1st Edition
Manufacturing Optimization through Intelligent Techniques (2006)
Effective utilization of equipment is critical to any manufacturing operation, especially with today's sophisticated, high-cost equipment and increased global competition. To meet these challenges in the manufacturing industry, you must understand and implement the myriad conventional and intelligent techniques for different types of manufacturing problems. Manufacturing Optimization Through Intelligent Techniques covers design of machine elements, integrated product development, machining tolerance allocation, selection of operating parameters for CNC machine tools, scheduling, part family formation, selection of robot coordinates, robot trajectory planning and both conventional and intelligent techniques, providing the tools to design and implement a suitable optimization technique. The author explores how to model optimization problems, select suitable techniques, develop the optimization algorithm and software, and implement the program.
The book delineates five new techniques using examples taken from the literature for optimization problems in design, tolerance allocation; selection of machining parameters, integrated product development, scheduling, concurrent formation of machine groups and part families, selection of robot co-ordinates, robot trajectory planning and intelligent machining. All the manufacturing functions described have been successfully solved by Genetic Algorithm. Other intelligent techniques have been implemented only for solving certain types of problems: simulated annealing; design and scheduling, particle swarm optimization and ant colony optimization; tolerance allocation and tabu search; as well as machining parameters optimization.
After reading this book, you will understand the different types of manufacturing optimization problems as well as the conventional and intelligent techniques suitable for solving them. You will also be able to develop and implement effective optimization procedures and algorithms for a wide variety of problems in design manufacturing.
CONVENTIONAL OPTIMIZATION TECHNIQUES FOR MANUFACTURING APPLICATIONS
Brief Overview of Traditional Optimization
Single Variable Techniques Suitable for Solving Various Manufacturing Optimization Problems (Direct Search Method)
Multivariable Techniques Suitable for Solving Various Manufacturing Optimization Problems (Direct Search Methods)
Dynamic Programming Technique
INTELLIGENT OPTIMIZATION TECHNIQUES FOR MANUFACTURING OPTIMIZATION PROBLEMS
Genetic Algorithm (GA)
Simulated Annealing (SA)
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Tabu Search (TS)
OPTIMAL DESIGN OF MECHANICAL ELEMENTS
Introduction
Gear Design Optimization
Design Optimization of Three-Bar Structure
Spring Design Optimization
Design Optimization of Single-Point Cutting Tool
OPTIMIZATION OF MACHINING TOLERANCE ALLOCATION
Dimensions and Tolerances
Tolerance Allocation of Welded Assembly
Tolerance Design of Over Running Clutch Assembly
Tolerance Design Optimization of Stepped Clone Pulley
Tolerance Design Optimization of Stepped-Block Assembly
OPTIMIZATION OF OPERATING PARAMETERS FOR CNC MACHINE TOOLS
Optimization of Turning Process
Optimization of Multi-Pass Turning Process
Optimization of Face Milling Process
Surface Grinding Process Optimization
Optimization of Machining Parameters for Multi-Tool Milling Operations Using Tabu Search
INTEGRATED PRODUCT DEVELOPMENT AND OPTIMIZATION
Introduction
Integrated Product Development
Total Product Optimization - Design for Life Cycle Cost (DLCC)
Case Illustration
Proposed Methodology
GA Illustrated
Conclusion
SCHEDULING OPTIMIZATION
Classification of Scheduling Problems
Scheduling Algorithms
Parallel Machine Scheduling Using Genetic Algorithms
Implementation of Simulated Annealing Algorithm
MODERN MANUFACTURING APPLICATIONS
Implementation of Genetic Algorithm for Grouping of Part Families and Matching Cell
Selection of Robot Coordinate Systems Using Genetic Algorithm
Trajectory Planning for Robot Manipulators Using Genetic Algorithm
Application of Intelligent Techniques for Adaptive Control Optimization
CONCLUSIONS & FUTURE SCOPE
Index
