Optimisation of Robotic Disassembly for Remanufacturing (Springer Series in Advanced Manufacturing)

✍ Scribed by Yuanjun Laili, Yongjing Wang, Yilin Fang, Duc Truong Pham

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 198
• Category: Library

✦ Synopsis

This book illustrates the main characteristics, challenges and optimisation requirements of robotic disassembly. It provides a comprehensive insight on two crucial optimisation problems in the areas of robotic disassembly through a group of unified mathematical models. The online and offline optimisation of the operational sequence to dismantle a product, for example, is represented with a list of conflicting objectives and constraints. It allows the decision maker and the robots to match the situation automatically and efficiently.

To identify a generic solution under different circumstances, classical metaheuristics that can be used for the optimisation of robotic disassembly are introduced in detail. A flexible framework is then presented to implement existing metaheuristics for sequence planning and line balancing in the circumstance of robotic disassembly.

Optimisation of Robotic Disassembly for Remanufacturing provides practical case studies on typicalproduct instances to help practitioners design efficient robotic disassembly with minimal manual operation, and offers comparisons of the state-of-the-art metaheuristics on solving the key optimisation problems. Therefore, it will be of interest to engineers, researchers, and postgraduate students in the area of remanufacturing.

✦ Table of Contents

Preface
Acknowledgements
Contents
Abbreviations
List of Figures
List of Tables
1 Introduction to Remanufacturing
1.1 The Need for Remanufacturing
1.2 Concept and Main Activities of Remanufacturing
1.3 Optimisation Problems in Remanufacturing
1.4 Summary
References
2 Robotic Disassembly for Remanufacturing
2.1 Concept and General Process of Robotic Disassembly
2.1.1 Is Robotic Disassembly New?
2.1.2 How Robotic Disassembly Works
2.1.3 How Are Disassembly Operations Defined?
2.1.4 How Robotic Disassembly Operations are Carried Out
2.1.5 What is Disassembly Planning and Scheduling?
2.2 Characteristics of Robotic Disassembly
2.2.1 How Disassembly is Different to Assembly
2.2.2 How to Adapt Assembly to Disassembly
2.3 Design of a Disassembly Cell Using Techniques For Assembly Cell Design
2.4 Key Enablers of Robotic Disassembly
2.4.1 Phase 1—Enabling Manufacturers to Benefit from Robotic Disassembly: Robust Disassembly
2.4.2 Phase 2—Enabling Third-Party Remanufacturers to Benefit from Robotic Disassembly: Flexible Disassembly
2.4.3 Phase 3—A Possible Goal of Robotic Disassembly: Autonomous Disassembly
2.5 Summary
References
3 Product Representation for Disassembly Sequence Planning
3.1 Classification of Product Representations
3.2 Matrix Representation
3.2.1 Interference Matrix
3.2.2 Disassembly Precedence Matrix
3.2.3 Immediate Preceded Matrix
3.3 Graph Representation
3.3.1 Task Precedence Diagram
3.3.2 Joint Precedence Graph
3.3.3 AND/OR Graph
3.3.4 Transformed AND/OR Graph
3.3.5 Connector-Based Precedence Graph
3.3.6 Disassembly Constraint Graph
3.4 Hybrid Representation
3.4.1 Component-Fastener Graph
3.4.2 Disassembly Graph Model and Disassembly Sequence Structure Graph
3.4.3 Disassembly Hybrid Graph Model
3.4.4 Disassembly Petri Net
3.5 Transformation and Comparison Between Different Representations
3.6 Summary
References
4 Component and Subassembly Detection
4.1 Overview
4.2 Two-Pointer Detection Strategy
4.3 Performance Analysis of the Two-Pointer Strategy
4.4 Summary
References
5 Modelling of Robotic Disassembly Sequence Planning
5.1 Preliminaries
5.2 Mathematical Representation
5.2.1 Decision Variables
5.2.2 Objectives
5.2.3 Constraints
5.3 Uncertainties in Robotic Disassembly
5.4 Summary
References
6 Modelling of Robotic Disassembly Line Balancing
6.1 Problem Statement
6.2 Mathematical Representation
6.2.1 Notations and Decision Variables
6.2.2 Objectives
6.2.3 Constraints
6.3 Representation of Uncertain Task Time in DLBP
6.4 Model Extendibility to Different Environments
6.5 Summary
References
7 Evolutionary Optimisation for Robotic Disassembly Sequence Planning and Line Balancing
7.1 Basic Process of Evolutionary Optimisation
7.2 Typical Evolutionary Algorithms
7.2.1 Typical Single-Objective Evolutionary Algorithm
7.2.2 Typical Multi-objective Evolutionary Algorithm
7.3 Encoding Methods for Two Robotic Disassembly Optimisation Problems
7.4 Algorithm Configuration
7.5 Summary
References
8 Solutions for Robotic Disassembly Sequence Planning with Backup Actions
8.1 Overview
8.2 Problem Formulation
8.2.1 Backup Actions
8.2.2 Expected Disassembly Time and Completion Rate
8.2.3 Mathematical Model
8.3 The Dual-Selection Multi-objective Evolutionary Algorithm
8.3.1 The Dual-Selection Strategy for Solution Exploration
8.3.2 The Variation Operators for Solution Exploitation
8.3.3 Time Complexity of DS-MOEA
8.4 Experiments and Discussions
8.4.1 Product Example and Experimental Configurations
8.4.2 How Does the New Model Change the Calculation of Disassembly Time?
8.4.3 The Effectiveness of Backup Actions
8.4.4 The Efficiency of the DS-MOEA on Robotic Disassembly Sequence Planning
8.5 Summary
References
9 Robotic Disassembly Sequence Re-planning
9.1 Preliminaries
9.2 Ternary Bees Algorithm for Disassembly Re-planning
9.3 Experiments and Discussions
9.4 Summary
References
10 Solutions for Robotic Disassembly Line Balancing
10.1 Preliminaries
10.2 Encoding Method for Solving the Generic DLBP Model
10.3 Benchmarking Instances and Experimental Settings
10.4 Experimental Comparison and Discussion
10.5 Summary
References
11 Solutions for Mixed-Model Disassembly Line Balancing with Multi-robot Workstations
11.1 Preliminaries
11.1.1 Problem Definition and Assumptions
11.1.2 Disassembly Precedence Relations
11.1.3 Illustrative Example
11.2 Mathematical Formulation
11.2.1 Notations
11.2.2 Mathematical Model for MDLB-MR
11.3 Problem-Specific Bi-Criterion Evolutionary Algorithm for MDLB-MR
11.3.1 Procedure of PBEA
11.3.2 Solution Encoding and Decoding
11.3.3 Solution Initialisation
11.3.4 Problem-Specific Variation Operators
11.4 Computational Experiments
11.4.1 Experimental Settings
11.4.2 Experimental Results and Discussions
11.5 Summary
References
Index