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 proces...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 precision,customization,and productivity for analysis and modification.展开更多
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 process...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.展开更多
文摘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 precision,customization,and productivity for analysis and modification.
文摘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.
