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Virtual manufacturing in Industry 4.0:A review

Data Science and Management 2024年第1期7卷 47-63页

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of KyreniaKyreniaNorth CyprusVia Mersin 10Turkey CAD/CAPP/CAM Research Center Department of Mechanical EngineeringAmirkabir University of Technology(Tehran Polytechnic)Tehran15875-4413Iran Department of Computer Engineering Cyprus International UniversityNorth CyprusVia Mersin 10Turkey

Virtual manufacturing is one of the key components of Industry 4.0,the fourth industrial revolution,in improving manufacturing *** manufacturing enables manufacturers to optimize their production processes using real-time data from sensors and other connected devices in Industry ***-based virtual manufacturing platforms are a critical component of Industry 4.0,enabling manufacturers to design,test,and optimize their processes collaboratively and *** 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 *** data analytics can be used in conjunction with virtual manufacturing to provide valuable insights and optimize production processes in Industry *** 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 *** 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 *** limitations and advantages of virtual manufacturing systems in Industry 4.0 are discussed,and future research projects are also ***,productivity in the part manufacturing process can be enhanced by reviewing and analyzing the applications of virtual manufacturing in Industry 4.0.

关键词： Virtual manufacturing Industry 4.0

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Additive manufacturing modification by artificial intelligence, machine learning, and deep learning: A review

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Additive Manufacturing Frontiers 2025年第2期4卷

作者： Soori, Mohsen Jough, Fooad Karimi Ghaleh Dastres, Roza Arezoo, Behrooz Department of Civil Engineering Final International University Girne 99370 Turkey Department of Computer Engineering Cyprus International University Nicosia 99258 Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran 15875-4413 Iran

The manufacturing sector has been transformed owing to additive manufacturing (AM), which has made it possible to create intricate, personalized items with little material waste. However, optimizing and enhancing AM processes remain challenging owing to the intricacies involved in design, material selection, and process parameters. This review explores the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques to improve and innovate in the field of AM. AI-driven design optimization procedures offer innovative solutions for the 3D printing of complex geometries and lightweight structures. By leveraging machine learning (ML) algorithms, these procedures analyze extensive data from previous manufacturing processes to enhance efficiency and productivity. ML models facilitate design and production automation by learning from historical data and identifying intricate patterns that human operators may miss. Deep learning (DL) further augments this capacity by utilizing sophisticated neural networks to manage and interpret complex information and provide deeper insights into the manufacturing process. Integrating AI, ML, and DL into AM enables the creation of optimized, lightweight components that are crucial for reducing fuel consumption in the automotive and aviation industries. These advanced AI techniques optimize the design and production processes and enhance predictive modeling for process optimization and defect detection, leading to improved performance and reduced manufacturing costs. Therefore, integrating AI, ML, and DL into AM improves precision in component fabrication, enabling advanced material design innovations and opening new possibilities for innovation in product design and material science. This review discusses and highlights significant advancements and identifies future directions for applying AI, ML, and DL in AM. By leveraging these technologies, AM processes can achieve unprecedented levels of preci

关键词： Additive manufacturing Artificial intelligence Deep learning Machine learning Optimization

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Artificial neural networks in supply chain management, a review

Journal of Economy and Technology

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Journal of Economy and Technology 2023年 1卷 179-196页

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey

Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired by the structure and function of the human brain. In the context of supply chain management, ANNs can be used for demand forecasting, inventory optimization, logistics planning, and anomaly detection. ANNs help companies to optimize their inventory levels, production schedules and procurement activities in terms of productivity enhancement of part production. By considering multiple variables and constraints, ANNs can identify the most efficient routes, allocate resources effectively, and reduce costs. Furthermore, ANNs can identify anomalies as well as abnormalities in supply chain data, such as unexpected demand patterns, quality issues and disruptions in logistics operations in order to minimize their impact on the supply chain. ANNs can also analyze supplier performance data, including quality, delivery times and pricing in order to assess the reliability and effectiveness of suppliers. This information can support decision-making processes in supplier evaluation and selection processes. Moreover, ANNs can continuously monitor supplier performance, raising alerts for deviations from predefined criteria to provide safe and secure supply chain in part production processes. By analyzing various data sources, including weather conditions, and political instability, ANNs can identify and mitigate risks in terms of safety enhancement of supply chain processes. Artificial neural networks in supply chain management is studied in the research work to analyze and enhance performances of supply chain management in process of part manufacturing. New ideas and concepts of future research works are presented by reviewing and analyzing of recent achievements in applications of artificial neural networks in supply chain management. Thus, productivity of part manufacturing can be enhanced by promoting the supply chain management using the artificial neural networks.

关键词： Artificial neural networks Supply chain management

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AI-powered blockchain technology in industry 4.0, a review

Journal of Economy and Technology

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Journal of Economy and Technology 2023年 1卷 222-241页

作者： Mohsen Soori Roza Dastres Behrooz Arezoo Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875–4413 Iran

Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. Smart contracts, powered by blockchain, can be developed by using the AI capabilities. These contracts can execute themselves based on predefined conditions, automating various processes and reducing the need for intermediaries. Blockchain provides a transparent and immutable ledger for supply chain transactions. AI can be integrated to analyze this data, providing insights into the entire supply chain. This helps in tracking and tracing products, ensuring authenticity, and optimizing the supply chain based on data-driven decisions. AI algorithms can be employed to analyze data from the manufacturing process recorded on the blockchain. This allows for predictive maintenance and quality control, helping to identify and address issues before they result in defects or downtime. AI and blockchain can work together to secure the massive amount of data generated by IoT devices in an Industry 4.0 setting. Blockchain ensures the integrity and immutability of IoT data, while AI can analyze this data for insights and optimization. AI and blockchain can facilitate the creation of decentralized energy grids where AI algorithms optimize energy distribution and blockchain ensures transparent and secure energy trading through smart contracts. AI and blockchain can support decentralized and collaborative manufacturing networks where different entities can securely and efficiently collaborate on production processes. Blockchain enables individuals or organizations to securely monetize their data. AI can help analyze and determine the value of data, ensuring fair compensation in tokenized ecosystems. In this paper, applications of AI-Powered Blockchain Technology in Industry 4.0 is reviewed and discussed and future research works are also suggested. As a result, efficiency, transparency, and security across various industrial processes can be enhanced

关键词： Artificial Intelligence Blockchain AI-Powered Blockchain Industry 4.0

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Retraction Note: Minimization of Surface Roughness and Residual Stress in Grinding Operations of Inconel 718

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Journal of Materials Engineering and Performance 2024年第17期33卷 9283-9283页

作者： Soori, Mohsen Arezoo, Behrooz Department of Aeronautical Engineering University of Kyrenia Kyrenia Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

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Blockchains for industrial Internet of Things in sustainable supply chain management of industry 4.0, a review

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Sustainable Manufacturing and Service Economics 2024年 3卷

作者： Mohsen Soori Fooad Karimi Ghaleh Jough Roza Dastres Behrooz Arezoo Department of Civil Engineering Final International University AS128 Kyrenia North Cyprus Via Mersin 10 Turkey Department of Computer Engineering Cyprus International University North Cyprus Via Mersin 10 Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran

The integration of blockchain technology in the Industrial Internet of Things (IIoT) for sustainable supply chain management in the context of Industry 4.0 offers several potential benefits. A public and auditable record of the environmental impact of each supply chain stage can be made using blockchain technology. A more streamlined and effective supply chain is made possible by blockchain's decentralized structure. Delays, mistakes, and the need for middlemen are decreased by real-time access to a shared ledger. IIoT devices like sensors and RFID tags can provide real-time data on the location, condition, and environmental parameters of goods. Blockchain can then be used to record and incentivize sustainable practices, such as reducing energy consumption or minimizing waste. The integration of blockchain with IIoT can develop the supply chain management for enabling real-time tracking of goods, optimizing inventory management, and ensuring compliance with sustainability standards. The paper provides a comprehensive overview of the key challenges facing traditional supply chains and how the combined use of Blockchains and IIoT technologies. The review also evaluates the environmental, social, and economic implications of adopting Blockchain-enabled IIoT solutions in supply chain operations. Furthermore, the review assesses the current state of research and development, identifying gaps in existing literature and proposing avenues for future exploration. As a results, by highlighting the synergies between these technologies, it seeks to inspire further innovation and adoption, ultimately fostering a more resilient, transparent, and environmentally conscious industrial ecosystem.

关键词： Blockchains Internet of Things Sustainable supply chain Industry 4.0

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AI-Based Decision Support Systems in Industry 4.0, A Review

Journal of Economy and Technology

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Journal of Economy and Technology 2024年

The development of modern technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) resulted in a new era of industrial automation and data interchange, which is known as Industry 4.0. AI-based decision support systems (DSS) play a crucial role in this paradigm by enhancing the integration and processing of IoT and sensor data to optimize operations, improve productivity, and enable predictive maintenance. Machine learning models analyze production data and visual inspections to identify defects and ensure product quality. This review paper explores the transformative role of AI in enhancing DSS within Industry 4.0, highlighting key technologies including machine learning, deep learning, and natural language processing. It explores a number of applications, including supply chain optimization, energy management, predictive maintenance, quality control, and production planning, showing how AI-driven DSS can significantly boost operational dependability, cut costs, and improve efficiency. The article also discusses the AI-based DSS's architecture and implementation, with a focus on data management, user interface design, and IoT integration. Furthermore, it examines the challenges related to data quality, technical integration, and human factors, offering potential solutions and strategies for effective deployment. The study highlights the continued development of AI technologies and their potential to support autonomous decision-making in industrial settings by identifying new trends and areas for further research. This comprehensive review aims to provide valuable insights for researchers and practitioners, fostering a deeper understanding of the capabilities and future potential of AI-based DSS in Industry 4.0.

关键词： AI-Based Decision Support Systems Industry 4.0

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Digital twin for smart manufacturing, A review

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Sustainable Manufacturing and Service Economics 2023年 2卷

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of Kyrenia North Cyprus Via Mersin 10 Kyrenia Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran Department of Computer Engineering Cyprus International University North Cyprus Via Mersin 10 Turkey

A virtual representation of a physical procedure or product is called digital twin which can enhance efficiency and reduce costs in manufacturing process. Utilizing the digital twin, production teams can examine various data sources and reduce the number of defective items to enhance production efficiency and decrease industrial downtime. Digital Twin can be utilized to visualize the asset, track changes, understand and optimize asset performance throughout the analysis of the product lifecycle. Also, the collected data from digital twin can provide the complete lifecycle of products and processes to optimize workflows of part production, manage supply chain, and manage product quality. The application of digital twin in smart manufacturing can reduce time to market by designing and evaluating the manufacturing processes in virtual environments before manufacture. Comprehensive simulation platforms can be presented using digital twins to simulate and evaluate product performances in terms of analysis and modification of produced parts. Commissioning time of a factory can also be significantly reduced by developing and optimizing the factory layout using the digital twin. Also, the productivity of part manufacturing can be enhanced by providing the predictive maintenance and data-driven root-cause analysis during part production process. In this paper, application of digital twin in smart manufacturing systems is reviewed to analyze and discuss the advantages and challenges of part production modification using the digital twin. So, the research field can advance by reading and evaluating previous papers in order to propose fresh concepts and approaches by using digital twins in smart manufacturing systems.

关键词： Smart manufacturing Digital twin Manufacturing optimization

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Machine learning and artificial intelligence in CNC machine tools, A review

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Sustainable Manufacturing and Service Economics 2023年 2卷

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran Department of Computer Engineering Near East University Via Mersin 10 Lefkosha North Cyprus Turkey

Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.

关键词： Machine learning Artificial intelligence CNC machine tools

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