Intelligent Factory Research Articles

Yaw stability control of automated guided vehicle under the condition of centroid variation

The centroid of an automated guided vehicle (AGV) changes due to the irregular position and uneven weight of the cargo on the load platform, which affects the completion of the handling task between stations in intelligent factories. This paper presents a hierarchical control strategy to improve yaw stability considering centroid variation. Firstly, the vehicle body and hub motor models are established based on dynamics. Secondly a hierarchical controller is designed by using the method of extension theory, model predictive control and sliding mode control. Then based on CarSim and Simulink, the low-speed step co-simulation condition of the AGV is carried out. Compared to the uncontrolled condition, the maximum deviation of the yaw rate is reduced from 0.58 to 0.52 rad/s, and the difference with the theoretical value is reduced from 16 to 4%; the maximum deviation of the centroid sideslip angle is reduced from − 0.84 rad to − 0.77 rad, and the difference with the theoretical value is reduced from 12 to 3%. Finally, a four-wheel drive and four-wheel steering AGV are manufactured to carry out inter station steering experiments in simulated factory environment on different road adhesion coefficients. The difference between simulation and experiment is less than 5%. The results show that the designed controller is effective, and the research can provide theoretical and experimental basis for the low-speed steering control stability of AGV.

Identifying Benchmarks for Failure Prediction in Industry 4.0

Industry 4.0 is characterized by the availability of sensors to operate the so-called intelligent factory. Predictive maintenance, in particular, failure prediction, is an important issue to cut the costs associated with production breaks. We studied more than 40 publications on predictive maintenance. We point out that they focus on various machine learning algorithms rather than on the selection of suitable datasets. In fact, most publications consider a single, usually non-public, benchmark. More benchmarks are needed to design and test the generality of the proposed approaches. This paper is the first to define the requirements on these benchmarks. It highlights that there are only two benchmarks that can be used for supervised learning among the six publicly available ones we found in the literature. We also illustrate how such a benchmark can be used with deep learning to successfully train and evaluate a failure prediction model. We raise several perspectives for research.

Dual-band piezoelectric acoustic energy harvesting by structural and local resonances of Helmholtz metamaterial

The current study on acoustic energy harvesting is based on a single-band vibroacoustic conversion performed by either structural resonance or local resonance. In this paper, we propose a Helmholtz acoustic metamaterial (HAM) piezoelectric device having dual-band acoustic energy harvesting characteristics. The Helmholtz resonator of the metamaterial amplifies both structural and local resonances. HAM is designed based on the Bloch theorem, plane wave expansion method, and electroacoustic impedance analogy. Numerical simulation is performed to show the sound pressure amplification effect of HAM. A piezoelectric disk is bonded on the point defect of the Helmholtz metamaterial for energy localization and conversion, and HAM is clamped on a self-made experimental platform by simply supported boundary conditions on four sides. The time–frequency image of the voltage output from the swept frequency experiment shows two distinct bands corresponding to the structural resonance frequency of 381 Hz with the band width of 45 Hz and the local resonance frequency of 1526 Hz with the band width of 290 Hz. The peak-to-peak power of HAM is 0.13 mW, and its peak-to-peak voltage is 3.2 V at 391 Hz with the sound pressure of 31 Pa. At the input sound pressure of 23.32 Pa and frequency of 1526 Hz, the output voltage and power are found to be 1.5 V and 0.11 mW, respectively. Under the same amplitude of the input sound pressure, the output power of HAM is found 12.7 times and 4.4 times higher than those of the traditional acoustic metamaterial in the structural and local resonance bands, respectively. Field tests validate the superiority of the designed structure. In the milling environment, the voltage–pressure transmission rate reaches 0.11 V/Pa. The acoustic energy wall composed of HAM will be capable to provide a power solution for an intelligent factory.

Product innovation between circular economy and Industry 4.0

In the construction sector, the new Industry 4.0 and circular economy paradigms are currently playing an increasingly important role in order to respond to demands for energetic efficiency, optimisation of resource usage, automation and production activities monitoring. Therefore, this article aims to at illustrate industrial research and an experimental development activity that led to the creation of an innovative product named MULTIFId. It consists of an intelligent, economical and low-emission panel, made with waste from the paper and cardboard industrial manufacturing process which intends to contribute to the development of the fundamental enabling technology of “Advanced Materials” for Intelligent Factories.

System Integration and Application of a Networking Production Line for Aerospace Precision Manufacturing

In this paper, the networking integration application of an intelligent production line is studied and developed for aerospace precision manufacturing. The technical development of the performance equalization and control for EDM machine is also introduced with the related technologies of the machine network and Ethernet for Control Automation Technology (EtherCAT) network. In addition, the development of intelligent production lines is integrated to improve the system stability and production efficiency of production line systems for aerospace component manufacturing. The key technologies of Cyber-Physical System (CPS) and intelligent networking are also integrated to construct the real-time intelligent system monitoring system [Supervisory Control and Data Acquisition (SCADA)] for data record and management. The key technologies of the drilling process are developed for intelligent diagnosis and drilling holes inspection of related production processes. The integrated robot arm is responsible for the loading and unloading of the material, and the entire production line is connected by the EtherCAT network. The automatic optical inspection is introduced at the rear end of the production line to realize the quality judgment of the finished product. Furthermore, the virtual cloud network and the intelligent factory are achieved with the information collection and intelligent monitoring system.

A Case Study on Smart Factory Extensibility for Small and Medium Enterprises

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap.

Industrial IoT (IIoT) is a novel concept of a fully connected, transparent, automated, and intelligent factory setup improving manufacturing processes and efficiency. To achieve this, existing hierarchical models must transition to a fully connected vertical model. Since IIoT is a novel approach, the environment is susceptible to cyber threat vectors, standardization, and interoperability issues, bridging the gaps at the IT/OT ICS (industrial control systems) level. IIoT M2M communication relies on new communication models (5G, TSN ethernet, self-driving networks, etc.) and technologies which require challenging approaches to achieve the desired levels of data security. Currently there are no methods to assess the vulnerabilities/risk impact which may be exploited by malicious actors through system gaps left due to improper implementation of security standards. The authors are currently working on an Industry 4.0 cybersecurity project and the insights provided in this paper are derived from the project. This research enables an understanding of converged/hybrid cybersecurity standards, reviews the best practices, and provides a roadmap for identifying, aligning, mapping, converging, and implementing the right cybersecurity standards and strategies for securing M2M communications in the IIoT.

Design and Implementation of Real Time Monitoring System of Textile Equipment Based on MQTT

At present, with the progress of network information technology, the idea of intelligent factory and intelligent manufacturing is imperative. It is imperative to realize intellectualization and informatization in traditional manufacturing plants. According to the intelligentized demand of textile equipment, the real-time monitoring of the spindle status of the textile machine is designed and realized, and the functions of the real-time statistics of the operation efficiency, the number of broken ends, the spindle status of the textile machine, the statistical report and so on are made in real time. It has achieved remote and efficient production management and fault detection. It effectively reduces the cost of manual operation and maintenance, and improves the intelligence level of textile mills.

Review of Lean Manufacturing with IR4.0 in Automotive Industry

This paper aims to study the possibility of integration between Lean Manufacturing (LM) and Industrial Revolutions 4.0 (IR4.0). LM is generally known and acknowledged as a feasible system in the industrial sector. However, a new paradigm of IR4.0 has influenced manufacturers to look further into how LM could be implemented and adopted. It drives the integration of an intelligent factory to control machines, humans, products, and cloud solutions along the value chain. Manufacturers, especially in the automotive industry such as Toyota Company, have been using lean concepts and methods for so many years to eliminate wastes, reduce operational costs, and improve production performance. By integrating LM with IR4.0, the manufacturers could enhance productivity and quality by using the implementation chain. Besides, it enables self-management operational processes that could ensure the customer’s quality of production.

마할라노비스 거리법을 이용한 절단 품질에 대한 연구

Social interest in the 4th industry, intelligent factories, and smart manufacturing is continually growing along with the core technologies like big data and artificial intelligence, which can generate meaningful information by collecting and accumulating sensor data. Demand for industrial automation equipment is increasing worldwide due to the efforts needed to modernize manufacturing facilities, reduce automation and cycle time, and improve quality. Currently, the majority of research is focused on the development of automation facilities and improving productivity. The research on the contents of real-time data considering the characteristics of the cutting machine plasma machine is insufficient. In this study, based on the current data measured according to cutting current and cutting speed, a reference value for cutting quality is presented and the optimal process parameter has been selected. A model for predicting cutting quality by introducing the Mahalanobis Distance Method is presented. An attempt has been made to derive selection and optimal cutting process variables. Based on the predictive model, threshold values were specified and used in real-time data to consider the correlations between multivariate variables and evaluate the degree of scattering around the average of specific values of each variable. Also, process parameters suitable for surface roughness were calculated.

Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data

ABSTRACT The production execution system is designed and constructed to achieve intelligent inspection of product quality and information management of energy conservation and emission reduction in intelligent factories. In this study, big data and business intelligence (BI) data are applied to analyse and standardise the production process, and a low-carbon economic visualisation platform is designed. The research results show that the application of big data technology in intelligent factory quality management and low-carbon economy visualisation platform can make up for the lack of objectivity of product quality management, and realise the visualisation of low-carbon economy data index.

Application of Intelligent Manufacturing in the Production Line of Hydraulic Support Coal Machinery Plant

From the definition of intelligent manufacturing, the status quo, shortcomings and solutions of the hydraulic support coal machine factory are introduced. Intelligent manufacturing is a human-machine integrated intelligent system composed of intelligent machines and human experts. Combined with research and the implementation of its own work, it introduces the current production lines of China’s hydraulic support coal machine factory from cutting, welding, spraying, and processing. The application situation of intelligent manufacturing, finally analyzed the problem of limited site, tight construction period, and unreasonable production process in the intelligent manufacturing of the hydraulic support coal machine factory, and proposed solutions that must be lean first, combined with automation and informatization. Realize the intelligent construction of the factory on the basis of optimized technology and modern management concepts, and finally establish a CPS system based on digital twins to realize a predictable intelligent factory.

Metrisable assessment of the course of stream-systemic processes in vector form in industry 4.0

The goal of this paper is to present an innovative conception how to use metrisable vector structure of a manufacturing process, based on quantitative relations between the activity of input streams, features of the product, and effect of losses; all of which are excellent practical solution for Industry 4.0, and in turn intelligent factories. This solution can be a usefull way in the process of building sustainable organization. A vector representation of manufacturing processes was formulated, one which is based in system engineering. Three manufacturing system state vectors were proposed. These are: input stream vector {upphi }, product features vector overrightarrow {{text{ P}}} which is also referred to as quality vector, and losses vector overrightarrow {{text{ S}}}. Scalar, vector, and mixed products of these vectors may form constitutive equations of manufacturing processes. The relations between the vectors upphi, overrightarrow {{text{ P}}},overrightarrow {{text{ S}}} provide a possibility for a metrisable, complex analysis and assessment of a contemporary manufacturing process. The paper shows practical methods for defining the size of the vector values within the process. The demonstrated vector description of stream-systemic processes can also be applied to non-material manufacturing.

THE CHALLENGES FOR DEVELOPING A PUBLIC AGENDA UNDER INDUSTRY 4.0

The term Industry 4.0 was used in 2011 in Germany and is being called the fourth industrial revolution. It encompasses the implementation of high technology capable of making the “Intelligent Factory”, in order to increase productivity and go beyond that, integrate production, machines, and users with the use of communication technologies. It is not only about the increase of new technologies in production but will reach dimensions in labor relations, in social and institutional spaces, and regulation. Therefore, the objective of this paper is to identify the main guidelines that should integrate the public agenda of governments with the advent of industry 4.0. For this purpose, bibliographic research was used. The main results of this research were the following: regulation of new technologies, training, and adaptation of the labor market, integration of the public management systems through IoT, policies to stimulate the implementation of the main technologies and innovations in Industry 4.0, not only in economic activities but also improve the distribution systems of public services and sustainability.

A Study on Intelligent Manufacturing Industrial Internet for Injection Molding Industry Based on Digital Twin

As one of the basic manufacturing industries in China, injection molding industry is faced with the problems of low degree of informatization and intelligence, resulting in low production efficiency and high costs. It is urgent to integrate deeply with new generation of information technology to achieve transformation and upgrade. In this paper, an integrative industrial Internet architecture of “integration of intelligent equipment, intelligent production lines, intelligent workshops, intelligent factories, and intelligent formats” was described. The injection molding intelligent control system, the production management, control platform based on the MES system, and other key technologies were researched. Also, the smart factory architecture based on digital twin was established, and the implementation method of the smart factory digital twin system was elaborated. The feasibility and effectiveness of the method had been verified through the industrial application, which provided the technical supports for the injection molding industrial Internet system. Finally, the intelligent manufacturing industrial Internet cloud platform for injection molding industry was prospected.

DWPT vs OFDM Under a Noisy Industrial Channel

In the industrial world, a trend towards connected, robotic and intelligent factories is in constant development to face competition from countries with low production costs. The industrial revolution ‘Industry 4.0’ is considerably reducing the boundaries between the physical and digital worlds. This has given rise to interconnected factories in which people, machines and products interact with each other using smart sensors. Commonly referred to as Industrial Internet of Thing (IIoT). Except that, it requires adapted communication systems for the industrial environment. Such a propagation environment is known by its complexity that should be considered in order to propose a robust and reliable communicating system. In this paper, a performance evaluation between a pulse-modulated Discrete Wavelet Packet Transform (DWPT) system and an Orthogonal Frequency Division Multiplexing (OFDM) multicarrier modulation system over a noisy industrial channel will be presented. Thus, allowing not only to provide an alternative to conventional high-rate industrial wireless communication systems but also to know the limits of both techniques

Przemysł 4.0 i sztuczna inteligencja: szansa czy zagrożenie dla realizacji koncepcji zrównoważonego i trwałego rozwoju?

This paper discusses the problems connected with visible changes in industry in the context of the consequent four industrial revolutions. The last one is associated with “industry 4.0”, which in turn manifests in the presence of the following constitutive parts (systems): cyber physical systems, the Internet of Things, the Internet of Services and intelligent factories. Another important factor of the ongoing changes is the appearance of a new branch, which tries to comprise in its theoretical divagations the problems discussed in IT, mathematics, neurophysiology, electronics, psychology, anthropology and philosophy. In the experimental area this realm, in turn, is treated as a branch of IT. All these constituents can be defined as artificial intelligence. The aim of this research is an attempt to answer the question posed in the title of the article, taking into consideration the potentially most holistic approach to these problems in the context of sustainable development of the constituent capitals taking into consideration not only the increasing of opportunities but maximizing the benefits in the natural, social and economic spheres.

An Information Theory Inspired Real-Time Self-Adaptive Scheduling for Production-Logistics Resources: Framework, Principle, and Implementation.

The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent factory is random, such as the arrival of customers’ jobs, and fuzzy, such as the processing time of Production-Logistics Resources. Besides, the coordination of production and logistic resources in a flexible workshop is also a hot issue. The availability of this information will enhance the quality of making scheduling decisions. However, when and how to use this information to realize the adaptive collaboration of Production-Logistics Resources are vital issues. Therefore, this paper studies the above problems by establishing a real-time reaction scheduling framework of Production-Logistics Resources dynamic cooperation. Firstly, a real-time task triggering strategy to maximize information utilization is proposed to explore when to use real-time information. Secondly, a collaborative method for Production-Logistics Resources is studied to explore how to use real-time information. Thirdly, a real-time self-adaptive scheduling algorithm based on information entropy is utilized to obtain a stable and feasible solution. Finally, the effectiveness and advancement of the proposed method are verified by a practical case.

Competencies for Industry 4.0

Industry 4.0 is a term that represents the radical transformation of Industry that has resulted from the integration of emerging technologies. It implies that we are witnessing the proximity of the Fourth Industrial Revolution. Industry 4.0 aims to enable intelligent factories to produce personalized output utilizing greener and more efficient processes. However, to accomplish this, manufacturers must overcome several barriers, such as the lack of qualified talent to develop and manage various high-tech systems. This deficiency means that Industry 4.0 demands a change in the labor market, explicitly requiring trained professionals who have the competencies and skills to thrive in this new environment. What should a set of competencies be in the modern professional profiles? In this work, we review the required competencies, the technologies that aid in developing them, and the methods of assessing them.

Field Robots for Intelligent Farms—Inhering Features from Industry

Estimations of world population growth urgently require improving the efficiency of agricultural processes, as well as improving safety for people and environmental sustainability, which can be opposing characteristics. Industry is pursuing these objectives by developing the concept of the “intelligent factory” (also referred to as the “smart factory”) and, by studying the similarities between industry and agriculture, we can exploit the achievements attained in industry for agriculture. This article focuses on studying those similarities regarding robotics to advance agriculture toward the concept of “intelligent farms” (smart farms). Thus, this article presents some characteristics that agricultural robots should gain from industrial robots to attain the intelligent farm concept regarding robot morphologies and features as well as communication, computing, and data management techniques. The study, restricted to robotics for outdoor farms due to the fact that robotics for greenhouse farms deserves a specific study, reviews different structures for robot manipulators and mobile robots along with the latest techniques used in intelligent factories to advance the characteristics of robotics for future intelligent farms. This article determines similarities, contrasts, and differences between industrial and field robots and identifies some techniques proven in the industry with an extraordinary potential to be used in outdoor farms such as those derived from methods based on artificial intelligence, cyber-physical systems, Internet of Things, Big Data techniques, and cloud computing procedures. Moreover, different types of robots already in use in industry and services are analyzed and their advantages in agriculture reported (parallel, soft, redundant, and dual manipulators) as well as ground and aerial unmanned robots and multi-robot systems.