Smart Factory Research Articles

Context-Aware System for Information Flow Management in Factories of the Future

The trends of the 21st century are challenging the traditional production process due to the reduction in the life cycle of products and the demand for more complex products in greater quantities. Industry 4.0 (I4.0) was introduced in 2011 and it is recognized as the fourth industrial revolution, with the aim of improving manufacturing processes and increasing the competitiveness of industry. I4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity. In addition, concepts such as Smart Factories are emerging, which use context awareness to assist people and optimize tasks based on data from the physical and virtual world. This article explores and applies the capabilities of context-aware applications in industry, with a focus on production lines. In specific, this paper proposes a context-aware application based on a microservices approach, intended for integration into a context-aware information system, with specific application in the area of manufacturing. The manuscript presents a detailed architecture for structuring the application, explaining components, functions and contributions. The discussion covers development technologies, integration and communication between the application and other services, as well as experimental findings, which demonstrate the applicability and advantages of the proposed solution.

Research on vehicle path planning of automated guided vehicle with simultaneous pickup and delivery with mixed time windows

AbstractThe authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.

A novel trusted hardware-based scalable security framework for IoT edge devices

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA.

A Comprehensive Survey of Security Challenges and Solutions of Industrial Internet of Things (IIoT) Networks

Despite the transformative potential of the Industrial Internet of Things (IIoT) in optimizing industrial operations, significant security and privacy challenges persist, underscoring the need for comprehensive solutions. This paper addresses this research gap by presenting a thorough survey of the security challenges and solutions in IIoT networks. Drawing from the extensive literature on IIoT, we identify and categorize key threats such as malware attacks, data breaches, and supply chain vulnerabilities. Additionally, we explore privacy concerns arising from the vast amounts of sensitive data collected and transmitted by IIoT devices. This analysis highlights the critical importance of encryption mechanisms, access controls, and authentication protocols as fundamental security measures. By synthesizing current research, this paper provides insights to guide the development of robust security strategies for IIoT deployments. Addressing these challenges is crucial to maintaining the reliability and integrity of industrial operations, and safeguarding against production disruptions, financial losses, and safety hazards. Through robust security measures and privacy-enhancing technologies, IIoT deployments can foster trust, compliance, and innovation in industrial settings, driving the evolution towards smart factories and connected enterprises. Keywords – Industrial Internet of Things, Cyber Security, Network Security, Threats.

Dynamic beam steering for wireless optical power transfer in IoT applications.

The alignment of a receiver with a pencil beam in a wireless optical power transfer (WOPT) system employing a resonance beam charging (RBC) technology limits the establishment of a resonance cavity. Accurate tracking necessitates precise and dependable monitoring, which requires the exact placement of transmitting and receiving devices. Herein, we present a concept of a two-dimensional (2D) beam steering mechanism for RBC-based WOPT systems utilizing dispersed laser beams. The proposed approach allows a significant improvement, including reduction of scanning times and minimization of errors, in relation to conventional pencil-beam-based systems. Experimental results reveal 14% faster acquisition time efficiency, an 18% improvement in pointing accuracy, and a 24% enhancement in tracking accuracy. These results establish the prerequisites for the implementation of dispersed beam steering in the RBC-based WOPT system. This capability empowers the system to charge movable devices and Internet of Things devices consistently in smart factories.

Cyber Security Challenges in Industry 4.0: A Review

In the era of Industry 4.0, when smart factories and networked systems are reshaping the landscape of industrial production, the protection of important data and information security is a top priority. Cyber-physical systems and the technology that supports it are the keys to Industry 4.0. It is founded on four essential design principles: interoperability, availability of information, technological assistance, and decentralized decision-making. These design principles, however, provide new weaknesses that could be exploited by bad people. To protect these systems from emerging dangers, great consideration should be given to the proactive and adaptive security measures, which will consequently enable the continuing growth and success of Industry 4.0 technologies. This paper will delve into the multifaceted challenges that Industry 4.0 presents in terms of data security and the emerging solutions and strategies required protecting vital information in this brave new world of manufacturing. The exploration of these challenges and the proposed solutions are essential for businesses and policymakers alike to navigate the complexities of data security and ensure the resilience of critical information in the digital age of Industry 4.0.

An Integrated Approach to Precedence-Constrained Multi-Agent Task Assignment and Path Finding for Mobile Robots in Smart Manufacturing

Mobile robots play an important role in smart factories, though efficient task assignment and path planning for these robots still present challenges. In this paper, we propose an integrated task- and path-planning approach with precedence constrains in smart factories to solve the problem of reassigning tasks or replanning paths when they are handled separately. Compared to our previous work, we further improve the Regret-based Search Strategy (RSS) for updating the task insertions, which can increase the operational efficiency of machining centers and reduce the time consumption. Moreover, we conduct rigorous experiments in a simulated smart factory with different scales of robots and tasks. For small-scale problems, we conduct a comprehensive performance analysis of our proposed methods and NBS-ISPS, the state-of-the-art method in this field. For large-scale problems, we examine the feasibility of our proposed approach. The results show that our approach takes little computation time, and it can help reduce the idle time of machining centers and make full use of these manufacturing resources to improve the overall operational efficiency of smart factories.

Deep reinforcement learning for adaptive flexible job shop scheduling: coping with variability and uncertainty

ABSTRACT The frequently evolving manufacturing system necessitates real-time large-scale data analytics, surpassing the capabilities of classical systems or human skills, necessitating knowledge development and self-adaptive control. Unpredictable real-time events in smart factories cause changes in the effectiveness of planned schedules or tasks; even slight interruptions can accumulate to make the preplanned schedule unoptimized, if not impossible. To meet the required production efficiency, typical dynamic scenarios on the shop floor, such as failures, random job arrivals, and machine setup, must be addressed quickly. Hence, this study proposes a novel Triple Deep Q Network (TDQN) approach for learning high-quality dispatching rules for addressing the Flexible Job Shop Scheduling Problem (FJSSP) in uncertainty. For the dispatching rule-based FJSSP, the Markov Decision Process (MDP) to choose a suitable operation-machine (O-M) pair is formulated to allow operation selection and resource assignment resolutions to be made simultaneously. The performance of the TDQN method is investigated against the Double Deep Q Network (DDQN) and Deep Q Network (DQN) approach and found to be more stable. Moreover, among the 29 different shopfloor configuration settings, TDQN performed better in 62.07% of the overall instances, while DDQN and DQN performed better in only 31.03% and 6.9% of instances, respectively.

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev-Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

Optimal Deployment of Fog-Based Solution for Connected Devices in Smart Factory

Optimal Deployment of Fog-Based Solution for Connected Devices in Smart Factory

Research Paper on Role of Data Features and Data Collection Tools in Artificial Intelligence

Abstract: Artificial intelligence (AI) is revolutionizing various industries by enabling machines to learn, reason, and make decisions autonomously. However, the success of AI systems depends heavily on the quality and quantity of data used for training and testing. Therefore, data collection tools have become essential in AI development. In this paper, we will discuss some popular data collection tools in AI that facilitate the process of gathering large volumes of high-quality data for training and testing AI models. Robotics and sensors are increasingly being used to collect data for AI applications in various industries like healthcare, manufacturing, and agriculture. For instance, in healthcare, robots equipped with sensors can collect medical data like vital signs, blood pressure, and heart rate from patients. In agriculture, drones equipped with sensors can collect crop data like moisture levels, temperature, and nutrient content. These tools provide high-quality data that can be used to train AI models for diagnosis, prediction, and decision-making. Mobile apps are increasingly being used to collect user data for AI applications. Apps like Google Maps, Waze, and Uber collect location data that can be used to train AI models for navigation and traffic prediction. Healthcare apps like MyFitnessPal and Fitbit collect user health data that can be used to train AI models for personalized health recommendations. The Internet of Things is enabling the collection of vast amounts of real-time data from various devices like smart homes, smart cities, and smart factories. This data can be used to train AI models for predictive maintenance, energy management, and resource optimization.

A Case Study on the Food Industry's Smart Factory Establishment for Expanding Global Competitiveness

A Case Study on the Food Industry's Smart Factory Establishment for Expanding Global Competitiveness

LIFNS: Design of a novel Lidar-IMU fusion navigation system for AGVs in smart factories

Traditional Automated Guided Vehicle (AGV) robots commonly employ 2D plane navigation systems. However, with the expansion of AGV application scenarios and the increasing complexity of structures, the limitations of existing 2D navigation systems have become more pronounced, rendering them inadequate for addressing these challenges. To tackle this issue, this paper proposes a novel navigation system suitable for AGV robots composed of fused Lidar and Inertial Measurement Unit (IMU), named as Lidar-IMU Fusion Navigation System (LIFNS). LIFNS primarily comprises mapping, localization, and fused path planning modules. In the mapping module, a 3D point cloud map for precise 3D localization and a 2D grid map for path planning are constructed using multiple-line Lidar and IMU. For the localization module, a fusion localization approach is introduced, combining IMU data with Normal Distributions Transform (NDT) point cloud registration through Unscented Kalman Filtering (UKF). Finally, global path planning is executed using the A* algorithm on the grid map, while local path planning utilizes the Timed-Elastic Band (TEB) algorithm. The effectiveness and universality of LIFNS are validated through simulation experiments and real-world deployment tests. The experimental results demonstrate that LIFNS achieves centimeter-level accuracy in both mapping and localization, effectively alleviating the issues of low precision and significant limitations present in traditional AGV robots. This positions LIFNS with promising applications in enclosed settings such as smart factories, industrial parks, and healthcare facilities.

A preliminary step toward intelligent forming of fabric composites: Artificial intelligence-based fiber distortions monitoring

This work presents a prototype of an intelligent quality inspection tool for application to fibers distortion monitoring in the fabric composites forming processes. To this end, a series of hemisphere draping tests on a typical commingled fiberglass/polypropylene twill weave were conducted at the dry form, and the defects (in-plane and out-of-plane) for each formed part were inspected via camera vision. By gathering data from around 30 samples and over 1200 images from different forming regions, different machine-learning algorithms were trained and validated. As an application scenario in a smart factory, the developed simple AI tool would be used by an operator, or a robot, to scan different areas of the formed parts and identify ‘defected’ (fail) versus ‘non-defected’ (pass) scenarios. It was found that the K-nearest neighbors and Support Vector Machine models detect the defects with an error rate of less than 5% in the present case study, regardless of the background noise in the images such as external objects, marks on samples, or blurriness.

A study of sustainability risks from industry 4.0 perspective: taxonomy and future research avenues

Purpose A large number of organisations are moving towards adopting Industry 4.0 (I4.0), and simultaneously, the emphasis on attaining sustainability development goals is also increasing. Hence, it is imperative to understand the interplay between I4.0 and sustainability. However, the literature addressing the same is still in infancy. Accordingly, the purpose of this study is to fill this gap in the literature by exploring the potential sustainability impacts of I4.0 on the organisations and society in terms of sustainability risks. Design/methodology/approach To gain an understanding of sustainability aspects in the I4.0 context, relevant literature is gathered using Scopus and Web-of-Science database. An in-depth review of 51 research papers is performed to determine the sustainability risks associated with I4.0. Findings From the study, a total of 16 sustainability risks are identified, and I4.0 sustainability risk taxonomy is developed. The proposed taxonomy extends the sustainability implications of I4.0 beyond the triple bottom line umbrella and includes the organisational perspective as well. Furthermore, the study provides future research avenues to scholars by positing five potential research questions under different risk management stages. Research limitations/implications The study provides an understanding of sustainability risks associated with the adoption of I4.0. The findings will help practitioners streamline their production and operation processes by finding out possible solution to the sustainability risks of their smart factories in advance. The present research will act as a stepping stone towards I4.0 sustainability. The proposed research questions will assist the future researchers in extending the field of I4.0. Originality/value To the best of the authors’ knowledge, this is one of the first studies to address the topic of sustainability risks in the context of I4.0.

Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

Towards Sustainable Manufacturing: A Maturity Assessment for Urban Smart Factory

Towards Sustainable Manufacturing: A Maturity Assessment for Urban Smart Factory

Unleashing the potential of digital twins: a new era with aeronautics 4.0      

Abstract* Introduction The aerospace value chain consists of several processes, and digitizing it first requires an assessment of these processes and their ability to be transferred to the fully integrated digital thread perspective of smart factories. A digital thread refers to the continuous flow of data and information related to a product throughout its life cycle, integrating and connecting all aspects of a product’s journey. Within this framework, digital twin technology, an essential element of Industry 4.0, comprises the implementation of a virtual presentation of a physical object, system, or process. It brings together the digital replica not only of the physical attributes but also of the behavioral performance of the physical twin. Methods To achieve a digital thread perspective for aeronautics, a research agenda is proposed, including all breakpoints in the current value chain through a PESTLE Analysis, which defines the value-added areas targeted by this transition to digital technology. An aeronautics 4.0 model Digital Thread Twin Smart Aeronautics D2TSAero is proposed, which provides a new perspective in the aeronautics field by integrating its main ecosystems into an interconnected model made possible by the integration of digital twin agent instances. Proof of Concept A use case that deals with the dependability management of an aircraft fuel distribution system is presented. Based on these results, we can see that the proposed twin model can help in reducing real system parts down time, and it can also improve the management of maintenance across the system life cycle by offering a single source of trust for all stakeholders involved in the digital thread cycle of the real twin. Conclusion A forward-looking perspective on the future of aeronautics with this integrated approach is presented, summarizing all the discussed points and the importance of digital twins in supporting the digitalization of the field.

Implementation of OPC UA for Machine-to-Machine Communications in a Smart Factory

The development of autonomous and intelligent robots in manufacturing plants is being made possible by advancements in communications technologies and the Industrial Internet of Things (IoT). These autonomous devices are capable of gathering data from several sensor devices and multiple linked device systems through the use of OPC UA communication protocols. These devices can transmit and receive enough data these days to allow them to make decisions. Organization of individual devices is the sole application where high-level decision-making powers are available; communication between devices only entails low- level processing. The purpose of these images is to increase the OPC UA protocol-enabled devices' communication capabilities. This involves choosing methods for data reception and processing between devices connected vertically (deeper devices) and horizontally (equal devices). Because of this, the energetic gadget device makes the manufacturing facility site visitors better. KEYWORDS: Internet of things, Machine, Communications, Enterprises, Protocols, OPC UA protocols

The USA - China robotics competition: Leading the race in innovation and global power

The International Federation of Robots claims that the number of robots being produced today is at an all-time high, particularly in sectors like electronics and the automotive industry. Considering this, as artificial intelligence (AI) becomes more and more popular, it is imperative to study the robotics market, particularly in the nations that are major players in it. Robots are now integrated into all industries, especially automotive and electronics. The need for modernization and increasing competition are pushing countries to automate production and improve business processes; the United States and China have achieved the greatest success in this. According to the authors, it is China that will succeed in the industrial robotics market by introducing “smart manufacturing” and “smart factories.” In addition, there is now a growing trend towards creating robots that interact with people: the development of technology, the emergence of artificial intelligence, as well as human acceptance of the robot - all this contributes to the introduction of robots into our lives. Thus, according to the authors, it is the United States that has achieved the greatest success in the field of introducing service robots responsible for interaction with humans. The evolution of the robotics industry in the two major market participants - China and the United States - is analyzed. The study analyzes the industries in both countries to pinpoint development areas and reviews government initiatives that support business growth in both China and the United States.