Smart Factories Of Industry Research Articles

Internet of Things for Smart Factories in Industry 4.0, A Review

Internet of Things for Smart Factories in Industry 4.0, A Review

Production Scheduling Optimization enabled by Digital Cognitive Platform

Automation and optimization are two key concepts for smart factories in industry 4.0. The production planning and scheduling optimization models are widely used to support the aforementioned key concepts. Due to the wide availability of various Industrial Internet of Things (IIoT) devices and predictive models, the factories of the future would be self-learned and capable to act in monetary situations. To achieve this, the planning optimization, dynamic rescheduling and production line balancing solutions should be available to todays’ production lines. In this paper, we are introducing a real-world example of a solution for production scheduling optimization and production line balancing based on genetic algorithm. The introduced application was developed over an established Cognitive Analytics Platform for Anomaly Detection. The application uses deployment, security and visualization services available by the platform. Therefore, this work presents alongside the optimization solution, the way the platform can be modified for serving other use cases besides the anomaly detection, in order to provide a complete tool for factory automation and optimization.

A Study on Semantic-Based Autonomous Computing Technology for Highly Reliable Smart Factory in Industry 4.0

Smart factories have made great progress with the development of various ICT technologies, such as IoT, big data, and artificial intelligence. The recent development of smart factory technology has shown results in automation and data acquisition and processing. However, it still has incomplete points to be converted to advanced technology, including intelligence. For intelligentization, there is a need to propose a new research method in addition to the previous methodologies. Considering the specificity of the factory, the data structure and methodology of the Semantic Web can be effective. Therefore, in this study, a smart factory was designed by the convergence of monitoring technology, autonomous control technology, and semantic web technologies. Based on the proposed methodology, a methodology for the autonomous control of a smart factory on a digital twin was designed.

The Sensor Network for Multi-agent System Approach in Smart Factory of Industry 4.0

This research was developed to plan, monitor, and control the production in a modern manufacturing system model with heterogeneous production facilities, consisting of several automatic machine tools and conventional machine tools. Therefore, it proposed, a smart factory concept that utilises computer technology, internet networks and sensors so that the production process can be monitored. The sensor network monitors the condition of the machine tools and the status of the job. The temperature sensors, the vibration sensors, the electrical energy sensors are used to check tool conditions in machine tools. Meanwhile, the radio frequency identification (RFiD) system is used to check the status of the workpiece whether it has been completed, work in progress, or is waiting in a buffer or a pallet stocker. The information relating to the performance of the machine tools is sent using the IoT application so that through the web. The machine performance data are collected, and their status can be monitored. Likewise, job status is visible on the shop-floor control system. The sensor network model at the prototype scale had been built and tested on a laboratory scale. The test results showed that the performance of machine tools and job status were monitored properly.

High-definition map update framework for intelligent autonomous transfer vehicles

ABSTRACT Autonomous transfer vehicles (ATVs) can be considered as one of the critical components of context-aware structured smart factories in Industry 4.0 era. Conventional mapping methods such as grid maps can provide information for navigation, but they are not enough for complex environments that require interactions. On the other hand, high-definition (HD) mapping, which is mainly used in traffic networks, includes more information about an environment to perform excellent autonomous behaviour. In order to increase the efficiency of ATVs in flexible factories, an up-to-date environmental map information is required to perform successful long-term autonomous navigation. Therefore, when there exists a change in the environment, a simultaneous update of HD-map is as important as the creation of it. In this study, we propose an HD-map update methodology for ATVs that operates in smart factories. To the best of our knowledge, HD mapping has not been applied in smart factories. The proposed method includes the object detection and localisation tool to detect objects visually and determines their positions in connection with the conventional maps of the environment. Experimental results of a simulated factory environment demonstrate that the ATV can properly update the HD-map when a predefined sign is removed from or a new sign is added to the environment.

Smart factory in Industry 4.0

AbstractThe transformation from traditional manufacturing to intelligent manufacturing intrigues the profound and lasting effect on the future manufacturing worldwide. Industry 4.0 was proposed for advancing manufacturing to realize short product life cycles and extreme mass customization in a cost‐efficient way. As the heart of Industry 4.0, smart factory integrates physical technologies and cyber technologies and makes the involved technologies more complex and precise in order to improve performance, quality, controllability, management, and transparency of manufacturing processes. So far, leading manufacturers have begun the journey toward implementing smart factory. However, most firms still lack insight into the challenges and resources for implementing smart factory. As such, this paper identifies the requirements and key challenges, investigates available new technologies, reviews existing studies that have been done for smart factory, and further provides guidance for manufacturers to implementing smart factory in the context of Industry 4.0.

Reviewing Cyber-Physical System as a Part of Smart Factory in Industry 4.0

Due to the successful application of the Internet of Things (IoT) and the Internet of Services (IoS) in association with other emerging technologies, the fourth industrial revolution is finally achieved through various advancements of cyber-physical systems (CPS). In manufacturing, CPS, in association with the existing enabling technologies, bridges the gap between the manufacturing and information technology (IT) and gives birth to the smart factory that is significantly changing the global market, societal life, and economic growth. This transformation will go far beyond today's conventional industry in terms of adaptability, energy and resource efficiency, accuracy, reliability, safety, user-friendliness, scalability, and intelligence while thoughtfully dealing with the technical challenges allied with the development of CPSs in terms of wireless networking, reliability, complexity, security, and data protection to achieve the desired level of performance. Moreover, a successful industrial revolution paves the way for efficient and thoughtful management operations. This article provides a detailed analysis of CPS in the industrial domain, its required technologies and management skills, architectures, and expected features. This article also highlights a few case studies and discusses their benefits as well as related challenges, along with the possible solutions to them. And, last but not least, an in-depth discussion on the socio-economic impact of CPS-based industrial revolution is presented in this context.

An interactive method for multi-criteria dispatching problems with unknown preference functions

This research investigates multi-criteria dispatching problems with unknown user preference functions to help decision makers (DMs) find solutions that meet their multi-criteria preferences. The explicit consideration of the following factors distinguish this research from previous literature: (1) dispatching management with three conflicting objectives, (2) nonlinear preference functions with unknown weights, (3) indecisive threshold that models the bounded rationality of DMs, and (4) near optimal solution through a small number of interactions. In a smart factory of Industry 3.5, the proposed interactive centroid method (ICM) enhances the efficiency of human–computer interaction and improves the quality of multi-criteria manufacturing decisions significantly. Our extensive numerical study reveals that the ICM algorithm finds near optimal solutions under linear or quadratic utility functions through less than a dozen interactions with DMs. The average gap between the ICM solutions and true optimal is merely 1.537% and 0.882% under quadratic and linear utility functions, respectively.

Designing an Information Security System in Smart Factories of the Industry 4.0

The problem of designing an information security system in smart factories of Industry 4.0 is investigated. Information security system is a company infrastructure component to provide the company information security. In the example of typical smart factory structure cyber and physical components are defined which are mostly susceptible to the malicious software components implementations. Such components could be components with their own means of calculations and engaged in the production technological operations. Smart factory information security risks can be explained with program and technical means for the operator to control the cyber and physical configuration remotely and other components of the production infrastructure into a single digital space of the company. Measures are proposed to increase the information security level of the Industry 4.0 smart factory based on technical, organization and program solutions.

저전력 IoT 네트워크에서 불안정한 노드를 회피하는 적응적인 ADP-RPL

A recent challenge is that IoT devices tend to be configured in an LLN environment. LLN has thousands of nodes with limited processing power, memory, and energy, and these nodes are typically connected by lossy links. The RPL for LLN is designed for use in resource-constrained devices in industry, homes, and cities. The primary purpose of the RPL is to provide IPv6 connectivity for many battery-powered embedded devices using low-power radios. The RPL is expected to be very active in smart factories in Industry 4.0. However, smart factories are an environment with high interference and mobility. Although RPL is designed for LLN, energy consumption may increase due to frequent parent changes in unstable networks. Adaptive RPL (ADP-RPL) calculates the degradation of link quality as the rank value increases and the parent"s change time and then reflects it in the rank value sent to neighboring nodes. In order to prevent frequent parent changes, we used a threshold that is proportional to the rank of neighboring nodes. In this paper, we evaluated the performance of ADP-RPL using a Cooja simulator. As a result, ADP-RPL shows better performance with more nodes. In order to evaluate performance in the actual environment, a test was performed in an environment in which GET and POST messages were transmitted using the CoAP protocol. RE-Mote device was used to evaluate the indoor space where interference occurred. In the real environment, the rate of ADP-RPL transmission failure and the number of parent changes were reduced compared to RPL.

Client-server technologies at the enterprises of Industry 4.0

The task is to project the distributed architecture of calculation net for a digital and smart factory of Industry 4.0. The calculation net architecture defines the calculation resources components and their interaction to organize designer project activity or to manufacture a set of items in a serial plant. You may use in Industry 4.0 factory calculation net architecture client and server technologies. All factory technological equipment is classified by the type of the operation being completed: thin client, thick client, universal client and server equipment. Thin client of cyber and physical production is the simplest communication device with installed software which functions as a terminal and being used by the operator. Thick client of cyber and physical production is a complete production machine (test equipment) which is capable to work automatically by the programed algorithm. Universal client of cyber and physical production combines the properties of thin and thick clients. A cyber and physical system is a universal client example of the smart factory calculation net. Digital factory net thin client is a communication device with software which is used to control the modes of digital models virtual tests. Digital factory net thick client is a work station to conduct digital models virtual tests with your own calculation resources. Digital factory net universal client is a PC which is a part of the designer automatic work place and which is used to develop and create construction, program and technological documentation and also item digital models. Server equipment is a terminal server, data server and application server to exchange the information in multi-level hierarchy calculation net. Some schemes of digital and smart factories calculation net architecture are given.

The life cycle of technical documentation in the smart factory of Industry 4.0

The task is to synthesize all stages of the item designing component technical documentation life cycle in the companies of the Industry 4.0. The companies of the Industry 4.0 is a new type of project and production companies which activity is done with paperless documentation. The traditional designing procedures of construction, program and technological documentation are studied which are available today in the companies of the Industry 3.0. There is a scheme of technological documentation life cycle in the Industry 3.0 companies. It is clear that for the Industry 4.0 companies they need new forms of item designing component technical documentation which could be digital replica, digital shadow and digital twin of the item which is done in different stages of item projection. Also it is clear that new forms of electronic documents require new instrument means for project activity. There is a scheme of item designing component technical documentation life cycle in the Industry 4.0 companies.

Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

Formation principles of digital twins of Cyber-Physical Systems in the smart factories of Industry 4.0

The task of organizing the production of instrument-making enterprises of Industry 4.0 using digital twins of Cyber-Physical Systems (CPS) based on cloud services is considered. Cloud services are a component of SPS, acting in production as active Internet agents for the manufacture of instrumentation products. The combination of CPS, united by a single computerized control system, forms the production infrastructure of a smart factory. Smart Factory is a manufacturing plant of Industry 4.0, operating in automatic mode. A scheme of interaction between the components of the smart factory Industry 4.0 and cloud services for the production activity of the instrument-making enterprise is proposed.

Ontology-based Manufacturing Control Systems (MCS)

Programmable Logic Controller (PLC) is a typical control kernel for manufacturing automation, especially in the era of Industry 3.0. Centralized control architecture of PLC with an ease of maintenance has been widely applied in many arenas for automation. However, PLC’s centralized structure is becoming incapable to handle the control requirements of smart factories in Industry 4.0, within which are full of diverse sensors and actuators, not to mention its incapability of expansion and flexibility.OWL (Ontology Web Language) is a language representing knowledge in the format of ontology, whereas ontology is formal way to describe taxonomies and classification networks. Compared to the traditional program control system, system with OWL is ease of modification and expansion. By taking advantages of OWL into manufacturing control system, this research develops a manufacturing ontological knowledge model. Because of the system is built based on Jena package, SPARQL as a knowledge query language will be easily deployed to demonstrate the flexibility, robustness, and re-configurability of an ontology-based manufacturing control system to the environmental disturbances.

Best Practices. Managerial Early Warning System as Best Practice for Project Selection at a Smart Factory

The purpose of the paper is to contribute to the development of best practices at emerging factories of the future, i.e. smart factories of Industry 4.0. Smart factories need to develop effective managerial early warning systems to identify and respond to subtle threats or opportunities, i.e. weak signals, in order to adapt to an ever-changing environment in a timely manner and thus gain or maintain a competitive advantage on the market. These factories need to develop and implement a several-stage early warning system that is specific to their industry. The aim of our study is, with the help of semi-structured group interviews, to examine which stages of a managerial early warning system are present in the case of a global innovative supplier in the automotive industry. As such, a four-stage managerial early warning system model for a knowledge-based automotive smart factory is proposed, in which aggregate activities and management decision-making strategies are defined for each stage, with the importance of intuition being taken into consideration. We found that managers rely on intuition and extensive analysis for satisficing strategies and teamwork for optimizing strategies, when using their managerial early warning system.

TRAINING GAPS IN ENGINEERING DEGREES FOR INDUSTRY 4.0 IN SPAIN. A PROPOSAL FOR ACTIONS

The aim of this paper is to identify the competency requirements of manufacturing resulting from the fourth industrial revolution. The smart factory of industry 4.0 is described in generic form, identifying the main technologies employed, and the implications of their use in the development of new business models and the innovative activity of the company. In this way, we can approximate the main future competencies of the workforce, focusing on the advanced technical and management functions to be developed by the grade industrial engineer. Thus, a typology of competences has been established that distinguishes between: (1) technological ones, related to the knowledge and use of new information, communication and production technologies; (2) technical ones, related to the capabilities for the design and management of the smart factory business processes; and (3) the personal ones, that collect the attitude dimension of the engineers in the development of their functions. Using information from the ANECA, about the competences of Engineering Degrees in Mechanics, Industrial Electronics and Automation, and Industrial Organization, in this paper we identify the competence gap of the grade engineers trained in Spanish public universities in relation to the I4.0 competences (technical and technological) for the smart factory management on the future.

Proactive caching for edge computing-enabled industrial mobile wireless networks

As manufacturing systems shift from automated patterns to smart frameworks such as smart factories in Industry 4.0, industrial wireless networks (IWNs) are serving as promising communication systems that can be applied to the manufacturing field. When the mobile elements and static nodes are introduced into the system, large amounts of data downloaded from mobile networks or tele-servers can be one of the greatest challenges for industrial mobile wireless networks (IMWNs). Mobility and industrial properties have rarely been considered by previous research on download strategies and caching methods. In this paper, we present a three-layer cache architecture based on edge computing and other heritage traditional networks. Then, useful spatial and temporal mobility properties are mapped using different groups and edge computing servers that contain mobile nodes. Then, according to the sojourn time, the capacity of edge computing servers and other neighbouring nodes, we propose a proactive caching strategy for large amounts of data downloaded by mobile networks that considers location and mobile trajectories. Moreover, the superiority of our proposed scheme is demonstrated by comparison case studies of widely used classical schemes. The numerical results show that our proposed strategy achieves higher goodput and real-time and other performance.

Guest Editorial Special Section on Recent Trends and Developments in Industry 4.0 Motivated Robotic Solutions

The twelve papers in this special section focus on the development of robotic solutions for smart factories in industry - the concept of the fourth industrial revolution (industry 4.0). The inclusion of robotics is expected to deeply change the future manufacturing and production processes, and lead to smart factories that will benefit from the main design principles of Industry 4.0: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity. Robotics will have a key role in this development since innovative technologies and solutions, traditionally associated with the service robotics sector, are going to migrate to industrial smarter robots, exploiting the maturing of navigation, localization, sensing, and motion control technologies. These smarter robots will draw on a much broader range of technology, allowing higher levels of dexterity and flexibility, the ability to learn tasks without formal programming, and to autonomously collaborate with other autonomous devices and human operators.

Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory

The smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for market capitalization in the near feature. Nowadays, cloud computing with the high performance of computing and self-service access plays an important role in realizing smart factor. To minimize the overall cost of company in a heterogeneous cloud environment, including multiple public clouds, while ensuring a proper level of quality-of-service, task placement across multiple public clouds is a critical problem, where task deadlines and long-haul data transmission costs between smart factory and different clouds must be considered. We formulate this task placement problem as an integer linear program (ILP) to minimize company cost under the task deadline constraint. With extensive simulations, we evaluate the performance of our proposed ILP model in heterogeneous public clouds with finite and infinite resources.