Machine Intelligence in Mechanical Engineering explains the latest applications of machine intelligence and data-driven decision-making in mechanical engineering industries. By providing introductory theory, trouble-shooting case studies, detailed algorithms and implementation instructions, this interdisciplinary book will help readers explore additional applications in their own fields. Those with a mechanical background will learn the important tasks related to preprocessing of datasets, feature extraction, verification and validation of machine learning models which unlock these new methods.
Machine Intelligence is currently a key topic in industrial automation, enabling machines to solve complex engineering tasks and driving efficiencies in the smart production line. Smart preventative maintenance systems can prevent machine downtime, smart monitoring and control can produce more effective workflows with less human intervention.
1. Chapter 1 : Machine intelligence in mechanical engineering: an introduction - p. Page x 2. Chapter 2 : A smart production line management system using face recognition and augmented reality - p. Page 9 3. Chapter 3 : Maintenance planning optimization through equipment performance prediction using machine learning based on inline instrument datasets—a surface condenser case study - p. Page 25 4. Chapter 4 : Minimizing intercellular movement of parts and maximizing the utilization of machines using the correlation index-based clustering algorithm - p. Page 52 5. Chapter 5 : Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment - p. Page 61 6. Chapter 6 : Application of machine vision technology in manufacturing industries—a study - p. Page 86 7. Chapter 7 : Estimation of wing stall delay characteristics with outward dimples using numerical analysis - p. Page 118 8. Chapter 8 : An Internet of Things-based integrative safety framework of autonomous vehicles for special needs society - p. Page 134 9. Chapter 9 : Motion planning and control for autonomous vehicle collision avoidance systems using potential field-based parameter scheduling - p. Page 146 10. Chapter 10 : Long-term predictive maintenance system with application and commercialization to industrial conveyors - p. Page 174 11. Chapter 11 : Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review - p. Page 189 12. Chapter 12 : Application of computationally intelligent modeling to glass fiber reinforced polymer drilling - p. Page 225 13. Chapter 13 : Applied advanced analytics in marketing of mechanical products - p. Page 247 14. Chapter 14 : Information and communication technologies: enablers for the successful implementation of supply chain 4.0 - p. Page 285 15. Chapter 15 : A pilot study and development of prediction model for tire compound quality - p. Page 296 16. Chapter 16 : Machine intelligence based learning for ecological transportation - p. Page 309 17. Chapter 17 : A review on the social impacts of automation on human capital in Malaysia - p. Page 324 18. Chapter 18 : Autonomous systems with intelligent agents - p. Page 339 19. Chapter 19 : Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs - p. Page 356 20. Chapter 20 : Secure cloud web application in an industrial environment: a study - p. Page 375 21. Chapter 21 : Deep learning applied solid waste recognition system targeting sustainable development goal - p. Page 390 22. Index - p. Page 419
Machine Intelligence in Mechanical Engineering explains the latest applications of machine intelligence and data-driven decision-making in mechanical engineering industries. By providing introductory theory, trouble-shooting case studies, detailed algorithms and implementation instructions, this interdisciplinary book will help readers explore additional applications in their own fields. Those with a mechanical background will learn the important tasks related to preprocessing of datasets, feature extraction, verification and validation of machine learning models which unlock these new methods.
Machine Intelligence is currently a key topic in industrial automation, enabling machines to solve complex engineering tasks and driving efficiencies in the smart production line. Smart preventative maintenance systems can prevent machine downtime, smart monitoring and control can produce more effective workflows with less human intervention.
1. Chapter 1 : Machine intelligence in mechanical engineering: an introduction - p. Page x 2. Chapter 2 : A smart production line management system using face recognition and augmented reality - p. Page 9 3. Chapter 3 : Maintenance planning optimization through equipment performance prediction using machine learning based on inline instrument datasets—a surface condenser case study - p. Page 25 4. Chapter 4 : Minimizing intercellular movement of parts and maximizing the utilization of machines using the correlation index-based clustering algorithm - p. Page 52 5. Chapter 5 : Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment - p. Page 61 6. Chapter 6 : Application of machine vision technology in manufacturing industries—a study - p. Page 86 7. Chapter 7 : Estimation of wing stall delay characteristics with outward dimples using numerical analysis - p. Page 118 8. Chapter 8 : An Internet of Things-based integrative safety framework of autonomous vehicles for special needs society - p. Page 134 9. Chapter 9 : Motion planning and control for autonomous vehicle collision avoidance systems using potential field-based parameter scheduling - p. Page 146 10. Chapter 10 : Long-term predictive maintenance system with application and commercialization to industrial conveyors - p. Page 174 11. Chapter 11 : Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review - p. Page 189 12. Chapter 12 : Application of computationally intelligent modeling to glass fiber reinforced polymer drilling - p. Page 225 13. Chapter 13 : Applied advanced analytics in marketing of mechanical products - p. Page 247 14. Chapter 14 : Information and communication technologies: enablers for the successful implementation of supply chain 4.0 - p. Page 285 15. Chapter 15 : A pilot study and development of prediction model for tire compound quality - p. Page 296 16. Chapter 16 : Machine intelligence based learning for ecological transportation - p. Page 309 17. Chapter 17 : A review on the social impacts of automation on human capital in Malaysia - p. Page 324 18. Chapter 18 : Autonomous systems with intelligent agents - p. Page 339 19. Chapter 19 : Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs - p. Page 356 20. Chapter 20 : Secure cloud web application in an industrial environment: a study - p. Page 375 21. Chapter 21 : Deep learning applied solid waste recognition system targeting sustainable development goal - p. Page 390 22. Index - p. Page 419
