Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024, 22-23 August, Suzhou, China

Advances in Intelligent Manufacturing and Robotics
Erscheinungsjahr 2025
ISBN: 978-981-963948-9
Verlag: Springer Nature Singapore

Buch, Englisch, 849 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1447 g

Reihe: Lecture Notes in Networks and Systems

This book presents proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 Held on 22 and 23 August in Suzhou, China. This proceedings deliberates on the key challenges, engineering and scientific discoveries, innovations, and advances on intelligent manufacturing and robotics that are non-trivial through the lens of Industry 4.0. In this book, traditional and modern solutions that are employed across the spectrum of various intelligent manufacturing and robotics contexts are presents. The readers are expected to gain an insightful view on the current trends, issues, mitigating factors as well as proposed solutions from this book.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Chen, Wei

PP Abdul Majeed, Anwar

Ping Tan, Andrew Huey

Zhang, Fan

Zhu, Yuyi

Yan, Yuyao

Luo, Yang

Huang, Long

Liu, Chenguang

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde
• Technische Wissenschaften Technik Allgemein Nanotechnologie
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik

Inhaltsverzeichnis

Experimental Investigation on Rheological Characteristics of Aerated Fine Powders.- Reinforcement Learning Algorithm for two-leg robot with DDPG and TD3.- Research on abrasive-workpiece interaction mechanism in 2.5D needle-punched-Cf/C-SiC composites scratching tests.- A bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN).- Advancing In-Situ Bioprinting through Ant Colony Optimisation: Resolving Dead Zone Challenges in Path Planning.- Autonomous Real-Time Human-Robot Emotional Interaction through Facial Recognition.- Machine Learning for Predictive Risk Analytics in Industry 4.0: A Comprehensive Random Forest Evaluation.- Enhancing Deepfake Detection: A Weighted Summation Model of CNN Approach with Local and Global Analysis.- Virtual Streamer Features: Unveiling the Influence on Online Purchase Intentions in the TikTok Era.- Green Threads and Digital Dreams: Exploring the Influence of AI-Mediated Experiences on Purchase Intentions in Sustainable Apparel Markets.- The Implications of Platform Algorithms on Platform Worker’s Job Security.

Über Autor(innen)

Dr Chen obtained his PhD in Mechanical Engineering from The University of Newcastle, Australia. He has broad research and industrial consulting experience relating to manufacturing and digital technologies in interdisciplinary fields. Dr Chen’s original research interest covered multi-phase flow with primary applications in pneumatic conveying industry. During his further employment with BHP Billiton and Bradken Resources, he developed research interests in mineral processing. His current research areas include advanced sensing and IIoT and their coupling to numerical modelling and machine learning.

Dr Anwar P.P. Abdul Majeed obtained his PhD in Rehabilitation Robotics from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA). Prior to joining Sunway University, he served as an Associate Professor at the School of Robotics, Xi'an Jiaotong Liverpool University (XJTLU) and acted as the Programme Director of the newly developed MSc in Advanced Robotics Systems. He served as a Senior Lecturer (Assistant Professor) and the Head of Programme (Bachelor of Manufacturing Engineering Technology (Industrial Automation)) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMPSA before joining XJTLU. He is also currently serving as an adjunct Associate Professor at UCSI University, Malaysia. Dr Anwar is also a Visiting Research Fellow at EUREKA Robotics Centre, Cardiff Metropolitan University, UK.

Dr Andrew Tan obtained his PhD from The University of Nottingham. He was a manufacturing engineer in Agilent Technologies and Motorola Solutions, where he ensured the smooth development and sustenance of product manufacturing lines. In these positions, he has led extensive manufacturing projects across different teams, time zones, and varying concurrent engineering phases.

Dr Fan Zhang obtained his PhD in Material Perception from TU Delft, The Netherlands, and is currently an Assistant Professor in the School of Robotics at Xi'an Jiaotong Liverpool University (XJTLU). Prior to joining XJTLU, he worked as a Postdoctoral Researcher for Procter Gamble (DE) in Multisensory Perception and Consumer Knowledge research and later as Research Fellow at the University of Birmingham (UK) in Computational Modelling and Cognitive Robotics.

Dr Yuyao Yan is currently an Assistant Professor at Xi’an Jiaotong-Liverpool University (XJTLU) Entrepreneur College (Taicang). He received his Ph.D. from the University of Liverpool, U.K. His research interests lie in the field of 3D computer vision and pattern recognition.

Dr Yang Luo is an Assistant Professor at the School of Intelligent Manufacturing Ecosystem in Xi'an Jiaotong Liverpool University (XJTLU). His primary research interest centres on industrial sustainability, with a focus on reducing dependence on fossil-based energy sources through the implementation of intelligent operations and process planning. Dr Luo’s research activities encompass several areas within this field, including real-time energy management for flexible and reactive factories, modeling energy consumption across various levels (plant, process, and product), and energy capture and reuse in manufacturing enterprises. Prior to joining his current position, Dr Luo worked as a postdoctoral research associate (PDRA) at the Research Centre for Carbon Solutions (RCCS) in Heriot-Watt University, UK and Energy Sustainability Consultant at State Grid, China.

Dr Long Huang is an Associate Professor in the School of Intelligent Manufacturing Ecosystems. He has more than ten years of experiences in the development of thermo-fluid system modeling and simulation algorithms. His current research looks at pushing the boundary of performance enhancement of heat exchangers, through in-depth investigations of the heat and mass transfer phenomena. He obtained his Ph.D. in Mechanical Engineering from The University of Maryland, College Park. Prior to joining Xi’an Jiaotong-Liverpool University, his industrial experience includes consultancy work at Navigant Consulting Inc. (Washington D.C., USA) and Daikin Industries Ltd. (Osaka, Japan).

Dr Chenguang Liu is an Assistant Professor in the school of robotics at Xi’an Jiaotong-Liverpool University Entrepreneur College (Taicang). He obtained his PhD from the University of Liverpool, UK. After graduation, he was a postdoctoral research associate at Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO) and XJTLU. His research interests are the application and characterization of advanced materials in lithium-ion batteries and molecular electronic devices.

Dr Yuyi Zhu obtained his PhD in Engineering from the University of Warwick, UK. Prior to joining XJTLU, he worked as a research fellow in Warwick Manufacturing Group, leading projects on alloy design, process simulation, and validation with industry partners. Currently, He serves as the Director of the Education, Research Development Institute in the School of Intelligent Manufacturing Ecosystem at XJTLU. He consistently prioritises the comprehensive development of the school, focusing on exceptional teaching quality and fostering diverse student growth.