet been thoroughly investigated in this kind of environments. In this paper we give an overview on UWB communication. Also we investigate via measurements and simulations the applications and design challenges for UWB communication and ranging in underground mines.

The technologies used for an expert system development have a vital role and take the design to the next level of innovation. These innovations offer essential help to save lots of energy, resources and conjointly to secure the specified infrastructure. The paper concentrates on the innovative technologies used for building an expert system. An expert system also contains associate Interface within users interact and an Interface Engine that performs data reasoning. The planned design interconnects multiple programming languages, hardware and software parts to build the system.

We propose a methodological framework based on design science research for the design and development of data and information artifacts in data analysis projects, particularly managerial performance analysis. Design science research methodology is an artifact-centric creation and evaluation approach. Artifacts are used to solve real-life business problems. These are key elements of the proposed approach. Starting from the main current approaches of design science research, we propose a framework that contains artifact engineering aspects for a class of problems, namely data analysis using machine learning techniques. Several classification algorithms were applied to previously labelled datasets through clustering. The datasets contain values for eight competencies that define a manager’s profile. These values were obtained through a 360 feedback evaluation. A set of metrics for evaluating the performance of the classifiers was introduced, and a general algorithm was described. Our i...

In this work, we present an intelligent system developed for energy consumption distributed control and monitoring. It supports real time cloudbased data visualization of power profiles from different areas, so as to optimize overall power consumption. The local intelligent processing unit (LIPU) that control the different environments is described. The communication network model that allows connecting multiple LIPUs to apply power consumption policies defined by the organization is analyzed, and the unit's capabilities in relation to cloud connectivity and realtime processing are considered through a theoretical scalability study. Finally, we describe relevant implementation features in the context of "Facultad de Informática" of the "Universidad Nacional de La Plata" (Argentine).

The rapid advancement of wireless communication technology has paved the way for the fifth-generation (5G) mobile network, which promises ultra-low latency, high data rates, and massive device connectivity. These capabilities are vital for the Internet of Things (IoT), which connects billions of devices to collect and exchange data. This paper explores the synergy between 5G and IoT technologies, emphasizing their integration in enabling smart applications. It further examines the benefits, challenges, and real-world use cases such as smart cities, healthcare, and industrial automation. The study concludes with insights into the future scope of 5G-enabled IoT systems.

MTConnect is an open and extensible protocol designed for the exchange of data between shop floor devices and software applications. MTConnect allows manufacturers to facilitate retrieval of information and data from factory devices, such as machine tools, sensors, and controllers. Currently, MTConnect users read data from sensors through proprietary sensor interfaces using adaptors. The suite of Institute of Electrical and Electronics Engineers (IEEE) 1451 standards defines a set of open, common communication interfaces for sensor networks, including both sensor interfaces and network interfaces. This paper proposes an integration architecture of MTConnect with IEEE 1451 standard-based sensor networks. In the architecture, MTConnect plays a network interface role in the IEEE 1451 standard-based sensor networks via an MTConnect Agent. An adaptor is used to provide the mapping between the MTConnect Agent and the IEEE 1451 sensor network. A prototype system integrating MTConnect with ...

Service-orientation supports the construction of flexible and comprehensive industrial applications. The growing scale and complexity of the applications, however, demand for enhanced self-management functions providing efficient self-adaptation and repair mechanisms. We propose the approach of policy-controlled self-management which has been developed and successfully tested in the context of Web Service based control applications. We use hierarchically structured management policies where high-level policies serve as abstract definitions of management objectives and low-level policies represent concrete rules for resource monitoring und correcting interventions. The definition, analysis, refinement and deployment of the policies are supported by an interactive graphical modeling tool.

An experimental study shows the feasibility of service-oriented architectures for industrial automation and control systems even with respect to lower, real-time dependent control functions. For that purpose, general SOA-guidelines were refined in order to cover the distribution of control functions between services and the lay-out and management of devicebased sensor, actor and control services. Particular emphasis was placed on the dynamic lease-based binding of services which on the one hand provides flexible and loose coupling of system components but on the other hand has to ensure reliable communication and cooperation. The guidelines were applied to the experimental implementation of a manufacturing cell control system using a real-time version of the Java Runtime Environment. The Device Profile for Web Services (DPWS) was used as basic infrastructure technology. Test and evaluation were performed under distributed simulation of technical processes and devices. We shortly describe DPWS, present the architecture guidelines, outline the experimental control system implementation, and report on its evaluation.

The rapid progression of industrial automation and control systems is driven by the integration of intelligent and interconnected technologies. These advancements enable real-time monitoring, predictive maintenance, optimized resource allocation, and minimal human intervention. This paper explores how these integrated technologies enhance operational efficiency and reliability in industrial systems. Using a combination of literature review, real-world applications, and data analysis, the study highlights the importance of converging AI, IoT, and advanced control frameworks in fostering sustainable, reliable, and efficient automation infrastructures

The data acquisition process has the role of transposing physical phenomena from the real world into analogue and digital electrical signal for further processing, analysis and storage of the measurement data. This paper proposes a data acquisition system based on a Programmable Logic Controller (PLC) for voltage and current monitoring in a DC circuit. Two interfacing methods are presented. The first method consists of interfacing the system by means of a specialized graphical terminal. The second method is realized by connecting the system to a PC terminal trough the serial RS-232 interface.

C()p~ri g hl © IFA( . Inlorl ll;tlioll C()llIrol Prohl t'Tll s in \Ianu t'a cturill g T {'( hTl oiog\ . \Ltdrid . Spain I ~Ho\4 COMPUTER-AIDED PROGRAMMING TOOL FOR ROBOTICS J. Warczynski /lI.Il illllf uj ('rilllru/ ElIg ill f aillg. T eclllll ca / l: lII i'f rslly of PUWOII, Powall. Po/and Abs t ra c t . There is a growing interest in the development of software tools for off -l ine r o bo t programming in the field of robotics aplication. The use of such tools could simplify the time-and-labour-consuming task of robot pr ogram development as compared with classical programming methods . This co uld be particularly promising within a CAD CAM allowing in t egra tio n r o b o ti cs into the c ommon planning and control system. The paper des c ribes su c h a s o ftware package aimed t o assist o ff-line development and debugg i ng rob o t programs as well as model-based evaluation o f robot perf o rman c e wi th respect t o the movement dynamic phenomena. The system opera t es in the framework of three main modes which are modelling , programmlng and simulation . Also the workcell layout and verification of c o n tro l strategies are possible . Ke ywo rds . Ro b o t ; r o b o t system modelling, off-line programming, simulation , r o b o t dynamics .

Abstract- Automation systems offer many possibilities when developing safety and energy efficiency related to resident housing. Automation has been most widely used in public buildings but the use of these systems is constantly generalizing in residential housing. Teaching of building automation is not very common today. In HAMK University of Applied Sciences the degree program in automation and research center AutoMaint are specialized in process- and material handling automation as well as in remote control- and diagnostics applications related to industrial business service. With the increase in the use of building automation more is invested also into developing teaching of building automation. A learning environment for building automation has been created, which can also be used as a web-based learning environment. Key Words- Automation, building, remote laboratory, softPLC..

In this paper, we will introduce a new concept to reduce the non value added process in the manufacturing industry. This concept is based on the combination of transportation and machining tasks with mobile robots. The reason behind this concept is to reduce the waste of production time, cost and effort. To realize this concept, a demonstration system has been built. The first step to accomplish this system is to synchronize an automated guided vehicle (AGV) to a moving object. The position data of the AGV and the moving object are collected by using an optic measurement system and used to control the movement of the AGV. An external control system including a Proportional-Integral-Derivative (PID) controller has been built to control the AGV. Experimental validation was performed using an AGV and an optical measurement system for the proposed control system. The results show that the accuracy of the optimized control system has been improved in comparison with the control system without the use of the PID controller.

The manufacturing sector has undergone a profound transformation due to the integration of smart materials, artificial intelligence (AI), and sustainable engineering practices. These advancements have led to the development of precision-driven, highly efficient, and environmentally sustainable production systems. This paper provides an in-depth analysis of advanced manufacturing techniques, exploring their impact on production efficiency, quality improvement, and environmental sustainability. A thorough literature review highlights the evolution of these technologies before 2023, emphasizing key breakthroughs and challenges. Additionally, the study presents empirical data, flow charts, and visual representations to support the discussion. Future trends and recommendations for sustainable and AI-driven manufacturing are also explored.

This paper proposes a new manufacturing paradigm, we call Cloud Agile Manufacturing, and whose principal objective is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledgeor at least without having to be expertsin managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.