Muutke küpsiste eelistusi

Intelligent Machining [Kõva köide]

Edited by (University of Aveiro, Portugal), Edited by (Rutgers University, USA)
  • Formaat: Hardback, 320 pages, kõrgus x laius x paksus: 236x160x18 mm, kaal: 522 g
  • Ilmumisaeg: 08-May-2009
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848211295
  • ISBN-13: 9781848211292
Teised raamatud teemal:
Intelligent MachiningSuurem pilt
  • Formaat: Hardback, 320 pages, kõrgus x laius x paksus: 236x160x18 mm, kaal: 522 g
  • Ilmumisaeg: 08-May-2009
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848211295
  • ISBN-13: 9781848211292
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848211292.html
Märksõnad:
Machining, as a reliable manufacturing process, still offers unmatched capabilities in producing high quality three-dimensional parts from metals, polymers, ceramics, wood and composites. Advances in computational modeling and optimization methods enabled researchers to develop cost effective and high throughput modern machining processes.

This book aims to provide recent advances intelligent machining for modern manufacturing engineering. It includes six chapters that provide basic fundamentals, modern machining processes, analytical and mechanistic modeling approaches, finite element modeling and systems based modeling, recent optimization methods and case studies.

Preface xi
Modern Machining Process
1(70)
Luis Norberto Lopez De Lacalle
Joaquim De Ciurana
Tugrul Ozel
Introduction
1(1)
High speed cutting
2(1)
Hard turning
3(12)
Tool micro-geometry
8(3)
Surface quality and integrity
11(1)
Tool wear and failure
11(4)
High-speed milling
15(28)
Historical remarks
15(3)
Current meaning of HSM
18(3)
High-speed milling of aluminum alloys
21(9)
High-speed milling of titanium alloys
30(1)
High-speed milling of die/mould steels
31(5)
High-speed milling of superalloys
36(1)
Machine tool technology
37(5)
Cutting tool technology
42(1)
High-throughput drilling
43(15)
Throughput drilling process parameters
45(4)
Models for cutting forces in throughput drilling
49(6)
Optimal conditions in throughput drilling
55(3)
Environmentally benign manufacturing
58(3)
Dry and near-to-dry machining
59(1)
Reduction of machine power consumption
60(1)
References
61(10)
Analytical and Mechanistic Modeling of Machining Processes
71(54)
Wit Grzesik
The essential features of metal cutting processes
71(3)
Orthogonal (two-dimensional) machining
71(1)
Oblique (3D) machining
72(2)
Plastic deformation and fracture
74(4)
Mechanisms of plastic deformation
74(1)
Measures of plastic deformation
74(1)
Material constitutive models
75(1)
Mechanism of fracture in metal cutting
76(2)
Chip formation in metal cutting operations
78(19)
Models for primary deformation zone
78(2)
Chip formation mechanisms
80(2)
Chip formation models
82(6)
Relationships for shear angle
88(5)
Chip flow rules
93(3)
Fracture in chip breaking
96(1)
Cutting forces and stresses
97(6)
Force distribution in the cutting zone
97(2)
Models for cutting forces
99(1)
Ploughing force and minimum UCT
100(2)
Stresses on the shear plane
102(1)
Cutting energy and power
103(3)
Components of cutting energy
103(1)
Specific cutting energy/power
104(2)
Friction in metal cutting
106(5)
Models for tool/chip interface
106(2)
Tool/chip contact length
108(1)
Determination of friction coefficient
109(2)
Relationships between friction and plastic deformation
111(1)
Thermal modeling
111(4)
Heat partitioning
111(2)
General analytical model for cutting temperature
113(1)
Temperature in the primary and secondary deformation zone
114(1)
Mechanistic and predictive models for orthogonal and oblique cutting
115(6)
Mechanistic models
115(2)
Strain rate analysis
117(2)
Similarity method
119(2)
References
121(4)
Finite Element Modeling of Machining Processes
125(48)
Pedro J. Arrazola
Tugrul Ozel
Introduction
125(2)
Finite element modeling at the meso scale (cutting edge)
127(10)
Finite element models: formulations
130(5)
Commercial software
135(2)
Sensitivity study to define input parameters
137(1)
Finite element model definition
137(2)
Sensitivity analysis of input parameters
139(9)
Test plans
139(2)
Analysis of the results
141(2)
Process and numerical parameter influence
143(1)
Identification tolerance
143(1)
Inverse identification of input parameters
144(4)
Identification of input parameters
148(9)
Material behavior law
148(5)
Friction coefficient
153(4)
Identification of the other parameters
157(1)
Finite element model validation
157(5)
In-process techniques
157(2)
Before and post-process techniques
159(1)
Validation of FEM results
159(2)
Summary
161(1)
Conclusions
162(1)
Acknowledgments
163(1)
References
163(10)
Computational Modeling of Machining Systems
173(42)
Ramon Quiza
J. Paulo Davim
Introduction
173(5)
Relevance of the modeling of machining systems
173(1)
Brief historical note
174(3)
Off-line versus on-line modeling
177(1)
Computational tools for modeling of machining systems
178(9)
Preliminary considerations
178(1)
Artificial neural networks
178(4)
Fuzzy logic
182(2)
Neuro-fuzzy systems
184(1)
Other non-conventional techniques
185(2)
Modeling for monitoring and control
187(10)
Tool wear monitoring
187(7)
Tool breakage and fault detection
194(1)
Monitoring of other parameters
195(1)
Intelligent control
196(1)
Modeling for process planning and optimization
197(2)
Modeling of tool wear and tool life
197(1)
Modeling of surface roughness
198(1)
Modeling of cutting forces
198(1)
Modeling of other parameters
199(1)
A case study
199(9)
Experimental set-up
200(2)
Neural network-based modeling
202(4)
Comparison between statistical and neural network models
206(2)
Concluding remarks
208(1)
Acknowledgment
209(1)
References
209(6)
Design of Experiments
215(30)
Vinayak N. Gaitonde
Ramesh S. Karnik
J. Paulo Davim
Introduction
215(2)
Conventional approach
216(1)
Need for mathematical modeling
216(1)
Design of experiments
217(6)
Classification of DOE
218(5)
Modeling using response surface methodology
223(8)
Steps for the development of an RSM-based model
223(3)
Response surface modeling - a case study using FFD
226(1)
Response surface modeling - a case study using CCD
227(2)
Response surface modeling - a case study using BBD
229(2)
Taguchi-based designs
231(10)
Experimental plans and orthogonal arrays
231(2)
Signal-to-noise ratio
233(1)
Taguchi optimization procedure
234(1)
Taguchi multi-objective optimization
235(6)
Summary
241(1)
Acknowledgement
242(1)
References
242(3)
Single and Multi-objective Optimization Methods
245(26)
Tugrul Ozel
Machining optimization
245(3)
Intelligent optimization techniques
248(5)
Case studies in machining optimization
253(14)
Minimizing surface roughness and minimizing machining time
253(5)
Maximizing tool life and maximizing material removal rate
258(1)
Minimizing machining induced stress and minimizing surface roughness
259(8)
References
267(4)
List of Authors 271(2)
Index 273
Tugrul Özel is an Assistant Professor of Industrial and Systems Engineering at Rutgers University, USA. His teaching and research interests include the modeling of machining processes, automation and process control, optimization of processes and systems, and micro/nano manufacturing.

J. Paulo Davim is Aggregate Professor in the Department of Mechanical Engineering of the University of Aveiro, Portugal and is Head of MACTRIB (Machining and Tribology Research Group). His main research interests include machining/manufacturing processes and tribology/surface engineering.