Aiming at the problem of the “inverse relationship” between the hardness (wear resistance) and toughness of the traditional single homogeneous structure coating on the surface of titanium alloy, the ...design and development of a high hardness, high toughness and wear-resistant coating with high reliability and long life is great significance for expanding the application of titanium alloy. Inspired by the microstructure of high-performance organisms in nature, the design ideas of multi-phase, multi-level and multi-scale hybrid reinforcement are used to give full play to the synergy, coupling and multi-functional response mechanism between different phases in the coating to obtain the wear-resistant coating with high hardness and high toughness. This article mainly reviews the research progress of several typical wear-resistant coatings with high hardness and high toughness on the surface of titanium alloy from the aspects of preparation process, microstructure, mechanical properties, and strengthening-toughening mechanisms, such as gradient structure coating, multi-scale structure coating and layered structure coating. On this basis, it is pointed out that in the future, the wear-resistant coating with high hardness and high toughness on the surface of titanium alloy should develop in the direction of developing intelligent manufacturing technology, optimal design and precise tailoring of microstructural architectures, and constructing the numerical simulation technology of composition-structure-performance. Furthermore, the authors propose the technology route for the controllable preparation of wear-resistant coating with high hardness and high toughness on the surface of titanium alloy, namely, through the coordination of theoretical calculations, numerical simulations, and intelligent manufacturing technologies to achieve the controllable preparation of the optimal structural coatings on the surface of titanium alloy, establish the functional relationship between composition-structure-performance, and accurately reveal the mechanism of strengthening and toughening, which provides a new idea for alleviating and balancing the bottleneck of the “inverse relationship” between hardness (wear resistance) and toughness, and achieving the preparation of titanium-based surface composite with excellent comprehensive properties.
As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability ...is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.
•The research mainly addresses digital twin for sustainable manufacturing supply chains.•A systematic literature review of 70 research papers on various dimensions of a digital twin for sustainable ...manufacturing is presented.•Technologies such as IoT, cloud computing, and blockchain have increased the potential of digital twin and the digital twin should include the "things" and "humans" from the entire supply chain.•A sustainable digital twin implementation framework for the supply chain is presented as an outcome of this study.
A digital twin is an integration of virtual and physical systems using disruptive technologies. More precisely, it is a method of developing sustainable, intelligent manufacturing systems for attaining robust quality, reducing time, and customized products using real-time information throughout the product life cycle. This paper presents a systematic literature review of 98 research papers on various digital supply chain twin dimensions with sustainable performance objectives. The selected papers were reviewed and classified into three broad categories: components of the digital twin, applications in the manufacturing supply chain, and sustainability. Based on the review and future perspectives from the study, we suggest that advancements in technologies such as IoT, cloud computing, and blockchain have increased the potential of digital twin applications in the supply chain. The results indicate that a digital supply chain twin should include the things and humans from the entire supply chain and not be restricted to the local manufacturing systems. Based on our review findings, we present a sustainable digital twin implementation framework for supply chains. The proposed framework will guide future practitioners and researchers.
•The KPCA_IRBF technique is firstly proposed for feature fusion.•A novel tool wear assessment technique based on KPCA_IRBF and GPR is developed.•GPR performs better than ANN and SVM in prediction ...accuracy.•The KPCA_IRBF technique helps to compress and smooth the confidence interval of GPR.
To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since theGaussiannoises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.
Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a ...revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
•An intelligent decision support system (DSS) based on data mining technology is applied to enterprises.•Data mining technology can analyze the statistical data from multiple angles and perspectives ...by modeling, classifying, and clustering a large amount of data.•The system can make the decision-making of enterprises more effective and scientific and finally obtain the satisfactory decision-making results.
To comprehensively understand the decision information system for the information processing of the intelligent manufacturing under Internet of Things, an intelligent decision support system (DSS) based on data mining technology is applied to enterprises to establish an Internet of Things-based intelligent DSS for manufacturing industry, thereby supporting the decision-makers in making intelligent decisions through the intelligent DSS. The research results show that data mining technology can analyze the statistical data from multiple angles and perspectives by modeling, classifying, and clustering a large amount of data, as well as discovering the correlations between the data. Also, in statistical work, the data are counted, and their correlations are utilized to support the decision analysis. Therefore, it can be concluded that the establishment of intelligent DSS for enterprises in manufacturing industry and the utilization of data mining technology as the key technology to achieve the system can make the decision-making of the manufacturing enterprises more effective and scientific. Eventually, the satisfactory decision-making results can be obtained.
Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks. ...The intelligent manufacturing (IM) systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms. The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network, which mitigates the severity of industrial chain disruption. In this study, we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks. Considering the constraints of the IM resources, we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.
Tool condition monitoring (TCM) in machining operations is crucial to maximise the useful tool life while reducing the risks associated with tool breakage. Unlike progressive tool wear, tool breakage ...occurs randomly, with more severe implications for workpiece quality, machining system stiffness, and even operator safety. Existing literature reviews on TCM focus on tool wear monitoring, including wear state recognition and remaining useful life prediction. However, a comprehensive review of tool breakage monitoring (TBM) techniques is lacking. Generic signal processing and intelligent decision-making methods cannot fully satisfy the practical requirements of the TBM. In addition, developing and evaluating TBM models using imbalanced data is more challenging. Herein, we present the first systematic review on TBM to bridge these limitations, and provide adequate guidance for avoiding catastrophic tool failures during cutting processes. Signal acquisition, feature extraction, and decision-making methodologies for the TBM are outlined and compared with related techniques for tool wear monitoring. The effects of data imbalance on TBM models are considered, and feasible solutions are provided at the data and algorithm levels. Finally, the challenges faced by the TBM are discussed, and potential research directions are suggested. The research and application of TBM techniques will certainly better empower various machining operations in response to intelligent manufacturing demands.
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•Tool breakage monitoring techniques in machining operations are systematically reviewed.•The difference between tool wear and breakage monitoring are compared.•TBM data imbalance problems are discussed at the data and algorithm levels.•The performance metrics commonly used to evaluate TBM models are highlighted.•The specific research route of tool life cycle management is proposed.
•A concept of production synchronization with pillars and measures is proposed.•A novel manufacturing mode-GMS is designed to organize production operations for FPAI.•A model for coordinated ...decision-making of production and delivery is developed.•IIoT-enabled GiMS promises to transform real-time visibility in operations.•Could services under GiMS facilitate synchronous operations with enhanced visibility.
The layout of fixed-position assembly islands (FPAI) offers flexibility and efficiency for the production of bulky or fragile products with medium variety and volumes. With the unique production operations and customized delivery requirements in FPAI, the manufacturing practitioners are plagued by long waiting times, frequent setups, and high finished product inventory levels, which are mainly caused by the unsynchronous organization and operations of production and delivery. For achieving synchronization of production and delivery with time windows in FPAI, this paper introduces a concept of production synchronization with three pillars of real-time visibility and information-sharing, coordination of decision-making and synchronized operations. Following the concept, a synchronization-oriented Graduation Manufacturing System (GMS) with distinct functional tickets, including job ticket (JT), setup ticket (ST), operation ticket (OT) and twined logistics ticket (LT) is designed to organize production operations in an integrated and synchronized manner in FPAI. The IIoT-enabled Graduation Intelligent Manufacturing System (GiMS) with real-time visibility and information-sharing is proposed for achieving real-time operational visibility in FPAI. Under GiMS, consider customer requirements and production constraints, a coordinated decision-making model of production and delivery with time windows for FPAI is developed. The observation and analysis of the case company show the effectiveness of the proposed concept and approach, with the highest synchronous degree between production and delivery as well as the best performance in simultaneity (lowest waiting times), punctuality (minimum number of tardy jobs) and cost-efficiency (lowest setup times).
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