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El. knyga: Optimisation of Manufacturing Processes: A Response Surface Approach

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(University of Canberra, Australia)
  • Formatas: 332 pages
  • Išleidimo metai: 12-Nov-2022
  • Leidėjas: Maney Publishing
  • Kalba: eng
  • ISBN-13: 9780429611674
Kitos knygos pagal šią temą:
Optimisation of Manufacturing Processes: A Response Surface ApproachDidesnis paveikslėlis
  • Formatas: 332 pages
  • Išleidimo metai: 12-Nov-2022
  • Leidėjas: Maney Publishing
  • Kalba: eng
  • ISBN-13: 9780429611674
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Many engineering companies around the world are currently undergoing a quality control and improvement revolution that originally started in Japan many decades ago and this book provides a brief overview of this revolution. Robust design is a central component of the modern approach to quality improvement and is a phrase used to describe any engineering activity whose Robust design is a central component of the modern approach to quality improvement and is a phrase used to describe any engineering activity whose objective is to develop high quality products (and processes) at low cost. A key characteristic of robust design is the use of statistically planned (designed) experiments to identify those process variables that determine product quality. Robust design was developed in Japan by G. Taguchi in the early 1950s and its widespread use throughout Japanese industry is one of the main reasons why the country has emerged as a major producer of relatively cheap high quality products, especially in the automobile, home electronics and microprocessing sectors. Despite its early success in Japan, robust design remained virtually untried in the United States and Europe until the early 1980s. However, the realisation that quality is a vital ingredient required for success in today's highly global and competitive markets has since prompted Western companies to embrace the robust design concept. This book explores the planning, implementation and analysis of experiments designed both to improve existing manufacturing process and to create newer and better processes and products.
Preface xi
I. CONCEPTS METHODS AND CASE STUDIES 1(26)
1. PROCESS OPTIMISATION THROUGH INDUSTRIAL EXPERIMENTATION
3(14)
1.1 OBJECTIVES OF INDUSTRIAL EXPERIMENTATION FOR QUALITY IMPROVEMENT
3(1)
1.2 A SIMPLE MODEL OF A MANUFACTURING PROCESS
4(2)
1.3 PROCESS OPTIMISATION
6(6)
1.4 STATISTICALLY PLANNED (DESIGNED) EXPERIMENTS
12(3)
1.5 PLANNING A RESEARCH PROGRAMME
15(2)
2. ENGINEERING CASE STUDIES
17(10)
2.1 THE AUSFORMING PROCESS
17(1)
2.2 HIGH STRENGTH STEELS
18(1)
2.3 HOT FORGED COPPER POWDER COMPACTS
18(3)
2.4 CLOSED DIE FORGING OF AERO ENGINE DISKS
21(2)
2.5 FRICTION WELDING
23(1)
2.6 THE INJECTION MOULDING EXPERIMENT
24(1)
2.7 THE SPRING FREE HEIGHT EXPERIMENT
25(1)
2.8 THE PRINTING PROCESS STUDY
26(1)
II. LINEAR EXPERIMENTAL DESIGNS 27(28)
3. THE 2^k EXPERIMENTAL DESIGN: FULL FACTORIALS
29(10)
3.1 THE 2^2 FACTORIAL DESIGN
29(4)
3.1.1 A Traditional Design
29(1)
3.1.2 A Full 2^2 Factorial Design in Standard Order Form
30(2)
3.1.3 A Geometric Representation of the 2^2 Experimental Design
32(1)
3.2 THE 2^3 FACTORIAL DESIGN
33(3)
3.2.1 A Geometric Representation of the 2^2 Experimental Design
33(1)
3.2.2 Standard Order Form for the 2^3 Factorial Experiment
33(2)
3.2.3 An Example of a Replicated 2^3 Factorial Experiment
35(1)
3.3 THE 2^K FACTORIAL DESIGN
36(3)
3.3.1 Standard Order Form for the 2^5 Factorial Experiment
36(1)
3.3.2 General Comments on the 2^k Designs
36(3)
4. THE 2^k-p EXPERIMENTAL DESIGN: FRACTIONAL FACTORIALS
39(16)
4.1 BASIC CONCEPTS
39(1)
4.2 THE ONE HALF FRACTION OF THE 2^K DESIGN
40(3)
4.2.1 Step One: Defining the Base Design
40(1)
4.2.2 Step Two: Introduction of the Remaining Factor
40(3)
4.3 OTHER FRACTIONAL FACTORIAL DESIGNS
43(2)
4.3.1 Step One: Defining the Base Design.
43(1)
4.3.2 Step two: Introduction of the Remaining Factors
43(2)
4.4 A 2^7-3IV DESIGN FOR THE AUSFORMING PROCESS
45(1)
4.5 TAGUCHI'S ORTHOGONAL ARRAYS
46(9)
III. OPTIMISATION OF LINEAR PROCESSES 55(174)
5. CONTROLLING THE MEAN: LOCATION EFFECTS IN LINEAR DESIGNS
57(52)
5.1 DEFINITION OF LOCATION EFFECTS
57(3)
5.1.1 Control of a Mean Quality Characteristic using Main Location Effects
57(1)
5.1.2 Control of a Mean Quality using First Order Interaction Location Effects
58(1)
5.1.3 Higher Order Interaction Location Effects
59(1)
5.2 METHODS OF CONTROLLING THE MEAN QUALITY CHARACTERISTICS
60(4)
5.2.1 A Control Matrix for the Mean of a Quality Characteristic
60(2)
5.2.2 A First Order Response Surface Model for the Mean.
62(2)
5.3 THE YATES AND LEAST SQUARES PROCEDURES FOR ESTIMATING LOCATION EFFECTS
64(4)
5.3.1 The Yates Technique
64(2)
5.3.2 The Least Squares Technique
66(2)
5.4 LOCATION EFFECTS ESTIMATED FROM FRACTIONAL DESIGNS
68(8)
5.4.1 Aliasing Algebra
70(3)
5.4.2 Taguchi Designs and Aliasing
73(1)
5.4.3 Yates Technique for Fractional Factorials
74(2)
5.5 LOCATION EFFECTS IN THE AUSFORMING PROCESS
76(33)
5.5.1 The First Two Factors Only
76(4)
5.5.2 The First Three Factors Only
80(5)
5.5.3 The First Five Factors Only
85(16)
5.5.4 All Seven Factors of the Ausforming Process
101(8)
6. TESTING THE IMPORTANCE OF LOCATION EFFECTS IN THE 2^K DESIGN
109(28)
6.1 A DISTRIBUTION OF EFFECT ESTIMATES
109(5)
6.2 THE STANDARD DEVIATION OF A LOCATION EFFECT ESTIMATE
114(2)
6.3 THE t TEST IN A REPLICATED DESIGN
116(6)
6.3.1 The Test
116(2)
6.3.2 Application of the t Test to the 2^3 Ausforming Experiment
118(4)
6.4 THE t TEST WITHIN THE LEAST SQUARES PROCEDURE
122(3)
6.5 A GRAPHICAL TEST FOR THE IMPORTANCE OF LOCATION EFFECTS
125(12)
6.5.1 Test Derivation
125(3)
6.5.2 Illustration of Graphical Test Using the High Strength Steel Case Study
128(4)
6.5.3 Illustration of Graphical Test Using the Ausforming Process
132(5)
7. CONTROLLING PROCESS VARIABILITY: DISPERSION EFFECTS IN LINEAR DESIGNS
137(72)
7.1 NOISE - DESIGN FACTOR INTERACTIONS AND PROCESS VARIABILITY
138(3)
7.2 A GENERALISED RESPONSE SURFACE APPROACH TO PROCESS VARIABILITY
141(2)
7.3 AN APPLICATION TO THE 2^5 DESIGN ON THE AUSFORMING
143(3)
7.4 PREDICTION ERRORS AND PROCESS VARIABILITY
146(2)
7.4.1 Estimate a Simplified Response Surface Model of the Process
146(1)
7.4.2 Calculate the Prediction Error Variability at Each Factor Level
146(1)
7.4.3 Testing the Importance of Dispersion Effects
147(1)
7.5 THE DISK FORGING OPERATION EXPERIMENT
148(11)
7.5.1 Estimate a Simplified Model of the Process
149(1)
7.5.2 Calculate the Error Variability at Each Factor Level
149(10)
7.5.3 Testing the Importance of Dispersion Effects
159(1)
7.6 A GENERALISED LINEAR MODEL
159(12)
7.6.1 Inner and Outer Arrays
159(1)
7.6.2 Simple Summary Statistics
160(4)
7.6.3 The Tendency for Process Mean and Variability to Move Together
164(2)
7.6.4 PerMIA Summary Statistics
166(2)
7.6.5 Step 1 Identify All the Control Factors
168(2)
7.6.6 Step
2. Obtain Reliable Estimates of the Dispersion Effects for All the Control Factors
170(1)
7.7 THE COPPER COMPACT EXPERIMENT
171(13)
7.7.1 Step
1. Identify All the Control Factors for Making Copper Compacts
172(10)
7.2.2 Step
2. Reliable Estimates of the Dispersion Effects for Making Copper Compacts
182(2)
7.8. COMPARING THE RESPONSE SURFACE AND GENERALISED LINEAR MODELS USING THE INJECTION MOULDING EXPERIMENT
184(25)
7.8.1 Design Problems
185(3)
7.8.2 Analysis of the Data
188(21)
7.8.2.1 The Blind Use of the (S-N)t Ratio
191(3)
7.8.2.2 The Lack of Analysis for Noise Factors
194(15)
8. LINEAR PROCESS OPTIMISATION
209(20)
8.1 A TWO STEP PROCESS OPTIMISATION PROCEDURE
209(5)
8.1.1 The Procedure
209(3)
8.1.2 General Techniques
212(2)
8.2 ILLUSTRATIONS OF PROCESS OPTIMISATION
214(17)
8.2.1 The Ausforming Process
214(4)
8.2.2 The Copper Compact Experiment
218(4)
8.2.3 The Injection Moulding Experiment
222(4)
8.2.4 Optimising the Disk Forging Operation
226(3)
IV. NON LINEAR EXPERIMENTAL DESIGNS 229(54)
9. SOME NON LINEAR EXPERIMENTAL DESIGNS
231(24)
9.1 3^K DESIGNS
231(4)
9.2 A 3^2 DESIGN FOR THE FRICTION WELDING CASE STUDY
235(1)
9.3 CENTRAL COMPOSITE DESIGNS
236(6)
9.4 A CENTRAL COMPOSITE DESIGN FOR THE LINEAR FRICTION WELDING CASE STUDY
242(3)
9.5 THE BOX-BEHNKEN DESIGN
245(5)
9.5.1 Find all Combinations of Two
245(1)
9.5.2 Form 2^2 Designs for all Pairings
246(2)
9.5.3 Replication of Centre Points
248(2)
9.6 MIXED LEVEL FACTORIAL DESIGNS
250(5)
9.6.1 Factors at Two and Three Levels
251(2)
9.6.2 Factors at Two and Four levels
253(2)
10. LINEAR AND NON LINEAR EFFECTS
255(28)
10.1 A NON LINEAR EFFECT
255(5)
10.2 THE SECOND ORDER RESPONSE SURFACE MODEL
260(4)
10.2.1 Structure of the Second Order Response Surface Model
260(1)
10.2.2 Some Models for the 3k Design
261(2)
10.2.3 Some Models for the Central Composite and Box Behnken Designs
263(1)
10.2.4 A Model for Mixed Factorial Designs
263(1)
10.3 ESTIMATING RESPONSE SURFACE MODELS
264(8)
10.3.1 Estimating a Second Order Response Surface Model
264(2)
10.3.2 Estimating Some Response Surface Models using Data from a 3^2 Design
266(3)
10.3.3 Estimating Some Response Surface Models using Data from a 3^3 Design
269(2)
10.3.4 Estimating Some Response Surface Models using Data from a Central Composite Design
271(1)
10.4 ANALYSIS OF THE FRICTION WELDING EXPERIMENT
272(13)
10.4.1 The First Two Process Variables
272(6)
10.4.2 All Three Process Variables
278(5)
V. OPTIMISATION OF NON LINEAR PROCESSES 283(32)
11. SEQUENTIAL TESTING
285(12)
11.1 SEQUENTIAL TESTING AND THE PATH OF STEEPEST ASCENT
285(3)
11.2 SEQUENTIAL EXPERIMENTATION FOR THE AUSFORMING PROCESS
288(9)
11.2.1 The First Two Factors Only
288(6)
11.2.2 The First Five Factors
294(3)
12. DUAL RESPONSE SURFACE METHODOLOGIES
297(18)
12.1 THE DUAL RESPONSE SURFACE METHODOLOGY
297(4)
12.1.2 Minimise Variability Subject to a Mean Constraint
298(3)
12.1.2 Minimise the Mean Square Error
301(1)
12.2 THE PRINTING PROCESS CASE STUDY
301(14)
12.2.1 The Experiment
301(1)
12.2.2 The PerMIA
302(2)
12.2.3 The Modelled Response Surface
304(1)
12.2.4 Minimise Variability Subject to a Mean Constraint
304(8)
12.2.5 The Mean Square Error
312(3)
REFERENCES 315(2)
INDEX 317


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