Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution System Along with ERP Back-End Integration: Ready-Made Garments as a Case Study

Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution Systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

Analysis of the Supply Chain in Industrial Companies in Guayaquil,Ecuador-A Secondary Publication

This research clearly outlines the supply chain in a process of evolution and digital transformation,where the collaboration of its members is concentrated,having technological tools to help chain management.To analyze the advantages that this transformation produces in the supply chain,as well as its different processes,this study has a systemic perspective that involves precise elements for organizational development,such as the different phases of each process,operations,logistics,and distribution.It must be borne in mind that any strategy in industrial companies grants the automation of procedures in the supply chain and determines a product in any phase of production,making the organization more sensitive to any variation in orders.The methodology included a bibliographic and non-experimental review that allows a descriptive and analytical study,which details the various characteristics of the fact that is being investigated,collecting information through interviews with different people who are involved with industrial companies.Among the results obtained,it was identified that digital transformation helps reduce costs and generates greater profitability.In conclusion,it was obtained that the digital supply chain helps in each of the phases of the processes,these are supervised by devices that help to have fast and effective information.

ChainApparel: A Trustworthy Blockchain and IoT-Based Traceability Framework for Apparel Industry 4.0

Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers,manufacturers,suppliers,and consumers.Each stakeholder has implemented different technology-based systems to record and track product transactions.However,these systems work in silos,and there is no intra-system communication,leading to a lack of complete supply chain traceability for all apparel stakeholders.Moreover,apparel stakeholders are reluctant to share their business information with business competitors;thus,they involve third-party auditors to ensure the quality of the final product.Furthermore,the apparel manufacturing industry faces challenges with counterfeit products,making it difficult for consumers to determine the authenticity of the products.Therefore,in this paper,a trustworthy apparel product traceability framework called ChainApparel is developed using the Internet of Things(IoT)and blockchain to address these challenges of authenticity and traceability of apparel products.Specifically,multiple smart contracts are designed and developed for registration,process execution,audit,fault,and product traceability to authorize,validate,and trace every business transaction among the apparel stakeholders.Further,the real-time performance analysis of ChainApparel is carried out regarding transaction throughput and latency by deploying the compute nodes at different geographical locations using Hyperledger Fabric.The results conclude that ChainApparel accomplished significant performance under diverse workloads while ensuring complete traceability along the complex supply chain of the apparel industry.Thus,the ChainApparel framework helps make the apparel product more trustworthy and transparent in the market while safeguarding trust among the industry stakeholders.

Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human beings.In the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital applications.The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 environment.The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification.Primarily,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based encryption.The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images.The secret images,encrypted by signcryption technique,are embedded into cover images.Besides,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax classifier.The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication.The proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects.The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

Real Time Automation and Ratio Control Using PLC&SCADA in Industry 4.0

Industrial Control Systems(ICS)and SCADA(Supervisory Control and Data Acquisition)systems play a critical role in the management and regulation of critical infrastructure.SCADA systems brings us closer to the real-time application world.All process and equipment control capability is typically provided by a Distributed Control System(DCS)in industries such as power stations,agricultural systems,chemical and water treatment plants.Instead of control through DCS,this paper proposes a SCADA and PLC(Programmable Logic Controller)system to control the ratio control division and the assembly line division inside the chemical plant.A specific design and implementation method for development of SCADA/PLC based real time ratio control and automated assembly line system in a chemical plant is introduced.The assembly line division is further divided into sorting stage,filling stage and the auxiliary stage,which includes the capping unit,labelling unit and then the storage.In the ratio control division,we have defined the levels inside the mixer and ratio of the raw materials through human machine interface(HMI)panel.The ratio of raw materials is kept constant on the basis of flow rates of wild stream and manipulated stream.There is a flexibility in defining new levels and the ratios of the raw materials inside the mixer.But here we taken the predefined levels(low,medium,high)and ratios(3:4,2:1,2:5).Control valves are used for regulating the flow of the compositions.In the assembly line division,the containers are sorted on the basis of size and type of material used i.e.,big sized metallic containers and small sized non-metallic containers by inductive and capacitive proximity sensors.All the processes are facilitated with laser beam type or reflective type sensors on the conveyor system.Building a highly stable and dependable PLC/SCADA system instead of Distributed Control System is required to achieve automatic management and control of chemical industry processes to reduce waste manpower and physical resources,as well as to improve worker safety.

From the Current Industrial Stage to the Industry 4.0： Transformation Processes and Case Analysis of the FESTO Company

Despite of the acceleration of investments and the expansion of countries towards the Industry 4.0, companies have difficulties in planning the transition processes and implementation of the scenarios of Industry 4.0. To benefit from the Industry Approach 4.0, it is necessary to take technological and organizational transition processes into account, since the phenomenon involves interoperability between humans; between humans and machines; and between machines and production. This paper proposes to examine the transformation processes of the current industrial model to the Industry 4.0 model of FESTO AG, in addition to the framework proposition for the analysis of transformation processes for Industry 4.0. Through the face-to-face interviews and the institutional materials of FESTO, it was observed that the company inserted in its strategy of products and innovation the concept of Industry 4.0. To do so, FESTO planned and built a new production plant based on connectivity, sustainability, and collaborative environment, especially between man and machine. To support this orientation, FESTO has strengthened its technological base, culture, training of its productive, commercial, and management teams.

循环经济理念下的产业园4.0规划设计——以广州中创产业园为例

在国家推进碳中和的背景下,实现经济高质量发展尤为重要。循环经济具有降低能源消耗和碳排放的关键作用,是实现经济高质量发展的必由之路。以广州中创产业园为例,引入循环经济理念,对产业园区规划存在的问题进行分析,并进行系统性规划设计。同时,基于循环经济理念对产业园进行功能结构布局,进一步探讨物质循环系统、生态循环系统和智慧园区具体的规划策略。探索广州中创产业园4.0实现循环经济发展的路径与对策,为我国其他工业化地区建设面向循环经济的工业园区4.0提供参考。

面向物流4.0的高职物流工程综合实训室建设实践

实训室建设与管理是高等教育的重要内容,如何建设面向物流4.0的综合实训室是高职物流工程专业普遍关心的问题。介绍了物流4.0的概念,探讨其对从业能力的要求和对高职物流工程教育的挑战。分析了物流4.0对物流工程综合实训室和课程体系带来的变革要求。以浙江经济职业技术学院为例,详细介绍了物流工程综合实训室的建设过程,包括理念和目标、建设内容、建设成效和不足。可为我国高职院校物流工程教育中提供参考与借鉴。

Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

工业4.0下的质量管理

全球工业正面临第四次工业革命,即工业4.0。它指的是系统的连接和集成,以收集和分析数据,并将组织机构的运营数字化。质量4.0是与第四次工业革命相关的概念,指工业4.0背景下质量工作的数字化。探讨了质量4.0如何成为质量工作演进的一部分,重点是了解组织应如何过渡到质量4.0。提出了质量4.0的定义,并提出了过渡到质量4.0的总体过程,包括6个连续阶段,适用于计划过渡到质量4.0的不同组织机构。

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Intelligent Manufacturing in the Context of Industry 4.0： A Review

Our next generation of industry-lndustry 4.0-holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things （IoT）- enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the loT, cyber-physical systems （CPSs）, cloud computing, big data analytics （BDA）, and information and communications technology （ICT） that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

Digital Evaluation of Sitting Posture Comfort in Human-vehicle System under Industry 4.0 Framework

Most of the previous studies on the vibration ride comfort of the human-vehicle system were focused only on one or two aspects of the investigation. A hybrid approach which integrates all kinds of investigation methods in real environment and virtual environment is described. The real experimental environment includes the WBV（whole body vibration） test, questionnaires for human subjective sensation and motion capture. The virtual experimental environment includes the theoretical calculation on simplified 5-DOF human body vibration model, the vibration simulation and analysis within ADAMS/VibrationTM module, and the digital human biomechanics and occupational health analysis in Jack software. While the real experimental environment provides realistic and accurate test results, it also serves as core and validation for the virtual experimental environment. The virtual experimental environment takes full advantages of current available vibration simulation and digital human modelling software, and makes it possible to evaluate the sitting posture comfort in a human-vehicle system with various human anthropometric parameters. How this digital evaluation system for car seat comfort design is fitted in the Industry 4.0 framework is also proposed.

Challenges and Requirements for the Application of Industry 4.0:A Special Insight with the Usage of Cyber-Physical System

Considered as a top priority of industrial devel- opment, Industry 4.0 （or Industrie 4.0 as the German ver- sion） has being highlighted as the pursuit of both academy and practice in companies. In this paper, based on the review of state of art and also the state of practice in dif- ferent countries, shortcomings have been revealed as the lacking of applicable framework for the implementation of Industrie 4.0. Therefore, in order to shed some light on the knowledge of the details, a reference architecture is developed, where four perspectives namely manufacturing process, devices, software and engineering have been highlighted. Moreover, with a view on the importance of Cyber-Physical systems, the structure of Cyber-Physical System are established for the in-depth analysis. Further cases with the usage of Cyber-Physical System are also arranged, which attempts to provide some implications to match the theoretical findings together with the experience of companies. In general, results of this paper could be useful for the extending on the theoretical understanding of Industrie 4.0. Additionally, applied framework and proto- types based on the usage of Cyber-Physical Systems are also potential to help companies to design the layout of sensor nets, to achieve coordination and controlling of smart machines, to realize synchronous production with systematic structure, and to extend the usage of information and communication technologies to the maintenance scheduling.

Future Digital Design and Manufacturing: Embracing Industry 4.0 and Beyond

Digital design and manufacturing have been around for several decades from the numerical control of machine tools and automating engineering design in 1960s, through early Computer Aided Design （CAD）/Computer Aided Engineering analysis （CAE）/Computer Aided Manufacturing （CAM）, to modem digital design and manufacturing [1], and cloud manufacturing [2] converging into product lifecycle management （PLM） [3, 4] and Internet-enabled personalized manufacturing [5].

A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

There are numerous internet-connected devices attached to the industrial process through recent communication technologies,which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things(IIoTs).Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services,external trusted authorities,and centralized architectures;they have high computation and communication costs,low performance,and are exposed to a single authority of failure and bottleneck.Blockchain technology(BC)is widely adopted in the industrial sector for its valuable features in terms of decentralization,security,and scalability.In our work,we propose a decentralized,scalable,lightweight,trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application.The proposed scheme offers a double layer of data encryption,device authentication,scalability,high performance,low power consumption,and improves the industry’s operations;provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage.We also address data aggregation operations,which are considered challenging in electric power smart grids.We utilize a multi-chain proof of rapid authentication(McPoRA)as a consensus mechanism,which helps to enhance the computational performance and effectively improve the latency.The advanced reduced instruction set computer(RISC)machinesARMCortex-M33 microcontroller adopted in our work,is characterized by ultra-low power consumption and high performance,as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm(ECDSA).This improves the computational execution,increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer.Our experimental results show that the proposed scheme achieves excellent performance,data security,real-time data processing,low power consumption(70.880 mW),and very low memory utilization(2.03%read-only memory(RAM)and 0.9%flash memory)and execution time(0.7424 s)for the cryptographic algorithm.This enables autonomous network reconfiguration on-demand and real-time data processing.

Industry 4.0: Architecture and Equipment Revolution

The development of science and technology has led to the era of Industry 4.0.The core concept is the combination of“material and informationization”.In the supply chain and manufacturing process,the“material”of the physical entity world is realized by data,identity,intelligence,and information.Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization.The goal is“maximizing production efficiency,minimizing production costs,and maximizing the individual needs of human beings for products and services.”Achieving this goal will surely bring about a major leap in the history of the industry,which will lead to the“Fourth Industrial Revolution.”This paper presents a detailed discussion of industrial big data,strategic roles,architectures,characteristics,and four types of innovative business models that can generate profits for enterprises.The key revolutionary aspect of Industry 4.0 is explained,which is the equipment revolution.Six important attributes of equipment are explained under the Industry 4.0 perspective.

How AI-enabled SDN technologies improve the security and functionality of industrial IoT network:Architectures,enabling technologies,and opportunities

The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era

In smart industrial systems,in many cases,a fault can be captured as an event to represent the distinct nature of subsequent changes.Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis.Most event-based diagnosis techniques rely on perfect observations of observable events.However,in practice,it is common to miss an observable event due to a problem in sensorreadings or communication/transmission channels.This paper develops a fault diagnosis tool,referred to as diagnoser,which can robustly detect,locate,and isolate occurred faults.The developed diagnoser is resilient against missed observations.A missed observation is detected from its successive sequence of events.Upon detecting a missed observation,the developed diagnoser automatically resets and then,asynchronously resumes the diagnosis process.This is achieved solely based on postreset/activation observations and without interrupting the performance of the system under diagnosis.New concepts of asynchronous detectability and asynchronous diagnosability are introduced.It is shown that if asynchronous detectability and asynchronous diagnosability hold,the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations.The proposed technique is applied to diagnose faults in a manufacturing process.Illustrative examples are provided to explain the details of the proposed algorithm.The result paves the way towards fostering resilient cyber-physical systems in Industry4.0 context.