Search Results (32)

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article

The technological growth in the automation of manufacturing processes, as seen in Industry 4.0, is characterized by a constant revolution and evolution in small- and medium-sized factories. As basic and advanced technologies from the pillars of Industry 4.0 are gradually incorporated into their value chain, these factories can achieve adaptive technological transformation. This article presents a practical solution for companies seeking to evolve their production processes during the expansion phase of their manufacturing, starting from a base architecture with Industry 4.0 features which then integrate and implement specific tools that facilitate the duplication of installed capacity; this creates a situation that allows for the development of manufacturing execution systems (MESs) for each production line and a fog computing node, which is responsible for optimizing the load balance of order requests coming from the cloud and also acts as an intermediary between MESs and the cloud. On the other hand, legacy Machine Learning (ML) inference acceleration modules were integrated into the single-board computers of MESs to improve workflow across the new architecture. These improvements and integrations enabled the value chain of this expanded architecture to have lower latency, greater scalability, optimized resource utilization, and improved resistance to network service failures compared to the initial one. Full article

Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This paper presents a novel optimization framework, symmetry-aware adaptive Ant Colony Optimization (SA-AACO), designed to resolve key limitations in existing metaheuristic approaches. The proposed method introduces three core innovations: (1) a symmetry-awareness mechanism to eliminate redundant solutions arising from symmetrically equivalent configurations; (2) an adaptive pheromone-updating strategy that dynamically balances exploration and exploitation; and (3) a dynamic idle time penalty system, integrated with time window-based machine selection. Benchmarked across ten IPPS scenarios, SA-AACO achieves a superior makespan in 9/10 cases (e.g., 29.1% improvement over CCGA in Problem 1) and executes 18-part processing within 30 min. While MMCO marginally outperforms SA-AACO in Problem 10 (makespan: 427 vs. 483), SA-AACO’s consistent dominance across diverse scales underscores the feasibility of its application in industry to balance quality and efficiency. By unifying symmetry handling and adaptive learning, this work advances the reconfigurability of IPPS solutions for dynamic industrial environments. Full article

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

This article aims to review the industrial applications of AI-based intelligent system algorithms in the manufacturing sector to find the latest methods used for sustainability and optimisation. In contrast to previous review articles that broadly summarised existing methods, this paper specifically emphasises the most recent techniques, providing a systematic and structured evaluation of their practical applications within the sector. The primary objective of this study is to review the applications of intelligent system algorithms, including metaheuristics, evolutionary algorithms, and learning-based methods within the manufacturing sector, particularly through the lens of optimisation of workflow in the production lines, specifically Job Shop Scheduling Problems (JSSPs). It critically evaluates various algorithms for solving JSSPs, with a particular focus on Flexible Job Shop Scheduling Problems (FJSPs), a more advanced form of JSSPs. The manufacturing process consists of several intricate operations that must be meticulously planned and scheduled to be executed effectively. In this regard, Production scheduling aims to find the best possible schedule to maximise one or more performance parameters. An integral part of production scheduling is JSSP in both traditional and smart manufacturing; however, this research focuses on this concept in general, which pertains to industrial system scheduling and concerns the aim of maximising operational efficiency by reducing production time and costs. A common feature among research studies on optimisation is the lack of consistent and more effective solution algorithms that minimise time and energy consumption, thus accelerating optimisation with minimal resources. Full article

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

The working environment of the coal mine boom-type roadheader is harsh with large blind areas and numerous safety hazards for operators. Traditional on-site or remote control methods do not meet the requirements for intelligent tunneling. This paper proposes a digital twin monitoring system of an EBZ-type roadheader based on mixed reality (MR). First, the system integrates a five-dimensional digital twin model to establish the boom-type roadheader digital twin monitoring system. Second, the Unity3D software (v2020.3.25f1c1) and the MR Hololens (v22621.1133 produced by Microsoft) are used to build a digital twin human–machine interaction platform, achieving bidirectional mapping and driving of cutting operation data. Third, a twin data exchange program is designed by employing the Winform framework and the C/S communication architecture, making use of the socket communication protocol to transmit and store the cutting model data within the system. Finally, a physical prototype of the boom-type roadheader is built, and a validation experiment of the monitoring system’s digital twin is conducted. The experimental results show that the average transmission error of the cutting model data of the twin monitoring system is below 0.757%, and the execution accuracy error is below 3.7%. This system can achieve bidirectional real-time mapping and control between the twins, which provides a new monitoring method for actual underground roadheader operations. It effectively eliminates the operator’s blind areas and improves the intelligence level of roadheader monitoring. Beyond mining, this methodology can be extended to the monitoring and control of other mining equipment, predictive maintenance in manufacturing, and infrastructure management in smart cities. Full article

The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing. Full article

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework’s versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments. Full article

This paper explores the importance of smart manufacturing in the context of Industry 4.0, highlighting the crucial role of Manufacturing Execution Systems (MESs) in facilitating Industry 4.0, particularly in data capture and process management. It is worth noting that Small and Medium Enterprises (SMEs) face several obstacles, unlike large companies that have the resources to adopt these principles. This text explores the challenges that SMEs encounter when adopting Industry 4.0, considering budget constraints and technology transfer difficulties. The potential benefits of such projects are often difficult to measure during the initial stages, but they can facilitate the digital transformation of small businesses. To support this thesis, this paper presents an example of MES implementation in a manufacturing SME, showcasing the creation of a comprehensive data monitoring and industrial performance assessment system. This paper aims to introduce a systematic approach for integrating a Key Performance Indicator (KPI) framework using MESs within an SME. This paper highlights the importance of transitioning from big data to smart data to achieve outcomes in terms of operational efficiency, cost analysis, workload management, resource utilisation, knowledge dissemination, and enhanced operator engagement. Full article

Increasing industrial development and digital transformations have given rise to a technology called Digital Twin (DT) that has the potential to break the barrier between physical and cyberspace. DT is a virtual and dynamic model enabled through a bidirectional data flow that creates high-reliability models with interconnection and fusion between the physical and digital systems for full integration. In smart manufacturing, this technology is increasingly used in research and industry. However, the studies conducted do not provide a definition or a single integrally connected model. To develop the Digital Twin shown in this research, the literature was reviewed to learn about the enabling technologies and architectures used at the industrial level. Then, a methodology was used to obtain the physical process information, create the digital environment, communicate the physical environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real-time to obtain the digital twin supported with augmented reality. The system was implemented in the MPS-500 modular production station that has industrial sensors and actuators. The virtual environment was designed with Blender and Vuforia to create the augmented reality environment. In the proposed methodology, robust devices (field and control level) and low-cost embedded systems were used for the creation and communication of the virtual environment (monitoring and control); for the joint work of these technologies, they were carried out through the use of the following protocols: Open Platform Communications United Architecture (OPC UA), Ethernet, and machine to machine (M2M), with which a communication was achieved between the different levels of the automation pyramid. The results show that the proposed methodology for the implementation of the DT allows bidirectional communication between the physical and virtual environment and can also be visualized with the support of AR, thus providing its characteristics to the proposed DT. Digital Twin is an essential factor in creating virtual environments and improving applications between the real and digital world, establishing a bidirectional communication through the Ethernet protocol, with a communication time of approximately 100 ms. This technology interacts with the virtual environment and performs mappings, thus achieving timely and dynamic adjustment. This improves data management and production and incorporates process simulation and physical control in real-time, allowing to execute and trigger actions in the physical equipment simultaneously. Full article

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems. Full article

As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations. Full article