Smart Manufacturing In Industry Research Articles

Enhancing Cybersecurity in Smart Manufacturing: A Comprehensive Approach

The industry 4.0 defines a new era of much efficient and intelligent manufacturing processes that works in integration with other advanced technologies like AI, Big data, Block chain, etc. The smart manufacturing industry has remarkably evolved due to integration of advanced technologies like cyber-physical systems (CPS), Internet of Things (IoT), digital twins that makes up a highly connected infrastructure where data flows in real time. However, the involvement of these technologies has also significantly increased the exposure of manufacturing industry to the cyber threats. This article investigates several security challenges and major threats that are associated with the manufacturing industry. It evaluates various security implementations including digital twins, machine learning, and block chain based on their effectiveness, scalability, and feasibility in terms of security. Through a comprehensive literature review, case study analysis of notable cyberattacks and qualitative exploratory methods, this study explores the best line of defence against the cyber threats. Our findings highlight the critical role of robust and adaptive multiple line of defense mechanisms in mitigating cyber risks. The research concludes by emphasizing the need for an effective security strategy that combines advanced technological solutions with continuous employee education and proactive threat management to safeguard smart manufacturing processes against evolving cyber threats.

Strategies for Smart Manufacturing Industry 5.0: High Quality Development for the Future

The proposal of Industry 5.0 is an effort and attempt to shape a new type of industrialization using human defined values. The core elements of Industry 5.0, including human centeredness, sustainable development, and resilience, are all different forms of high-quality development. This article proposes four corporate value innovation strategies - rainforest strategy, wetland strategy, grassland strategy, and oasis strategy - to provide management suggestions for traditional manufacturing enterprises to shift to smart manufacturing Industry 5.0. Human-robot collaboration, AI and machine learning, IoT and cyber-physical systems, sustainable manufacturing, human-centered workplace designs, resilient and flexible supply chains, and digital twin and simulation technologies are covered in the text. This article presents a detailed plan for the shift towards Industry 5.0, emphasizing the advantages and real-world uses of these sophisticated production techniques. On the new journey, what kind of response can smart manufacturing Industry 5.0 makes and how can they make forward-looking strategic preparations?

User-empowered secure privacy-preserving authentication scheme for Digital Twin

Digital Twin (DT) is a revolutionary technology changing how a smart manufacturing industry carries out its day-to-day activities. DT can provide numerous advantages such as real-time synchronised functioning, monitoring and data analysis. However, security and privacy issues in DT have not been thoroughly investigated. This article proposes a user-empowerment-based privacy-preserving authentication protocol for a cloud-based Digital Twin using a Decentralised Identifier (DID) and Verifiable Credential (VC). Here, user empowerment provides full control to users over their identities, and with the help of VC, users can prove their authenticity and preserve their privacy. Although DID has emerged as a promising technology for introducing user empowerment, it suffers from some fundamental problems such as usability and auditability. Here we address all these issues and propose a user-revocation-enabled security solution for the DT. A security analysis of the proposed scheme shows that it is secured against significant security threats. With the help of performance analysis, we prove that the proposed work effectively ensures security and privacy in DT.

Priority RPL for IOT Based Smart Manufacturing Industries

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.

СТРАТЕГІЇ РОЗВИТКУ РОЗУМНОГО ВИРОБНИЦТВА В ІНДУСТРІЇ 4.0: ПЕРЕВАГИ ТА ВИКЛИКИ

Given the principles of industrial engineering, any simplification of production processes should contribute to the achievement of Industry 4.0 goals, but this hypothesis needs to be confirmed through scientific research on its possible challenges, opportunities, and benefits. The aim of the research clarifying the conceptual foundations, advantages and challenges of smart manufacturing in Industry 4.0. The subject of the research features and mechanisms of smart manufacturing in Industry 4.0. The methods of the research method of analysis and synthesis, historical method, dialectical, systemic, logical. The hypothesis of the research: it is assumed that the introduction of Industry 4.0 technologies will increase efficiency by reducing transaction costs and information asymmetry within the system. The statement of basic materials: identifies five important features of Industry 4.0; summarizes modern approaches to production optimization; proves that the concept of Industry 4.0 should cover not only the value creation process as such, but also labor organization, business models and other services; highlights the advantages and challenges of integrating traditional with Industry 4.0 technologies. The originality and practical significance of the research: a comprehensive, in-depth analysis of the mechanisms of functioning of the Industry 4.0 strategy as a subordinate, hierarchical and dynamically interacting set of relations is carried out. Conclusions and perspectives of further research: Industry 4.0 technologies have a number of advantages and, therefore, have a good potential for return on investment. This applies to automation, production management, and decision-making systems. Industry 4.0, unlike the previous industrial era, enables historically unprecedented automation, customization, and profitability. Prospects for further research include exploring opportunities to improve production efficiency through human-machine interaction.

Precise geometry and pose measurement of in-hand objects with simple features using a multi-camera system

In precision pick-and-place tasks, robotic grasp errors can cause the failure of subsequent placement steps. Therefore, in-hand object pose measurement has attracted the attention of the intelligent manufacturing industry. However, existing algorithms have limited accuracy and insufficient practicality. Moreover, they often assume that the actual object geometry is identical to the CAD model. To alleviate the problems in terms of accuracy, practicality, and uncritical assumptions, this paper proposes an in-hand measurement solution based on multi-view reconstruction for objects with simple features. In this concept, multiple monocular cameras are deployed at the robot end to calculate the object’s actual geometry and 6D pose by reconstructing its key geometric features. An in-hand pose compensation strategy and a robot terminal system integrated with manipulation and measurement are developed. To verify the solution’s effectiveness, several experiments were carried out. First, grasp errors caused by multiple sources are intuitively demonstrated in a simulation environment. The in-hand measurement is validated to compensate for grasp errors through comparison experiments. Then, robots can rearrange various irregularly piled objects in real-world experiments based on the developed terminal system. Results demonstrate that maximum remaining position errors are within 2 mm with maximum angle errors of less than 1°. The maximum error compensation rate reaches 95%, which is superior to existing in-hand measurement algorithms. It can be observed that the proposed solution is valid for multiple objects. Geometry measurement can help robots calculate the in-hand object pose and identify the object’s category and availability. Therefore, the proposed measurement solution seems accurate and universal for robot applications in smart manufacturing industries, enhancing the accuracy and flexibility of robot manipulation.

Real-time Mixed Reality (MR) and Artificial Intelligence (AI) object recognition integration for digital twin in Industry 4.0

In the digital era, real-time object recognition and inspection, based on Artificial Intelligence (AI), have become vitally important in a wide range of applications. With the evolution of Industry 4.0, the integration of digital informatics and decisions is crucial for smart manufacturing, Logistics 4.0, and supply chain digitization. However, much of digital content lacks interconnection. As such, the Digital Twin (DT) was proposed to promote the integration of physical machines and devices with the digital space. Augmented reality (AR) usually works in conjunction with the DT network to collect and provide real-time data. In fact, most of the AR devices are not fully connected in DT as the decision support from AI is disconnected. On the other hand, human-machines and object interactions are still complicated in AR, thus limiting applications. In order to achieve digital twinning for object recognition and human-machine interaction, we proposed a DT architecture for integrating the latest Mixed Reality (MR) device for real-time data streaming. Object recognition is empowered by the AI algorithm in the backend cloud computing and database. The entire system integrates the informatics between physical machines and devices and the digital content using an AI algorithm. Our proposed method achieved real-time informatics integration of MR and AI, while the warehouse is used as a case scenario with simulations. Industrial informatics integration can be applied for smart manufacturing, smart warehouse, and many Industry 4.0 applications.

A secured blockchain method for multivariate industrial IoT-oriented infrastructure based on deep residual squeeze and excitation network with single candidate optimizer

IoT infrastructures have been growing in recent years across a variety of industrial applications in sustainable smart cities and communities, including smart manufacturing and smart industries. Infrastructure geared towards Internet of Things also includes Cyber-Physical System (CPS). With a distributed environment, CPS has experienced significant success in industrial applications and critical infrastructure. However, there are numerous difficulties in such an environment, including those related to scalability, centralization, communication latency, and security and privacy. Deep residual squeeze and excitation network (DRSEN) with Single Candidate Optimizer-based IoT-oriented infrastructure is used in this manuscript to address these challenges. Blockchain creates a distributed environment for CPS's communication phase, and Software-Defined Networking (SDN) establishes protocols for data forwarding in network. At the application layer of suggested infrastructure, a deep learning-based cloud is used to address communication latency, centralization, and scalability issues. It makes high-performance, affordable computing resources available for smart city applications like smart industrial and smart transportation. The proposed method is implemented in ns-3 tool and the effectiveness is assessed using F-measure, reliability, scalability, accuracy, sensitivity, specificity, and precision. The proposed method provides 35.39%, 23.87% and 20.67% higher reliability, 42.39%, 11.39%, 34.16% and 25.78% higher accuracy, 15.02%, 26.64% and 37.55% higher F-measure compared with existing techniques like Industrial Internet of Things based on Blockchain Secure Device Authentication Solution (IIoT-BC-BASA), Industrial Internet of things based on Blockchain- InterPlanetary File System (IIoT-BC-IPFS), Industrial Internet of Things based on Blockchain- Shamir Threshold Cryptography (IIoT-BC-SHC), Industrial Internet of Things based on Blockchain- software Define Networking (IIoT-BC-SDN) methods, respectively.

Examining a generic streaming architecture for smart manufacturing's Big data processing in Anomaly detection: A review and a proposal

The smart manufacturing industry has witnessed a rapid increase in data generation due to the integration of sensors, IoT devices, and other advanced technologies. With this huge amount of data, the need for efficient data processing methods becomes critical for identifying anomalies in real-time. With the rise of Industry 4.0 practices, digitally enabled manufacturing units are shifting their focus towards Smart Manufacturing paradigm for better productivity, throughput and increased business volume. Traditionally digital manufacturing units have considered different AI approaches like Neural Network, Statistical Methods, Deep Learning etc. to detect and predict anomalies in their production lines. But with the Smart Manufacturing ecosystem, a manufacturing unit must integrate manufacturing intelligence in real-time across entire production lines through sensor data of IOT devices. Hence the traditional anomaly detection systems fall short to respond well, under the changed scenario, where large volumes of unstructured and varied types of data are being generated at high velocity, to be processed at (soft) real time. The article reviews the current state-of-the-art in big data processing for anomaly detection in smart manufacturing. The review covers various aspects such as data collection, data processing, anomaly detection, and real-time monitoring. The current paper also proposes a novel stateful data streaming computational model for big data processing in smart manufacturing units which conceptually lays the foundation on top of which any discrete anomaly detection engine would be able to work. The proposed architecture has several benefits, including its ability to handle the large volume, velocity, and variety of data generated in smart manufacturing. The architecture can be applied to various smart manufacturing applications, including predictive maintenance, quality control, and supply chain optimization. It is expected that this proposed architecture will pave the way for the development of more efficient and effective smart manufacturing systems in the future.

An Unsupervised Vibration Noise Reduction Approach and Its Application in Lubrication Condition Monitoring

Accelerometers are sensitive devices that capture vibrational fault signatures from industrial machines. However, noise often contaminates these fault signatures and must be eliminated before analysis. A data-driven (DD) denoising algorithm capable of filtering useful vibrational fault signatures from background noises was derived in this study. The algorithm was first validated by comparing its denoised result with a numerically generated ideal signal with a known exact solution. The DD denoising approach reduced the Mean Squared Error (MSE) from 0.459, when no denoising was performed, to 0.068, indicating an 85.2% decrease in noise. This novel approach outperformed the Discrete Wavelet (DW) denoising approach, which had an MSE of 0.115. The proposed DD denoising algorithm was also applied to preprocess vibration data used for the real-time lubrication condition monitoring of the plastic injection molding machine’s toggle clamping system, thereby reducing false positive relubrication alarms. The false positive rates, when analysis was performed on the raw vibration and the DW denoised vibration, were 10.7% and 7.6%, respectively, whereas the DD denoised vibration yielded the lowest false positive rate at 1%. This low false positive rate of the DD denoised vibration indicates that it is a more reliable condition monitoring system, thereby making this technique suitable for the smart manufacturing industry.

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field.

Industrial Social Robots (SOBOTS) to Enhance Smart Production Systems

This paper explores the potential use of social robots in smart manufacturing industries, presenting several use cases where such robots could bring benefits. The design considerations for developing social robots that can effectively operate in industrial production environments are also discussed, using the TRIZ methodology. Furthermore, the paper highlights the advantages of using social robots together with traditional robots in some use cases. The aim is to provide insights on how social robots can be designed and utilized to improve efficiency, safety, and worker satisfaction in smart industrial production systems.

A Provable Secure and Efficient Authentication Framework for Smart Manufacturing Industry

A Provable Secure and Efficient Authentication Framework for Smart Manufacturing Industry

Role of cyber-physical system and Industry 4.0 in smart manufacturing industries for business excellence

Role of cyber-physical system and Industry 4.0 in smart manufacturing industries for business excellence

Zero-Trust Model for Smart Manufacturing Industry

Traditional security architectures use a perimeter-based security model where everything internal to the corporate network is trusted by default. This type of architecture was designed to protect static servers and endpoints; however, we need to adapt to emerging technologies where serverless applications are running on containers, mobile endpoints, IoT, and cyber-physical systems. Since the beginning of the fourth industrial revolution (Industry 4.0), there has been a massive investment in smart manufacturing which responds in real-time to the supply chain and connects the digital and physical environments using IoT, cloud computing, and data analytics. The zero-trust security model is a concept of implementing cybersecurity techniques considering all networks and hosts to be hostile irrespective of their location. Over the past few years, this model has proven to be a remarkably effective security solution in conventional networks and devices. In this paper, the zero-trust approach will be fully explored and documented explaining its principles, architecture, and implementation procedure. It will also include a background of the smart manufacturing industry and a review of the existing cyber security solutions followed by a proposed design of the zero-trust model along with all the enabling factors for on-premises and cloud-hosted infrastructure. Various security solutions such as micro-segmentation of the industrial network, device discovery, and compliance management tools that are essential in achieving complete zero-trust security are considered in the proposed architecture.

Performance and Extreme Conditions Analysis Based on Iterative Modelling Algorithm for Multi-Trailer AGVs

Automatic guidance vehicles (AGV) are industrial vehicles that play an important role in the development of smart manufacturing systems and Industry 4.0. To provide these autonomous systems with the flexibility that is required today in these industrial workspaces, AGV computational models are necessary in order to analyze their performance and design efficient planning and control strategies. To address these issues, in this work, the mathematical model and the algorithm that implement a computational control-oriented simulation model of a hybrid tricycle-differential AGV with multi-trailers have been developed. Physical factors, such as wheel-ground interaction and the effect of vertical and lateral loads on its dynamics, have been incorporated into the model. The model has been tested in simulation with two different controllers and three trajectories: a circumference, a square, and an s-shaped curve. Furthermore, it has been used to analyze extreme situations of slipping and capsizing and the influence of the number of trailers on the tracking error and the control effort. This way, the minimum lateral friction coefficient to avoid slipping and the minimum ratio between the lateral and height displacement of the center of gravity to avoid capsizing have been obtained. In addition, the effect of a change in the friction coefficient has also been simulated.

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.

Design and implementation of Visual Epigynous Machine on Robotic Sorting

Robotic assembly is an important process to replace manual picking in industrial smart manufacturing. One of the important steps in assembly is robotics grasping and sorting of the workpiece. In this paper, a lightweight visual epigynous system was designed to address sorting workpieces with high efficiency, portability, and accuracy. The software of the system was divided into a vision calculation module (VCM), an ethernet communication module, a system control module, and a real-time display module. VCM is responsible for obtaining the information of workpieces, which includes segmenting the images captured by the webcam sequentially and recognizing the digit, and calculating the angle by algorithms of AKAZE feathers matching with and orientation estimation for vector comparison. The proposed method in VCM performs best in estimating the orientation of the workpiece through the comparison of different template matching algorithms. The industrial field experiments indicated that the robot could satisfy the requirement of visual sorting. This system provides a practical and robust solution for industrial workpiece sorting applications.

FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries.

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

Automatic tolerance analyses by generation of assembly graph and mating edges from STEP AP 242 file of mechanical assembly

Purpose This paper aims to propose a method to automate the tolerance analyses of mechanical assembly using STandard for the Exchange of Product model data-Application Protocol Part 242 (STEP AP 242) files derived from the 3-D computer-aided design (CAD) models. Design/methodology/approach Product manufacturing information and mating information available in ISO 10303 STEP AP242 files resulting from the 3-D CAD model of mechanical assembly are extracted. The extracted geometric attributes, geometric dimensioning and tolerancing (GD&T) and mating information are used to automatically generate assembly graph and mating edges required for the tolerance analyses of the mechanical assembly by using the matrix approach. Findings The feasibility of the proposed method is verified through two mechanical assembly case studies. The results of manual calculations and tolerance values computed by the automated method are very closely matching. Practical implications Tolerance analysis is an integral part of product development that directly influences the cost and performance of a product. Apart from the academic interest, the work is expected to have positive implications for the digital design and smart manufacturing industry that involve in the development of solutions for automation of design and manufacturing system functions. Originality/value The approach presented in the paper that aids the automation of tolerance analyses of mechanical assembly is an innovative application of the STEP AP 242 file. The automation of tolerance analyses would improve the productivity and efficiency of the product realization process.