intelligent manufacturing systems
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2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.


2021 ◽  
Author(s):  
Chunyan Duan ◽  
Mengshan Zhu ◽  
Kangfan Wang ◽  
Wenyong Zhou

Abstract Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears increasingly important. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Therefore, this paper devises a method based on improved FMEA model combined with machine learning for complex systems and applies it to the reliability management of intelligent manufacturing systems. The structured network of failure modes is constructed based on the knowledge graph for the intelligent manufacturing systems. The grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes, hereafter the clustering analysis is employed to extract the features of failure modes. The results show that the proposed method can more accurately reflect the coupling relationship between the failure modes compared with the conventional FMEA method. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.


2021 ◽  
Vol 26 (5) ◽  
pp. 625-645
Author(s):  
Yaping Fu ◽  
Yushuang Hou ◽  
Zifan Wang ◽  
Xinwei Wu ◽  
Kaizhou Gao ◽  
...  

Author(s):  
Jackson T. Veiga ◽  
Marcosiris A. O. Pessoa ◽  
Fabricio Junqueira ◽  
Paulo E. Miyagi ◽  
Diolino J. Dos Santos Filho

Author(s):  
Ercan Oztemel

Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufacturing functions in order to sustain total intelligent collaboration. Achieving this, on the other hand, ensures the improvement of manufacturing processes for better performance in an integrated manner. Note that, although some general information about intelligent manufacturing systems are given, this chapter is particularly focused on discussing intelligent quality related issues.


2020 ◽  
Vol 10 (22) ◽  
pp. 8300 ◽  
Author(s):  
Adriana Florescu ◽  
Sorin Adrian Barabas

The field of Flexible Manufacturing Systems (FMS) has seen in recent years a dynamic development trend and can now be considered an integral part of intelligent manufacturing systems and a basis for digital manufacturing. Developing the factory of the future in an increasingly competitive industrial environment involves the study and analysis of some FMS key elements and managerial, technical, and innovative efforts. Using a new approach, thus paper presents a material flow design methodology for flexible manufacturing systems in order to establish the optimal architecture of the analyzed system. The research offers a solution for modeling and optimizing material flows in advanced manufacturing systems. By using a dedicated analysis and simulation software, the structure of the system can be established and specific technical and economic parameters can be determined for each processing and transport capacity. Different processing scenarios will be evaluated through virtual modeling and simulations in order to increase the performance and efficiency of the system. Thus, an interactive tool useful in the design and management of flexible manufacturing lines will be developed for companies operating in the industrial sector. The application of this paper is mainly in the field of development of intelligent manufacturing systems, where the control system will make and use simulations in order to analyze current parameters and to predict the future.


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