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Design for Additive Manufacturing
Design for Additive ManufacturingAdditive Manufacturing Materials and TechnologiesMartin Leary?
Copyright
Contents
Preface
Intended audience
Author summary
Acknowledgements
1 - Introduction to AM
1.1 What benefits are enabled by additive manufacture?
1.2 Design for Additive Manufacture
1.2.1 DFAM tools to predict AM system technical response
1.2.2 DFAM tools to identify commercial opportunities
1.2.3 DFAM tools for autonomous design
1.3 Evolution of AM and technology development trajectories
1.4 The techno-economic motivation for Design for AM (DFAM) tools
References
2 - AM production economics
2.1 Classical engineering economics
2.2 Additive manufacturing economics
2.3 Economically optimal AM scenarios
Batch-enabled scenarios
Complexity-enabled scenarios
Ultra-high complexity scenarios
Mass-customisation scenarios
2.3.1 Batch-enabled scenarios
1 Pre-production conceptual design models
2 Surgical planning models
3 Pre-production technical validation models
4 Bespoke design of functional medical devices and clinical tools
5 Production hardware - jigs, fixtures and gauges
6 Short volume production runs
2.3.2 Complexity-enabled scenarios
1 High efficiency topologies
2 Functionally integrated designs
3 Series production of complex geometries
2.3.3 Ultra-high complexity-enabled scenarios
1 Ultra-high-efficiency topologies
2 Generative design
2.3.4 Mass-customized scenarios
2.4 Enhanced commercial scenarios for AM
Low-batch and high-complexity scenarios
Low-batch and ultra-high complexity scenarios
2.5 The paradox of reduced cost with increased complexity
2.6 Economies of high-volume AM production
2.7 The paradox of volume intersection for optimal cost.
2.8 The flawed cost-independence assumption of AM
2.9 The economic necessity of design for AM
2.10 Summary of chapter outcomes
Batch-enabled scenarios (Fig. 2.4 - Zone 1)
Complexity-enabled scenarios (Fig. 2.4 - Zone 2)
Ultra-high complexity applications (Fig. 2.4 - Zone 3)
Mass-customisation (Section 2.3.4)
The paradox of reduced cost with increased complexity
The paradox of volume intersection for optimal cost
The flawed cost-independence assumption of AM
References
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3 - Digital design for AM
3.1 Digital data and engineering design
3.1.1 The present value of knowledge
3.1.2 Parallel set narrowing
3.1.3 Evolutionary versus revolutionary design
3.2 Digital data flow within AM design and production
3.2.1 Digital AM design sequence
3.2.1.1 Design specification
3.2.1.2 Embodiment design
3.2.1.3 Detail design
3.2.1.4 Computer aided design
3.2.1.5 Volumetric geometry
3.2.1.6 Slice geometry
3.2.1.7 Toolpath and process parameters
3.2.1.8 Manufacture
3.2.1.9 Inspection and certification
3.3 Digital DFAM opportunities for digital design and data management
3.3.1 Embodiment design
3.3.2 Detail design
3.3.2.1 Build orientation
3.3.2.2 Support structure generation
3.3.3 Computer aided design
3.3.4 Volumetric geometry
3.3.4.1 Stereolithographic file format
3.3.4.2 Additive manufacturing format
3.3.5 Slice geometry
3.3.6 Tool path and process parameters
3.3.7 Manufacture
3.3.8 Inspection and certification
3.4 Opportunities for advanced digital DFAM opportunities
3.4.1 CAD to slice data
3.4.2 CAD to toolpath data
3.4.3 Design specification to toolpath data
3.5 Summary of chapter outcomes
3.5.1 Engineering design.
3.5.2 Embodiment and detail design
3.5.3 Volumetric data
3.5.4 Slice and toolpath data
3.5.5 Manufacture, inspection and certification
3.5.6 Advanced DFAM opportunities
References
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4 - Detail DFAM
4.1 Generalizable DFAM strategies
4.1.1 Distinguishing attributes of AM
Sequential material addition
Common source material
Distinct hardware implementation
Digital dataflow
4.1.2 Case Study: DFAM strategies applied to high-value aerospace structure
A focus on material addition
Inside-out design
Manipulation of material addition
Toolpath optimization
Net-shape manufacture
Orientation design
4.2 Review of AM classifications and inherent design implications
4.2.1 Vat Polymerization
4.2.2 Material Jetting
4.2.3 Binder Jetting
4.2.4 Material Extrusion
4.2.5 Powder Bed Fusion
4.2.6 Sheet Lamination
4.2.7 Directed Energy Deposition
4.3 Selection of AM technologies
Part volume versus layer thickness
Material deposition rate versus physical part volume
Material flexibility versus support requirements
Capital installation costs versus available material classification
4.4 Specific AM design strategies
4.4.1 AM architecture
4.4.2 Toolpaths
4.4.3 Heat transfer (applicable for thermal AM systems)
4.4.4 Support structures
4.4.5 Recoating
4.4.6 Digital data management
4.4.7 Net-shape manufacture
Parts consolidation
Design for nesting
Design for functional geometry
Elimination of post-manufacture machining
Design for fits and assembly
Hybrid components
4.4.8 Miscellaneous effects
4.5 Chapter summary
References
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5 - Design of lattice and zero-mean curvature structures
5.1 Lattice structures.
5.1.1 Maxwell stability criterion
5.1.2 Observed mechanical response (unit cell)
5.1.3 Observed mechanical response (lattice system)
Initial plastic consolidation
Linear elastic response
Non-linear elastic response
Yield strength, or elastic limit, σel∗
Unloading Modulus, Eul∗
Ultimate compressive strength, σpl∗
Crushing strength
Densification
5.1.4 Prediction of AM lattice response
5.1.5 Hybrid lattice design
5.1.6 Conformal lattice structures
5.1.7 Experimental observation of AM lattice response
5.1.8 Numerical analysis of AM lattice
5.1.8.1 The curse of dimensionality
5.1.8.2 Scale effects
5.1.8.3 Material data availability
5.1.8.4 Geometric data acquisition
5.1.9 Fatigue failure mode of AM lattice
5.1.10 Impact failure mode
5.1.11 Lattice manufacturability
5.2 Triply periodic minimal surfaces
5.2.1 Physical and mathematical definitions
5.2.2 Technical application of TPMS
5.2.3 TPMS application fields and active research areas
Fundamental studies of morphology and manufacturability (TRL 1-3)
Mechanical and functional response of TPMS structures (TRL 4-6)
Application of TPMS to commercial structures (TRL 7-9)
5.2.4 Mechanical response data
5.2.5 Challenges to TPMS application - tool path design
5.3 Summary of lattice and TPMS experimental response
5.3.1 TPMS summary
5.4 Chapter summary
References
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6 - Topology optimization for AM
6.1 The motivation towards topology optimization for AM design
6.2 Efficient optimization of load bearing structures for AM scenarios.
6.2.1 Closed-form expressions of structural response
6.2.2 Precedent of existing optimized structures
6.3 Topology optimization methods
6.3.1 Topology optimization definition and methods
6.3.2 Existing TO methods
6.3.3 The Michell truss
6.3.4 Ground structures
6.3.5 Level-set methods
6.3.6 Discrete (voxel) methods
6.3.7 Bidirectional evolutionary structural optimization method
6.3.8 Solid isotropic material with penalization method
6.4 Opportunities for TO applied to AM
6.4.1 TO integrated DFAM tools
6.4.2 Compromise between TO outcomes and AM manufacturability considerations
6.4.3 Challenges associated with computational costs of TO simulation
6.5 Parametric optimization
6.5.1 Brute force methods
6.5.2 Sequential optimization methods
6.5.3 Practical application of brute force and iterative methods
6.6 Topology optimization and generative design (BC2AM)
6.6.1 Automated extraction of parametric data from TO outcomes
6.6.2 Smoothed TO outcomes
6.7 Case study: optimization of high-value non-stationary aerospace component
6.7.1 Strategy for the efficient application of TO to the AM scenario
6.7.1.1 Define initial conditions
6.7.1.2 Identify spatial limits and the available design volume
6.7.1.3 Apply TO methods
6.7.1.4 Accommodate AM manufacturability
6.7.1.5 Generate a parametric representation of the preferred topology
6.7.2 Design outcomes
6.8 Chapter summary
References
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7 - Generative design
7.1 Generative design challenges and opportunities for AM
7.1.1 What is generative design?
7.1.2 Generative design system architecture and implementation
Embodiment GD systems
Detail GD systems
7.1.3 Generative design system complexity
Complexity as size.
Complexity as coupling.
Design for Additive Manufacturing
Design for Additive ManufacturingAdditive Manufacturing Materials and TechnologiesMartin Leary?
Copyright
Contents
Preface
Intended audience
Author summary
Acknowledgements
1 - Introduction to AM
1.1 What benefits are enabled by additive manufacture?
1.2 Design for Additive Manufacture
1.2.1 DFAM tools to predict AM system technical response
1.2.2 DFAM tools to identify commercial opportunities
1.2.3 DFAM tools for autonomous design
1.3 Evolution of AM and technology development trajectories
1.4 The techno-economic motivation for Design for AM (DFAM) tools
References
2 - AM production economics
2.1 Classical engineering economics
2.2 Additive manufacturing economics
2.3 Economically optimal AM scenarios
Batch-enabled scenarios
Complexity-enabled scenarios
Ultra-high complexity scenarios
Mass-customisation scenarios
2.3.1 Batch-enabled scenarios
1 Pre-production conceptual design models
2 Surgical planning models
3 Pre-production technical validation models
4 Bespoke design of functional medical devices and clinical tools
5 Production hardware - jigs, fixtures and gauges
6 Short volume production runs
2.3.2 Complexity-enabled scenarios
1 High efficiency topologies
2 Functionally integrated designs
3 Series production of complex geometries
2.3.3 Ultra-high complexity-enabled scenarios
1 Ultra-high-efficiency topologies
2 Generative design
2.3.4 Mass-customized scenarios
2.4 Enhanced commercial scenarios for AM
Low-batch and high-complexity scenarios
Low-batch and ultra-high complexity scenarios
2.5 The paradox of reduced cost with increased complexity
2.6 Economies of high-volume AM production
2.7 The paradox of volume intersection for optimal cost.
2.8 The flawed cost-independence assumption of AM
2.9 The economic necessity of design for AM
2.10 Summary of chapter outcomes
Batch-enabled scenarios (Fig. 2.4 - Zone 1)
Complexity-enabled scenarios (Fig. 2.4 - Zone 2)
Ultra-high complexity applications (Fig. 2.4 - Zone 3)
Mass-customisation (Section 2.3.4)
The paradox of reduced cost with increased complexity
The paradox of volume intersection for optimal cost
The flawed cost-independence assumption of AM
References
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3 - Digital design for AM
3.1 Digital data and engineering design
3.1.1 The present value of knowledge
3.1.2 Parallel set narrowing
3.1.3 Evolutionary versus revolutionary design
3.2 Digital data flow within AM design and production
3.2.1 Digital AM design sequence
3.2.1.1 Design specification
3.2.1.2 Embodiment design
3.2.1.3 Detail design
3.2.1.4 Computer aided design
3.2.1.5 Volumetric geometry
3.2.1.6 Slice geometry
3.2.1.7 Toolpath and process parameters
3.2.1.8 Manufacture
3.2.1.9 Inspection and certification
3.3 Digital DFAM opportunities for digital design and data management
3.3.1 Embodiment design
3.3.2 Detail design
3.3.2.1 Build orientation
3.3.2.2 Support structure generation
3.3.3 Computer aided design
3.3.4 Volumetric geometry
3.3.4.1 Stereolithographic file format
3.3.4.2 Additive manufacturing format
3.3.5 Slice geometry
3.3.6 Tool path and process parameters
3.3.7 Manufacture
3.3.8 Inspection and certification
3.4 Opportunities for advanced digital DFAM opportunities
3.4.1 CAD to slice data
3.4.2 CAD to toolpath data
3.4.3 Design specification to toolpath data
3.5 Summary of chapter outcomes
3.5.1 Engineering design.
3.5.2 Embodiment and detail design
3.5.3 Volumetric data
3.5.4 Slice and toolpath data
3.5.5 Manufacture, inspection and certification
3.5.6 Advanced DFAM opportunities
References
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4 - Detail DFAM
4.1 Generalizable DFAM strategies
4.1.1 Distinguishing attributes of AM
Sequential material addition
Common source material
Distinct hardware implementation
Digital dataflow
4.1.2 Case Study: DFAM strategies applied to high-value aerospace structure
A focus on material addition
Inside-out design
Manipulation of material addition
Toolpath optimization
Net-shape manufacture
Orientation design
4.2 Review of AM classifications and inherent design implications
4.2.1 Vat Polymerization
4.2.2 Material Jetting
4.2.3 Binder Jetting
4.2.4 Material Extrusion
4.2.5 Powder Bed Fusion
4.2.6 Sheet Lamination
4.2.7 Directed Energy Deposition
4.3 Selection of AM technologies
Part volume versus layer thickness
Material deposition rate versus physical part volume
Material flexibility versus support requirements
Capital installation costs versus available material classification
4.4 Specific AM design strategies
4.4.1 AM architecture
4.4.2 Toolpaths
4.4.3 Heat transfer (applicable for thermal AM systems)
4.4.4 Support structures
4.4.5 Recoating
4.4.6 Digital data management
4.4.7 Net-shape manufacture
Parts consolidation
Design for nesting
Design for functional geometry
Elimination of post-manufacture machining
Design for fits and assembly
Hybrid components
4.4.8 Miscellaneous effects
4.5 Chapter summary
References
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5 - Design of lattice and zero-mean curvature structures
5.1 Lattice structures.
5.1.1 Maxwell stability criterion
5.1.2 Observed mechanical response (unit cell)
5.1.3 Observed mechanical response (lattice system)
Initial plastic consolidation
Linear elastic response
Non-linear elastic response
Yield strength, or elastic limit, σel∗
Unloading Modulus, Eul∗
Ultimate compressive strength, σpl∗
Crushing strength
Densification
5.1.4 Prediction of AM lattice response
5.1.5 Hybrid lattice design
5.1.6 Conformal lattice structures
5.1.7 Experimental observation of AM lattice response
5.1.8 Numerical analysis of AM lattice
5.1.8.1 The curse of dimensionality
5.1.8.2 Scale effects
5.1.8.3 Material data availability
5.1.8.4 Geometric data acquisition
5.1.9 Fatigue failure mode of AM lattice
5.1.10 Impact failure mode
5.1.11 Lattice manufacturability
5.2 Triply periodic minimal surfaces
5.2.1 Physical and mathematical definitions
5.2.2 Technical application of TPMS
5.2.3 TPMS application fields and active research areas
Fundamental studies of morphology and manufacturability (TRL 1-3)
Mechanical and functional response of TPMS structures (TRL 4-6)
Application of TPMS to commercial structures (TRL 7-9)
5.2.4 Mechanical response data
5.2.5 Challenges to TPMS application - tool path design
5.3 Summary of lattice and TPMS experimental response
5.3.1 TPMS summary
5.4 Chapter summary
References
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6 - Topology optimization for AM
6.1 The motivation towards topology optimization for AM design
6.2 Efficient optimization of load bearing structures for AM scenarios.
6.2.1 Closed-form expressions of structural response
6.2.2 Precedent of existing optimized structures
6.3 Topology optimization methods
6.3.1 Topology optimization definition and methods
6.3.2 Existing TO methods
6.3.3 The Michell truss
6.3.4 Ground structures
6.3.5 Level-set methods
6.3.6 Discrete (voxel) methods
6.3.7 Bidirectional evolutionary structural optimization method
6.3.8 Solid isotropic material with penalization method
6.4 Opportunities for TO applied to AM
6.4.1 TO integrated DFAM tools
6.4.2 Compromise between TO outcomes and AM manufacturability considerations
6.4.3 Challenges associated with computational costs of TO simulation
6.5 Parametric optimization
6.5.1 Brute force methods
6.5.2 Sequential optimization methods
6.5.3 Practical application of brute force and iterative methods
6.6 Topology optimization and generative design (BC2AM)
6.6.1 Automated extraction of parametric data from TO outcomes
6.6.2 Smoothed TO outcomes
6.7 Case study: optimization of high-value non-stationary aerospace component
6.7.1 Strategy for the efficient application of TO to the AM scenario
6.7.1.1 Define initial conditions
6.7.1.2 Identify spatial limits and the available design volume
6.7.1.3 Apply TO methods
6.7.1.4 Accommodate AM manufacturability
6.7.1.5 Generate a parametric representation of the preferred topology
6.7.2 Design outcomes
6.8 Chapter summary
References
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7 - Generative design
7.1 Generative design challenges and opportunities for AM
7.1.1 What is generative design?
7.1.2 Generative design system architecture and implementation
Embodiment GD systems
Detail GD systems
7.1.3 Generative design system complexity
Complexity as size.
Complexity as coupling.