Machine Intelligence in Mechanical Engineering

K. Palanikumar, Elango Natarajan, S. Ramesh, J. Paulo Davim, K. Palanikumar, Elango Natarajan, S. Ramesh, J. Paulo Davim

• Publisher: Woodhead Publishing
• Year: 2024
• Print ISBN:
  9780443186448
• eBook ISBN:
  9780443186455
• Topic: Technology & Engineering
• Subtopic: Thermodynamics
• Index: Technology & Engineering

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. - Provides detailed case studies of how machine intelligence has been used in mechanical engineering applications - Includes a basic introduction to machine learning algorithms and their implementation - Addresses innovative applications of AR/VR technology in mechanical engineering

Table of contents

• Cover
• Title page
• Table of Contents
• Copyright
• Contents
• List of contributors
• Preface
• List of Illustrations
• List of Tables
• Chapter 1 : Machine intelligence in mechanical engineering: an introduction
• Chapter 2 : A smart production line management system using face recognition and augmented reality
• Chapter 3 : Maintenance planning optimization through equipment performance prediction using machine learning based on inline instrument datasets—a surface condenser case study
• Chapter 4 : Minimizing intercellular movement of parts and maximizing the utilization of machines using the correlation index-based clustering algorithm
• Chapter 5 : Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment
• Chapter 6 : Application of machine vision technology in manufacturing industries—a study
• Chapter 7 : Estimation of wing stall delay characteristics with outward dimples using numerical analysis
• Chapter 8 : An Internet of Things-based integrative safety framework of autonomous vehicles for special needs society
• Chapter 9 : Motion planning and control for autonomous vehicle collision avoidance systems using potential field-based parameter scheduling
• Chapter 10 : Long-term predictive maintenance system with application and commercialization to industrial conveyors
• Chapter 11 : Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review
• Chapter 12 : Application of computationally intelligent modeling to glass fiber reinforced polymer drilling
• Chapter 13 : Applied advanced analytics in marketing of mechanical products
• Chapter 14 : Information and communication technologies: enablers for the successful implementation of supply chain 4.0
• Chapter 15 : A pilot study and development of prediction model for tire compound quality
• Chapter 16 : Machine intelligence based learning for ecological transportation
• Chapter 17 : A review on the social impacts of automation on human capital in Malaysia
• Chapter 18 : Autonomous systems with intelligent agents
• Chapter 19 : Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
• Chapter 20 : Secure cloud web application in an industrial environment: a study
• Chapter 21 : Deep learning applied solid waste recognition system targeting sustainable development goal
• Index
• A