Intelligent Machining of Complex Aviation Components (Research on Intelligent Manufacturing)

✍ Scribed by Dinghua Zhang, Ming Luo, Baohai Wu, Ying Zhang

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 209
• Category: Library

✦ Synopsis

This book discusses the basic theoretical model and implementation method of intelligent machining technology and promotes the application of intelligent machining technology in the manufacturing of complex aviation components, such as aero-engine blisk, casing parts and blades. It not only presents the fundamental theory of intelligent machining, but also provides detailed examples of applications in the aviation industry.The topics covered include intelligent programming, intelligent processing models, process monitoring, machining process control, intelligent fixtures and applications in aviation components machining.This book is intended for researchers, engineers and postgraduate students in fields of manufacturing, mechatronics, mechanical engineering and related areas.

✦ Table of Contents

Preface
Contents
1 Introduction
1.1 Numerical Control Machining Technology
1.1.1 Development of Numerical Control Technology
1.1.2 Development Stage of CNC Machining Model
1.2 Intelligent Processing Technology
1.2.1 Intelligent Processing Technology
1.2.2 Ways to Realize Intelligent Processing
1.2.3 Basic Knowledge of Intelligent Processing Technology
1.3 The Content of This Book
References
2 Polymorphic Evolution Process Model for Time-Varying Machining Process
2.1 Description of the Machining Process System
2.1.1 The Cuter-Spindle Subsystem Dynamics Model
2.1.2 Workpiece-Fixture Subsystem Dynamic Model
2.2 Polymorphic Evolution Model of Machining Process
2.2.1 Definition of the Machining Process
2.2.2 Time Domain Dispersion of the Machining Process
2.2.3 Evolution of Polymorphic Models
2.3 Workpiece Geometric Evolution Model
2.3.1 Geometric Deformation Mapping Method
2.3.2 Deformation Mapping Modeling Method for Complex Machining Features
2.4 The Workpiece Dynamic Evolution Model
2.4.1 Dynamic Evolution Analysis of Workpiece Based on Structural Dynamic Modification Technique
2.4.2 Dynamic Evolution Analysis of Workpieces Based on the Thin Shell Model
2.5 Tool Wear Evolution Model
2.5.1 Tool Wear During the Machining Process
2.5.2 Evolutionary Modeling of Tool Flank Wear
References
3 Machining Process Monitoring and the Data Processing Method
3.1 The Detection Method During Cutting Process
3.2 Machining Process Detection
3.2.1 The Concept of Detection Processing
3.2.2 Implementation Method of Detection Processing
3.3 Milling Force Based Cutting Depth and Width Detection
3.3.1 Average Milling Force
3.3.2 Detection and Measurement in the Milling Process
3.3.3 Detection Response Equation
3.3.4 Detection and Recognition of Depth and Width of Cut
3.4 Detection and Recognition of Milling Cutter Wear Status
3.4.1 Measurement of Tool Wear
3.4.2 Milling Force Model of Worn Tool
3.4.3 Identification Process Analysis
3.4.4 Calculation and Identification of Wear
3.5 Identification of Cutting Force Coefficients Based on Monitored Data
3.5.1 Cutting Force Modeling Considering Cutter Vibrations
3.5.2 Cutting Force Coefficients Identification Considering Vibration
References
4 Learning and Optimization of Process Model
4.1 Learning and Optimization Method of the Machining Process Model
4.2 Time-Position Mapping of Processing Data
4.3 Iterative Learning Method of Machining Error Compensation
4.3.1 In-Position Detection Method for Workpiece Geometry Information
4.3.2 Compensation Modeling of Machining Errors for Thin-Wall Parts
4.3.3 Solution of Error Compensation Model for Thin-Walled Parts
4.3.4 Learning Control Method for Error Compensation Coefficient
4.3.5 The Application of Error Iterative Compensation Method in Thin-Walled Blade Machining
4.4 Iterative Learning Optimization Method for Deep-Hole Drilling Depth
4.4.1 Chip Evacuation Force Model for One-Step Drilling
4.4.2 Chip Evacuation Process in Peck Drilling for Deep-Hole
4.4.3 Iterative Learning Method for Drilling Depth Optimization
4.5 Process Optimization Method for Multi-hole Varying-Parameter Drilling
4.5.1 Mathematical Model of Drilling Parameter Optimization
4.5.2 Drilling Parameter Optimization Procedure
4.6 Cyclic Iterative Optimization Method for Process Parameters
4.6.1 Mathematical Model of Feed Rate Optimization
4.6.2 Online Solving for Feed Speed Optimization Problem
4.6.3 Offline Learning and Iterative Optimization for Process Parameters
References
5 Dynamic Response Prediction and Control for Machining Process
5.1 Control Method of Dynamic Response for Machining Process
5.2 Alternating Excitation Force During Milling
5.2.1 Alternating Excitation Force
5.2.2 Characterization and Decomposition of Alternating Excitation Force
5.3 Prediction of Milling Dynamic Response
5.3.1 Forced Vibration in Milling
5.3.2 Prediction of Milling Chatter Stability
5.4 Dynamic Response Control of Milling Based on Optimization of Cutting Parameters
5.5 Response Control Method Based on Variable Pitch Cutters Optimization Design
5.5.1 Stability Limit Calculation of Variable Pitch Cutters
5.5.2 Geometrical Relation Between Adjacent Pitch Angles
5.5.3 Design of Variable Pitch Angles
5.6 Control Method of Workpiece-Fixture Subsystem Dynamic Characteristics
5.6.1 Control Method Based on Additional Auxiliary Support
5.6.2 Control Method Based on Additional Masses
5.6.3 Control Method Based on Magnetorheological Damping Support
References
6 Clamping Perception for Residual Stress-Induced Deformation of Thin-Walled Parts
6.1 Residual Stress in Cutting Process
6.2 Residual Stress-Induced Deformation
6.3 Principles of RSID Perception and Prediction
6.4 RSID Perception Prediction Model
6.5 Potential Energy Perception of Residual Stress and Deformation in Typical Clamping Forms
6.5.1 Surface Constraints in Redundant Constraints
6.5.2 Redundant Constraints Are Point Constraints
6.6 Solving Residual Stress and Deformation Perception Prediction Model
6.6.1 Solution Method and Procedure
6.6.2 Application Cases in Thin-Walled Parts Machining
6.7 Active Control Method for Residual Stresses Induced Deformation of Thin-Walled Parts
6.7.1 Evolution of Residual Stress in Machining Process
6.7.2 The In-Processes Active Control Method
6.7.3 Application of Active Control Method for RSID in Blade Machining
References