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Behnam Malakooti, Ph.D., P.E.
 

Machinability and Machine Setup: Multiple Objective Optimization Approach

Behnam Malakooti

Brief Table Of Contents Detailed Table Of Contents

  • Part I. Machinability, Machine Set-up, and Tool-Life Supervision

    • Chapter 1 Machinability, Set-up, and Tool-Life: Accelerated Testing Approach
      • A Sensor-Based Accelerated Approach for Multiple-Attribute Machinability and Tool Life Evaluation (30)
      • 1. A Formal Theory of Training
      • 2. References
      • 3. The Authors Respond
      • 4. References
    • Chapter 2 Machinability, Set-up, and Tool-Life: Multiple Objective Interactive Optimization
      • An Interactive Multiple Criteria Approach for Parameter Selection in Metal Cutting (36)
      • 1. Introduction
      • 2. Mathematical Formulation of the Machining Operation
        • 2.1. Nomenclature
        • 2.2. Decision Variables (Parameters to be Assessed)
        • 2.3. Objective Function
        • 2.4. Problem Constraints
      • 3. An Interactive Heuristic Gradient-Based Multicriteria Approach
        • 3.1. Discrete Multiple Criteria Problem
        • 3.2. Assessment of the Gradient
        • 3.3. A Heuristic Gradient Cut
        • 3.4. One-Dimensional Search
        • 3.5. An Interactive Method
      • 4. Multiple Criteria Decision Making Method for Machining Operation
        • 4.1. Discrete Variable Approach for Generating Efficient Alternatives
        • 4.2. Interactive Discrete MCDM Approach for Machining Operations
      • 5. Example
      • 6. Computational Experiments and Comparison to Commercial Packages
        • 6.1. Experiments with the Example Problem for Interval m and Objective Bounds
        • 6.2. Experiments of Five Problems
        • 6.3. Comparison to Commercial Packages
      • 7. Decision Support Systems for Machining Operations
      • 8. Conclusions
      • 9. References
    • Chapter 3 Machinability, Set-up, and Tool-Life: Multiple Objective Artificial Neural Network Optimization
      • An Interactive Artificial Neural Network approach for machine set-up optimization (9)
      • 1. Introduction
      • 2. Basic Notations, and Review of Methods for Machine Setup
      • 3. Concepts of Artificial Neural Networks
        • 3.1. Feedforward Artificial Neutral Network
      • 4. Artificial Neural Network Method for Machine Setup
        • 4.1. Problem Formulation
        • 4.2. Summary of Developed Algorithm
      • 5. Example
      • 6. Conclusions
      • 7. References
    • Chapter 4 Machinability, Set-up, and Tool-Life: Multiple Objective Polynomial Optimization
      • Multiple Criteria Approach for Integrated Machining Supervision, Machinability, and Tool Performance with Polynomial Utility Functions (8)
      • 1. Introduction
      • 2. Mathematical Formulation of the Machining Operation
        • 2.1. Notations
        • 2.2. Machine Variables
        • 2.3. Objectives for Maintaining Operations
        • 2.4. Process Outputs for Matching Operations
        • 2.5. Constraints for the Integrated Problem
      • 3. General Relation of Machine Variables, Process Outputs, Machinability and Tool Performance
      • 4. An Interactive Holistic Method for the Complete Assessment of the Generalized Decomposable Multi-Attribute Utility Function
        • 4.1. A Generalized Decomposable Multi-attributed Utility Function
        • 4.2. A Holistic Method to Assess the Gradient of a Utility Function
        • 4.3. An Interactive Procedure to Assess GDMAUF and Rank the Discrete Alternatives
      • 5. An Interactive Discrete Multiple Criteria Decision Making Approach for Machining Operation
        • 5.1. GDMAUF for Supervising Machining Operations, Machinability Evaluation. and Tool Performance Evaluation
        • 5.2. Simulated Examples for Assessing GDMAUF, Supervising Machining Operations, Machinability Evaluation, and Tool Performance Evaluation Problems
      • 6. Conclusions
      • 7. Appendix: Simulated Examples for Supervising Machining Operations, Machinability Evaluation, and Tool Performance Evaluation Problems
      • 8. References

  • Part II. Hierarchical Multiple Objective Manufacturing Planning

    • Chapter 5 Hierarchical Multiple Objective for Computer Integrated Manufacturing
      • An Interactive Hierarchical Multi-Objective Approach for Computer Integrated Manufacturing (35)
      • 1. Introduction
      • 2. An Overview of Decision Making and Components of Computer-Integrated Manufacturing
        • 2.1. Classification of Decision Levels
        • 2.2. CIM at the Plant Level
      • 3. A Multi-Objective Decision Support System for Computer-Integrated Manufacturing
        • 3.1. Computer-Integrated Manufacturing Database (CIB DB)
        • 3.2. Structure of Multi-Objective Decision Support System
      • 4. A Hierarchical, Multi-Objective Approach for Analysis and Design of Computer-Integrated Manufacturing
        • 4.1. The Framework of Multi-Objective Structured Analysis and Design Technique (SADT)
        • 4.2. Mathematical Notation and Definitions for Multi-Objectives
        • 4.3. An Interactive, Hierarchical, Multi-Objective Approach
        • 4.4. A Notational Example
        • 4.5. A Numerical Example
      • 5. A Multi-Objective Approach for Selection of Software for Computer-Integrated Manufacturing
        • 5.1. Criteria for Evaluation
        • 5.2. Assessment of Criteria
        • 5.3. Selection of the Software
        • 5.4. An Example
      • 6. Conclusions
      • 7. References
    • Chapter 6 Hierarchical Multiple Objective Optimization for Production Planning
      • A Gradient-Based Approach for Solving Hierarchical Multiple Criteria Production Planning Problems (38)
      • 1. Introduction
      • 2. A Hierarchical Multi-Criteria Production Planning Framework
      • 3. Interactive Gradient-Based Method
      • 4. An Application to Facility Layout
      • 5. Conclusions
      • 6. References
      • 7. Appendix
    • Chapter 7 Multiple Objective Sampling in Statistical Quality Control
      • Selection of Acceptance Sampling Plans with Multi-Attribute Defects in Computer-Aided Quality Control (41)
      • 1. Introduction
      • 2. A Multi-Criteria Model for Acceptance Sampling
      • 3. An Interactive Paired Comparison Method for Planning Quality Control Systems
      • 4. A Computer Package with Experiments
      • 5. Lamp-making: a Case Study
      • 6. Conclusions
      • 7. Appendix
      • 8. References