Sheet Metal Forming Optimization: Bioinspired Approaches

✍ Scribed by Ganesh M. Kakandikar, Vilas M. Nandedkar

• Publisher: CRC Press;Taylor & Francis
• Year: 2018
• Tongue: English
• Leaves: 248
• Edition: 1
• Category: Library

✦ Synopsis

Automotive and aerospace components, utensils, and many other products are manufactured by a forming/drawing process on press machines of very thin sheet metal, 0.8 to 1.2 mm. It is imperative to study the effect of all involved parameters on output of this type of manufacturing process. This book offers the readers with application and suitability of various evolutionary, swarm, and bio-inspired optimization algorithms for sheet metal forming processes. Book initiates by presenting basics of metal forming, formability followed by discussion of process parameters in detail, prominent modes of failure, basics of optimization and various bioinspired approaches followed by optimization studies on various industrial components applying bioinspired optimization algorithms.

Key Features:

• Focus on description of basic investigation of metal forming, as well as evolutionary optimization

• Presentation of innovative optimization methodologies to close the gap between those formulations and industrial problems, aimed at industrial professionals

• Includes mathematical modeling of drawing/forming process

• Discusses key performance parameters, such as Thinning, Fracture, and Wrinkling

• Includes both numerical and experimental analysis

✦ Table of Contents

Content: CHAPTER 01: INTRODUCTION TO METAL FORMING1.1 INTRODUCTION1.2 DRAWING/DEEP DRAWING PROCESS1.3 STATE OF STRESS IN DEEP DRAWING 1.4 DESCRIPTION AND SIGNIFICANCE OF INTRINSIC SHEET METAL PROPERTIES1.5 FORMABILITY OF SHEET METAL1.6 STRAIN-DEFINED FAILURE-LIMIT CURVES1.7 CONSTRUCTION OF FORMING LIMIT DIAGRAM1.8 THICKNESS GRADIENTCHAPTER 02: PROCESS PARAMETERS IN DRAWING2.1 BLANK HOLDING FORCE2.2 FRICTION IN DRAWING2.3 FRICTIONAL PROBLEMS IN DEEP DRAWING2.4 CONTACT REGIONS IN DEEP DRAWING2.5 LUBRICATION MECHANISM IN DEEP DRAWING2.6 MICROMECHANISM2.7 THE GENERALIZED STRIBECK CURVE2.8 HOW TO CONTROL THE FRICTION LEVEL2.9 PLOUGHING AND ADHESION 2.10 FRICTION AND DRAWABILITY2.11 PROCESS IMPROVEMENTS2.12 DIE PROFILE RADIUS AND PUNCH NOSE RADIUSCHAPTER 03: FAILURES IN DRAWING3.1 THINNING AND THICKENING BEHAVIOR3.2 WRINKLING IN FORMING3.3 SPRINGBACK 3.4 FRACTURECHAPTER 04: ENGINEERING OPTIMIZATION4.1 OPTIMIZATION4.2 CLASSIFICATION OF OPTIMIZATION PROBLEMS4.3 LOCAL SEARCH METHODS4.4 GLOBAL SEARCH METHODS4.5 DEVELOPMENTS IN OPTIMIZATION APPROACHES4.6 BIOINSPIRED ALGORITHMSCHAPTER 05: THINNING OPTIMIZATION - COHORT INTELLIGENCE APPROACH5.1 COHORT INTELLIGENCE ALGORITHM5.2 GENERAL CONSTRAINED OPTIMIZATION PROBLEM5.3 PENALTY FUNCTION 5.4 STATIC PENALTY FUNCTION METHOD5.5 DYNAMIC PENALTY FUNCTION5.6 PROPOSED INNOVATIVE METHODOLOGY5.7 CONFIGURATIONS OF COMPONENTS5.8 SEALING COVER5.9 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - THINNING.5.10 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS5.11 ANALYSIS OF VARIANCE 5.12 OBJECTIVE FUNCTION FORMULATION5.13 RESULTS5.14 VALIDATION - NUMERICAL SIMULATION5.15 EXPERIMENTAL VALIDATIONCHAPTER 06: SPRING BACK OPTIMIZATION - FLOWER POLLINATION6.1 INTRODUCTION6.2 FLOWER POLLINATION6.3 FLOWCHART6.4 PSEUDOCODE6.5 SEALING COVER6.6 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - SPRINGBACK6.7 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS6.8 ANALYSIS OF VARIANCE 6.9 OBJECTIVE FUNCTION FORMULATION6.10 RESULTS6.11 VALIDATION - NUMERICAL SIMULATIONCHAPTER 07: FRACTURE OPTIMIZATION - GENETIC ALGORITHM APPROACH7.1 INTRODUCTION7.2 TAILCAP7.3 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - SPRINGBACK7.4 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS7.5 ANALYSIS OF VARIANCE7.6 OBJECTIVE FUNCTION FORMULATION7.7 RESULTS7.8 VALIDATION - NUMERICAL SIMULATIONCHAPTER 08: THINNING OPTIMIZATION OF PUNCH PLATE: GREY WOLF OPTIMIZER8.1 INTRODUCTION8.2 INSPIRATION8.3 MATHEMATICAL MODEL AND ALGORITHM8.4 THE PSEUDO CODE8.5 PENALTY FUNCTION8.6 PUNCH PLATE8.7 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - THINNING.8.8 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS8.9 ANALYSIS OF VARIANCE 8.10 OBJECTIVE FUNCTION FORMULATION8.11 RESULTS8.12 VALIDATION - NUMERICAL SIMULATIONCHAPTER 09: WRINKLING OPTIMIZATION: FIREFLY ALGORITHM9.1 BEHAVIOUR OF FIREFLIES9.2 FIREFLY ALGORITHM9.3 PSEUDOCODE9.4 CONNECTOR9.5 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - WRINKLING9.6 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS9.7 ANALYSIS OF VARIANCE9.8 OBJECTIVE FUNCTION FORMULATION9.9 RESULTS9.10 VALIDATION - NUMERICAL SIMULATIONCHAPTER 10: THICKNESS GRADIENT OPTIMIZATION: ANT LION APPROACH10.1 INTRODUCION10.2 THE PSEUDO CODE OF ALO ALGORITHM10.3 TAILCAP10.4 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - THICKNESS GRADIENT10.5 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS10.6 ANALYSIS OF VARIANCE10.7 OBJECTIVE FUNCTION FORMULATION10.8 RESULTS10.9 VALIDATION - NUMERICAL SIMULATIONCHAPTER 11: SPRINGBACK OPTIMIZATION: CUCKOO SEARCH 11.1 CUCKOO BREEDING BEHAVIOUR 11.2 LEVY FLIGHTS11.3 CUCKOO SEARCH11.4 PUNCH PLATE11.5 SELECTION OF PROCESS PARAMETERS AND PERFORMANCE MEASURE - THINNING.11.6 NUMERICAL INVESTIGATIONS - TAGUCHI DESIGN OF EXPERIMENTS11.7 ANALYSIS OF VARIANCE11.8 OBJECTIVE FUNCTION FORMULATION11.9 RESULTS

✦ Subjects

Sheet-metal work.