Soft Computing in Smart Manufacturing: Solutions toward Industry 5.0

https://audiopronews.com/headlines/s8bn6540u Title Page Copyright Contents Preface About the editors List of contributing authors 1 Control, monitoring, modeling, and optimization of manufacturing and machining technologies 1.1 Introduction to manufacturing and machining technology 1.1.1 The machining technology 1.1.2 Automation for machining technologies 1.1.3 Robotics in machining 1.1.4 Integrated manufacturing systems and machining 1.1.5 Computer integrated manufacturing 1.1.6 Precision in machining operations 1.2 Soft computing in machining technology 1.2.1 Introduction to soft computing 1.2.2 Soft computing and systems engineering 1.2.3 Soft computing and finite element modeling 1.2.4 Computational optimization of machining processes 1.3 Concluding synopsis and future prognosis for soft computing in Industry 5.0 References 2 Trustworthy human–machine assistance by dynamic process security monitoring in industrial environments 2.1 Introduction 2.1.1 Bridge to Industry 5.0 – “humans are already part of Industry 4.0 – that’s inherently upward compatible” 2.2 Security in Industry 4.0 – the approach of SecureIoT 2.2.1 High-level outline of SecureIoT 2.2.2 Architecture and infrastructure 2.2.3 Predictive security approach 2.3 The multivendor Industry 4.0 use case 2.3.1 Objectives and overall scenario 2.3.2 Architecture 2.3.3 Use case scenarios 2.4 Future aspects – toward Industry 5.0 References 3 Application of convolutional neural networks for visual control of intelligent robotic systems 3.1 Introduction 3.2 Convolutional neural networks 3.2.1 Convolutional neural networks architecture 3.2.2 Region-based convolutional neural networks 3.2.3 Transfer learning 3.3 Image-based visual servoing 3.3.1 Image features detection 3.3.2 Stereo image-based visual servoing 3.4 Experimental results 3.4.1 Simulation results 3.4.2 Real world experimental results 3.5 Conclusion References Notes 4 Digital twins in smart manufacturing 4.1 Introduction 4.2 Digital twins in industry 4.3 Development of static and dynamic digital twins 4.3.1 Digital twin concepts 4.3.2 Example of static digital twin development 4.3.3 Development of a dynamic digital twin 4.4 Conclusion References 5 A novel implementation of an open CNC PC-based and service-oriented architecture-based monitoring system for STEP-NC – a case study 5.1 Introduction 5.2 Methodology 5.2.1 Monitoring system 5.2.2 Monitoring module 5.2.3 Temperature monitoring module 5.2.4 Monitoring hardware deployment 5.2.5 Case study 5.3 Result and discussion of monitoring information between a new and worn tool for case studies 1 and 2 5.4 Conclusion References 6 Smart system based on STEP-NC for machine vision inspection (3SMVI) 6.1 Introduction 6.2 The state-of-the-art 6.2.1 Auto vision inspection system 6.2.2 Inspection expectations and extreme conditions 6.3 Camera options 6.3.1 The selection of the camera 6.3.2 Scanning of area or line 6.3.3 Color camera 6.4 Selection of the lens 6.4.1 Mounting of the lens 6.4.2 Sensor size and image circle 6.4.3 Pixel size and resolution 6.4.4 The focal length 6.4.5 The depth of field and aperture 6.5 The lighting system 6.5.1 The lighting mechanism 6.5.2 Source of lighting 6.5.3 Design and installation of systems 6.6 Conclusion References 7 Soft computing in advanced cutting processes 7.1 Smart machining in advanced cutting processes 7.2 Soft computing in machinability evaluation 7.2.1 Process parameterization in machinability evaluation 7.2.2 Soft computing based modeling in advanced cutting processes 7.3 Experiments and data acquisition in special cooling and lubrication 7.3.1 Analysis of cooling and lubrication in advanced machining 7.3.2 Experimental procedure and data measuring 7.4 Soft computing-based modeling of cooling and lubrication in machining 7.4.1 Standard curve fitting methodology in cutting force and energy consumption modeling 7.4.2 Bio- and natural-inspired methods in cutting force and energy consumption modeling 7.4.3 Bio- and nature-inspired methods in surface quality and tool-wear modeling 7.5 Summary Nomenclature References 8 Modeling and optimization of AWJM process on the processing of Banana fiber-reinforced polymer composites using Taguchi-JAYA method 8.1 Introduction 8.2 Materials development and machining details 8.2.1 Development of BFRP composites 8.2.2 Machining details of BFRP composites 8.3 Results and discussion 8.3.1 Modeling and statistical analysis of AWJM process 8.3.2 Main effect plot of MRR 8.3.3 Main effect plot on SR 8.3.4 Desirability function approach 8.3.5 JAYA algorithm 8.3.6 Multi-objective optimization 8.4 Confirmatory experiments 8.5 Conclusions References Index