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.

Virtual manufacturing in Industry 4.0:A review

Virtual manufacturing is one of the key components of Industry 4.0,the fourth industrial revolution,in improving manufacturing processes.Virtual manufacturing enables manufacturers to optimize their production processes using real-time data from sensors and other connected devices in Industry 4.0.Web-based virtual manufacturing platforms are a critical component of Industry 4.0,enabling manufacturers to design,test,and optimize their processes collaboratively and efficiently.In Industry 4.0,radio frequency identification(RFID)technology is used to provide real-time visibility and control of the supply chain as well as to enable the automation of various manufacturing processes.Big data analytics can be used in conjunction with virtual manufacturing to provide valuable insights and optimize production processes in Industry 4.0.Artificial intelligence(AI)and virtual manufacturing have the potential to enhance the effectiveness,consistency,and adaptability of manufacturing processes,resulting in faster production cycles,better-quality products,and lower prices.Recent developments in the application of virtual manufacturing systems to digital manufacturing platforms from different perspectives,such as the Internet of things,big data analytics,additive manufacturing,autonomous robots,cybersecurity,and RFID technology in Industry 4.0,are discussed in this study to analyze and develop the part manufacturing process in Industry 4.0.The limitations and advantages of virtual manufacturing systems in Industry 4.0 are discussed,and future research projects are also proposed.Thus,productivity in the part manufacturing process can be enhanced by reviewing and analyzing the applications of virtual manufacturing in Industry 4.0.

Industry 4.0: Revolution or Evolution?

“Industry 4.0” term is devoted to the fourth industrial revolution. Over time and by developing different technologies, this term is coming with the new paradigm and technologies, which help to connect the machines, products, and methods as an interconnected system. This paper aims to introduce an analysis and a reflection around the concepts industry 4.0 and their impacts in the actual industrial world. The effects of this digitalization will be investigated on supply chain systems, decision-making processes, and business models. The classic supply chain is evolving into a Network Supply System (NSS) that is an interconnected supply chain with more focus on product and customer expectations. The global value chain process tends to be product-oriented. Smart data make the decisions more dynamic, flexible, and precise. Therefore, every industrial sector has to be adapted to this digital transformation in all aspects. However, the environmental aspects, global warming, and human healthcare issues are the challenge facing industries and human life, which can be like a brake to make efforts to improve digital life and machine technicity. This paper tries to produce a critical analysis of the concept “industry 4.0 revolution” based on different guidelines to show that it is an evolution of the industry coming through the development of several technologies.

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.

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].

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.

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.

Macro Perspective of Industry 4.0： Factors Affecting Value Creation Modules

Business needs to manage such a variety of things inside the firm or outside the business consistently. This factor affects the business in various ways. The factor which affects the business is clients, suppliers, distributors and so on. These all components are isolated from two sections full-scale environment and miniaturized scale environment. Macro perspective environment components are those external variables which are past the association＇s control. The problem of this study is guaranteeing a supportable evolvement of human presence in its social, ecological, and monetary measurement. So, we have to adapt test in order to outfit toward manageability. As for the method used in this study, it depends on using empirical framework to meet the interest of capital and buyer, in addition to supporting human presence in its social, ecological, monetary measurement. The paper is organised as following sections： first section is the introduction, second section is the literature review, the third section includes the research method, fourth one discusses the analysis, while the conclusion is in the fifth section.

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.

Mutated Leader Sine-Cosine Algorithm for Secure Smart IoT-Blockchain of Industry 4.0

In modern scenarios,Industry 4.0 entails invention with various advanced technology,and blockchain is one among them.Blockchains are incorporated to enhance privacy,data transparency aswell as security for both large and small scale enterprises.Industry 4.0 is considered as a new synthesis fabrication technique that permits the manufacturers to attain their target effectively.However,because numerous devices and machines are involved,data security and privacy are always concerns.To achieve intelligence in Industry 4.0,blockchain technologies can overcome potential cybersecurity constraints.Nowadays,the blockchain and internet of things(IoT)are gaining more attention because of their favorable outcome in several applications.Though they generate massive data that need to be effectively optimized and in this research work,deep learning-based techniques are employed for this.This paper proposes a novel mutated leader sine cosine algorithm-based deep convolutional neural network(MLSC-DCNN)in order to attain a secure and optimized IoT blockchain for Industry 4.0.Here,an MLSC is hybridized using a mutated leader and sine cosine algorithm to enhance the weight function and minimize the loss factor of DCNN.Finally,the experimentation is carried out for various simulation measures.The comparative analysis is made for Best Tip Selection Method(BTSM),Smart Block-Software Defined Networking(SDN),and the proposed approach.The evaluation results show that the proposed approach attains better performances than BTSM and SDN.

Fault Handling in PLC-Based Industry 4.0 Automated Production Systems as a Basis for Restart and Self-Configuration and Its Evaluation

Industry 4.0 and Cyber Physical Production Systems (CPPS) are often discussed and partially already sold. One important feature of CPPS is fault tolerance and as a consequence self-configuration and restart to increase Overall Equipment Effectiveness. To understand this challenge at first the state of the art of fault handling in industrial automated production systems (aPS) is discussed as a result of a case study analysis in eight companies developing aPS. In the next step, metrics to evaluate the concept of self-configuration and restart for aPS focusing on real-time capabilities, fault coverage and effort to increase fault coverage are proposed. Finally, two different lab size case studies prove the applicability of the concepts of self-configuration, restart and the proposed metrics.

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.

FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an Industry 4.0 network is very complex and challenging.To manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network services.It follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response time.An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time.After implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time model.It should also be noted that the average response time increases when the arrival rate increases.

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.

I4EU(Industry 4.0 for European Union)--An European Initiative to Develop Training Activity

This paper presents an on-going European ERASMUS project to develop training programs and tools for Industry 4.0.After an introduction on the background and objective of the project,the paper will give an overview on the structure,partners and organization of the project.Based on the State-of-the-art,the targeted intellectual outputs(IOs)will be presented in detail and the set of planned activities to achieve IOs are outlined.The project progress and preliminary results are shown and the concluding summary will be given at the end of the paper.

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.

A Mini-Review and Perspective on Current Best Practice and Emerging Industry 4.0 Methods for Risk Reduction in Advanced Composites Manufacturing

The manufacturing of composite structures is a highly complex task with inevitable risks, particularly associated with aleatoric and epistemic uncertainty of both the materials and processes, as well as the need for in-situ decision-making to mitigate defects during manufacturing. In the context of aerospace composites production in particular, there is a heightened impetus to address and reduce this risk. Current qualification and substantiation frameworks within the aerospace industry define tractable methods for risk reduction. In parallel, Industry 4.0 is an emerging set of technologies and tools that can enable better decision-making towards risk reduction, supported by data-driven models. It offers new paradigms for manufacturers, by virtue of enabling in-situ decisions for optimizing the process as a dynamic system. However, the static nature of current (pre-Industry 4.0) best-practice frameworks may be viewed as at odds with this emerging novel approach. In addition, many of the predictive tools leveraged in an Industry 4.0 system are black-box in nature, which presents other concerns of tractability, interpretability and ultimately risk. This article presents a perspective on the current state-of-the-art in the aerospace composites industry focusing on risk reduction in the autoclave processing, as an example system, while reviewing current trends and needs towards a Composites 4.0 future.

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.

Special Issue on Future Digital Design and Manufacturing:Embracing Industry 4.0 and Beyond-Part Ⅱ

Digital design and manufacturing have been under pinned by digital modeling, simulation, and automation controls for decades. Under the new market requirement of mass customized products and services, the advancements in artificial intelligence （AI）, smart technology, virtual reality （VR）, big data, digital twin, robotics and human-centered design are becoming driving forces for the development of future digital design and manufacturing. This special issue focuses on the future digital design and manufacturing especially under the Industry 4.0 framework and beyond. This editorial introduces the papers in this special issue, which linked to the International Workshop on Digital Design and Manufacturing Technologies - Embracing Industry 4.0 and Beyond at Northumbria University in Newcastle, UK, held on 12-13 April 2016. In the Part I of the issue [1], there are 13 papers published in 2016, Vol- ume 29, No 6 of the Chinese Journal of Mechanical Engineering （this journal）.

Blockchain Based Secure Solution for Cloud Storage:A Model for Synchronizing Industry 4.0 and IIoT

Industry 4.0 is one of the hot topic of today’s world where everything in the industry will be data driven and technological advancements will take place accordingly.In the fourth phase of industrial revolution,manufacturers are dependent upon data produced by the consumers to invent,innovate or change anything for the product.Internet of things devices like OBD,RFID,IIoT,Smart devices are the major source of data generation and represents trends in the industry.Since the IoT device are vulnerable to hackers due to its limitation,consumer data security should be tighten up and enhanced.This paper gives an overview of industrial revolutions as well as proposes Blockchain Cloud Computing as a solution to store data for Industry 4.0.