Atjaunināt sīkdatņu piekrišanu

Manufacturing Optimization through Intelligent Techniques [Hardback]

4.00/5 (2 ratings by Goodreads)
(Kumaraguru College of Technology, Tamilnadu, India)
  • Formāts: Hardback, 240 pages, height x width: 229x152 mm, weight: 431 g, 50 Tables, black and white; 67 Illustrations, black and white, Contains 70 hardbacks
  • Sērija : Manufacturing Engineering and Materials Processing
  • Izdošanas datums: 27-Feb-2006
  • Izdevniecība: Marcel Dekker Inc
  • ISBN-10: 0824726790
  • ISBN-13: 9780824726799
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 95,02 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Manufacturing Optimization through Intelligent TechniquesPalielināt
  • Formāts: Hardback, 240 pages, height x width: 229x152 mm, weight: 431 g, 50 Tables, black and white; 67 Illustrations, black and white, Contains 70 hardbacks
  • Sērija : Manufacturing Engineering and Materials Processing
  • Izdošanas datums: 27-Feb-2006
  • Izdevniecība: Marcel Dekker Inc
  • ISBN-10: 0824726790
  • ISBN-13: 9780824726799
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780824726799.html
Keywords:
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.
Manufacturing Optimization through Intelligent Techniques
1(2)
Conventional Optimization Techniques for Manufacturing Applications
3(14)
Brief Overview of Traditional Optimization Techniques
3(1)
Single Variable Techniques Suitable for Solving Various Manufacturing Optimization Problems (Direct Search Methods)
4(3)
Unrestricted Search
5(1)
Search with Fixed Step Size
5(1)
Steps
5(1)
Search with Accelerated Step Size
5(1)
Exhaustive Search Method
5(1)
Dichotomous Search
6(1)
Fibonacci Search
6(1)
Disadvantages
7(1)
Golden Search Method
7(1)
Multivariable Techniques Suitable for Solving Various Manufacturing Optimization Problems (Direct Search Methods)
7(5)
Evolutionary Optimization Method
7(1)
Algorithm
8(1)
Nelder--Mead Simplex Method
8(1)
Algorithm
9(1)
Complex Method
10(1)
Hooke--Jeeves Pattern Search Method
10(1)
Exploratory Move
11(1)
Pattern Move
11(1)
Algorithm
11(1)
Dynamic Programming Technique
12(5)
Representation of Multistage Decision Process
13(2)
References
15(2)
Intelligent Optimization Techniques for Manufacturing Optimization Problems
17(28)
Genetic Algorithms (GA)
17(15)
Working Principle of GA
17(3)
Two-Point Crossover
20(1)
Multipoint Crossover
20(1)
Fundamental Difference
21(2)
GA Parameters
23(1)
Selection Methods
24(1)
Fitness-Proportionate Selection with ``Roulette Wheel'' and ``Stochastic Universal'' Sampling
24(1)
Sigma Scaling
25(1)
Elitism
26(1)
Boltzmann Selection
26(1)
Rank Selection
27(1)
Tournament Selection
28(1)
Steady-State Selection
28(1)
Inheritance Operators
29(1)
Matrix Crossover (Two-Dimensional Crossover)
29(1)
Inversion and Deletion
30(1)
Inversion
30(1)
Linear + End-Inversion
31(1)
Continuous Inversion
31(1)
Mass Inversion
31(1)
Deletion and Duplication
31(1)
Crossover and Inversion
31(1)
Simulated Annealing (SA)
32(2)
Optimization Procedure Using SA
33(1)
Ant Colony Optimization (ACO)
34(4)
State Transition Rule
35(1)
Pheromone Updating Rule
36(2)
Steps in Ant Colony Algorithm
38(1)
Particle Swarm Optimization (PSO)
38(3)
Background of Artificial Life
39(1)
Particle Swarm Optimization Technique
39(1)
Algorithm of Particle Swarm Optimization
40(1)
PSO Parameters Control
40(1)
Comparisons between Genetic Algorithm and PSO
41(1)
Tabu Search (TS)
41(4)
Tabu Search Algorithm
42(1)
General Structure of Tabu Search
42(1)
Efficient Use of Memory
42(1)
Variable Tabu List Size
43(1)
Intensification of Search
43(1)
Diversification
43(1)
Stopping Criterion
43(1)
References
44(1)
Optimal Design of Mechanical Elements
45(36)
Introduction
45(3)
Adequate Design
46(1)
Optimal Design
46(1)
Primary Design Equation
46(1)
Subsidiary Design Equations
46(1)
Limit Equations
47(1)
Optimal Design Procedure
47(1)
Gear Design Optimization
48(11)
Mathematical Model of Gear Design
48(1)
Preliminary Gear Considerations
48(1)
Decision Variables
48(1)
Constraints
48(1)
Determination of Range of Pitch Circle Diameter for Pinion
49(1)
Determination of Range of Teeth for Pinion
49(1)
Stress Constraints
50(1)
Efficiency of Coplanar Gears
50(1)
Calculation of Efficiency and Weight
50(1)
Error
51(1)
Individual Errors
51(1)
Profile Error
51(1)
Pitch Error
51(1)
Tooth Alignment Error
51(1)
Radial Run-Out Error
52(1)
Axial Run-Out Error
52(1)
Tooth Thickness Error
52(1)
Base Circle Error
52(1)
Composite Error
52(1)
Applying Genetic Algorithm
52(1)
Coding
52(1)
Gene
52(1)
Chromosome Length
53(1)
Crossover
53(1)
Mutation
54(1)
Numerical Illustration
54(1)
Initialization
54(1)
Evaluation
54(2)
Applying Simulated Annealing Algorithm
56(2)
Gear Details (without Optimization)
58(1)
Details of the Optimized Gear
59(1)
Design Optimization of Three-Bar Truss
59(7)
Problem Description
59(1)
Design Variables
59(4)
Objective Function
63(1)
Design Constraints
63(1)
Stress Constraints
64(1)
Deflection Constraints
64(1)
Frequency Constraints
65(1)
Buckling Constraints
65(1)
Area Constraint
66(1)
Spring Design Optimization
66(6)
Problem Formulation
67(1)
Design Variables
67(1)
Objective Function
67(1)
Design Constraints
67(1)
Deflection Constraint
68(1)
Shear Stress Constraint
68(1)
Constraint on Frequency of Surge Waves
68(1)
Diameter Constraint
68(1)
Limits on Design Variables
68(1)
Implementation of Genetic Algorithm
69(1)
Three-Bar Truss
69(1)
Spring
69(1)
GA Parameters
69(3)
Design Optimization of Single-Point Cutting Tools
72(9)
Single-Point Cutting Tools
72(1)
Development of Model
72(1)
Overview of Tool Geometry
72(2)
Design Model
74(4)
Design Optimization Problem of Single-Point Cutting Tool
78(1)
Implementation of GA
78(1)
Constant Values
78(1)
Genetic Operators
78(1)
Comparison of Results with Solution Obtained by Game Theory
79(1)
References
79(2)
Optimization of Machining Tolerance Allocation
81(40)
Dimensions and Tolerances
81(10)
Classification of Tolerance
81(1)
Tolerance Schemes
81(1)
Tolerance Modeling and Representation
81(1)
Tolerance Specification
81(1)
Tolerance Analysis
82(1)
Tolerance Synthesis
82(1)
Tolerance Transfer
82(1)
Tolerance Evaluation
82(1)
Tolerance and Cost Relationship
82(1)
Quality Loss Function
83(1)
Tolerance Allocation Methods
84(1)
Proportional Scaling Method
85(1)
Worst Case Limit
86(1)
Statistical Limit
86(1)
Allocation by Weight Factors
86(1)
Worst Case Limit
87(1)
Statistical Limit
87(1)
Constant Precision Factor Method
87(1)
Taguchi Method
87(1)
Tolerance Allocation Using Least Cost Optimization
88(1)
Tolerance Analysis versus Tolerance Allocation
89(1)
Tolerance Design Optimization
90(1)
Need for Optimization
91(1)
Tolerance Allocation of Welded Assembly
91(3)
Problem Statement
91(2)
Implementation of GA
93(1)
Coding Scheme
93(1)
Objective Function
93(1)
Evaluation
93(1)
GA Parameters
93(1)
Rank Selection Method
94(1)
Optimization Results
94(1)
Tolerance Design Optimization of Overrunning Clutch Assembly
94(5)
Problem Definition
94(1)
Optimum Tolerances for Overrunning Clutch
94(2)
Objective Function
96(2)
Implementation of Particle Swarm Optimization (PSO)
98(1)
Coding System
98(1)
Parameters Used
98(1)
Results and Discussion
98(1)
Tolerance Design Optimization of Stepped Cone Pulley
99(5)
Objective Function
99(1)
Decision Variables
99(3)
Constraints
102(1)
Finish Turning Datum Surface
102(1)
Process Requirements
102(1)
Evaluate Population
102(2)
Proposed GA Parameters
104(1)
Machining Datum
104(1)
Initialize Population
104(1)
Coding Scheme
104(1)
Tolerance Design Optimization of Stepped Block Assembly
104(17)
Proposed Approach
105(1)
Optimization of Nominal Values of Noncritical Dimensions
105(1)
Problem Formulation
106(1)
Numerical Illustration
106(2)
Implementation of Continuous Ant Colony Optimization (CACO)
108(1)
Randomly Generated Solutions in Ascending Order
108(1)
Global Search for Inferior Solutions
108(2)
Crossover or Random Walk
110(1)
Mutation
111(1)
Trail Diffusion
112(1)
Local Search
113(1)
After Applying CACO Algorithm
114(1)
Allocation of Tolerances for Optimal Nominal Values Using CACO
114(1)
Problem Formulation
115(1)
Multiple-Criterion Objective Function
116(1)
Objective Function
117(1)
Results and Discussions
118(1)
References
118(3)
Optimization of Operating Parameters for CNC Machine Tools
121(34)
Optimization of Turning Process
121(10)
Objective Function
121(1)
Nomenclature
122(1)
Operating Parameters
122(1)
Feed Rate
122(1)
Cutting Speed
123(1)
Constraints
123(1)
Implementation of Nelder-Mead Simplex Method
124(1)
Data of Problem
124(1)
Solution by Nelder-Mead Simplex Method
124(3)
Implementation of GA
127(1)
Binary Coding (V)
127(1)
Binary Coding (f)
127(1)
Fitness Function
128(1)
Reproduction
128(1)
Crossover
128(1)
Mutation
129(1)
GA Parameters and Result
129(2)
Optimization of Multi-Pass Turning Process
131(3)
Implementation of Dynamic Programming Technique
131(3)
Optimization of Face Milling Process
134(4)
Objective Function
134(1)
Machining Variables
135(1)
Machining Constraints
135(1)
Data of the Problem
136(1)
Implementation of GA for Face Milling Process Optimization
137(1)
Binary Coding
137(1)
Fitness Function
137(1)
Genetic Operations
138(1)
Optimization Results
138(1)
Surface Grinding Process Optimization
138(9)
Nomenclature
138(1)
Determination of Subobjectives and Variables for Optimization
139(1)
Relationships between Two Subobjectives and Four Optimization Variables
139(1)
Production Cost
139(1)
Production Rate
140(1)
Constraints
140(1)
Thermal Damage Constraints
140(1)
Wheel Wear Parameter Constraint
141(1)
Machine Tool Stiffness Constraint
141(1)
Surface Finish Constraint
142(1)
Resultant Objective Function Model
142(1)
Data of Problem
143(1)
Implementation of GA for Four Variable Problems
143(1)
Binary Coding
143(1)
Fitness Function
144(1)
Reproduction
144(1)
Crossover
144(1)
Mutation
144(1)
Implementation for Ten-Variable Surface Grinding Optimization
145(1)
Optimization Variables
145(1)
Special Coding
146(1)
Optimization of Machining Parameters for Multi-Tool Milling Operations Using Tabu Search
147(8)
Nomenclature
147(1)
Unit Cost
147(1)
Unit Time
148(1)
Constraints
148(1)
Power
148(1)
Surface Finish
148(4)
References
152(3)
Integrated Product Development and Optimization
155(20)
Introduction
155(1)
Integrated Product Development
155(4)
Design for Manufacturability (DFM)
156(1)
Design for Assembly (DFA)
157(1)
Design for Reliability (DFR)
158(1)
Design for Serviceability (DFS)
159(1)
Total Product Optimization --- Design for Life Cycle Cost (DLCC)
159(7)
Modeling for LCC Analysis
160(2)
Service Cost (SC)
162(1)
Replacement Cost (RC)
163(1)
Downtime Cost (DC)
164(2)
Case Illustration
166(3)
Proposed Methodology
169(2)
GA Illustrated
171(1)
Conclusion
172(3)
References
173(2)
Scheduling Optimization
175(10)
Classification of Scheduling Problems
175(3)
Single Machine Scheduling
176(1)
Flow Shop Scheduling
176(1)
Scheduling of Job Shops
176(1)
Parallel Machine Scheduling
176(1)
FMS Scheduling
177(1)
Scheduling Algorithms
178(1)
Parallel Machine Scheduling Optimization Using Genetic Algorithm
178(2)
Data of Problem
178(1)
Genetic Algorithm Parameters
179(1)
Fitness Parameter
179(1)
Representation
179(1)
Initialization
179(1)
Crossover
179(1)
Mutation
180(1)
Implementation of Simulated Annealing Algorithm
180(5)
Notations and Terminology
180(1)
SA Algorithm with RIPS: Step-by-Step Procedure
181(1)
SA Algorithm with CRIPS
182(1)
Numerical Illustration
182(1)
Obtaining Seed Sequence
182(1)
Improvement by SA Algorithm with RIPS
182(1)
References
183(2)
Modern Manufacturing Applications
185(20)
Implementation of Genetic Algorithm for Grouping of Part Families and Machining Cell
185(1)
Data of Problem
185(1)
Coding Scheme
185(1)
Crossover Operation
186(1)
Mutation
186(1)
Selection of Robot Coordinates Systems Using Genetic Algorithm
186(7)
Three-Degrees-of-Freedom Arm in Two Dimensions
188(1)
Three-Degree-of-Freedom Arm in Three Dimensions
188(1)
Objective Function
189(1)
Input Data for Two-Dimension Problem
190(1)
Input Data for Three-Dimension Problem
190(1)
Implementation of GA
190(1)
Reproduction
190(1)
Tournament Selection
190(1)
Genetic Operators
191(2)
Trajectory Planning for Robot Manipulators Using Genetic Algorithm
193(8)
Problem Description
194(1)
Robot Configuration
194(1)
Estimation of Time
195(1)
Input Data
195(1)
Procedure
195(2)
Assumptions
197(1)
Optimization Model
197(1)
Genetic Operators
198(1)
Simulation Results
198(3)
Application of Intelligent Techniques for Adaptive Control Optimization
201(4)
Adaptive Control System (ACS)
201(1)
Adaptive Control Optimization System (ACOS)
201(1)
Application of Intelligent Techniques for ACOS
202(1)
References
203(2)
Conclusions and Future Scope
205(2)
Index 207