Digitalization of design for support structures in laser powder bed fusion of metals

Abstract
Table of content
List of figures
List of tables
List of listings
List of abbreviations
Nomenclature
1 Introduction
2 Digital production by additive manufacturing
2.1 Laser powder bed fusion of metals (PBF-LB/M)
2.1.1 Technical process
2.1.2 Digital transformation of (additive) manufacturing
2.2 Digitalization of part design by topology optimization
2.2.1 Topology optimization methods
2.2.2 Numerical challenges
2.2.3 Physics addressed in topology optimization
2.2.4 Solver algorithms
2.2.5 Topology optimization for additive manufacturing
2.3 Digitalization of PBF-LB/M process
2.3.1 Process modeling
2.3.2 Material modeling
2.4 Support structures in PBF-LB/M
2.4.1 Integration of supports in the manufacturing process
2.4.2 Challenges in the application of supports
2.5 Support structure optimization
2.5.1 Support structure avoidance
2.5.2 Optimization of available support structures
2.5.3 Development of novel support structures
2.5.4 General characteristics of optimization approaches
2.5.5 Optimization goals
2.5.6 Quantification of optimization success
2.5.7 Automated support structure removal
2.6 Economic evaluation of additive manufacturing
2.6.1 Cost calculation
2.6.2 Cost prediction
3 Research Hypothesis and Methodology
4 Material Model of Ti-6Al-4V Alloy in Laser Powder Bed Fusion
4.1 Thermo-physical properties of Ti-6Al-4V
4.1.1 β-transus temperature
4.1.2 Solidus temperature
4.1.3 Liquidus temperature
4.1.4 Evaporation temperature
4.1.5 Martensite start temperature
4.1.6 Density
4.1.7 Specific heat capacity
4.1.8 Thermal conductivity
4.1.9 Powder material properties
4.2 Optical properties of Ti-6Al-4V
4.3 Mechanical properties of Ti-6Al-4V
4.3.1 Young’s modulus
4.3.2 Yield strength
4.3.3 Ultimate tensile strength
4.3.4 Poisson’s ratio
4.3.5
Coefficient of thermal expansion
5 Support Structure Topology Optimization
5.1 Topology optimization setup
5.1.1 Tree evaluation parameters
5.1.2 Experimental plan
5.1.3 Mesh convergence study
5.2 Results of systematic support structure topology optimization
5.2.1 General observations
5.2.2 Details on relevant tree design parameters
6 Support Structure Design
6.1 Digital tree (support) structure generation
6.1.1 Academic approaches to tree support generation
6.1.2 Commercial implementations of tree support structures
6.1.3 Algorithmic botany
6.2 Tree support modeling procedure
6.2.1 Data import
6.2.2 Creation of interface points
6.2.3 Tree sampling
6.2.4 Tree design
6.2.5 Data export
6.3 Validation of design rule implementation
7 Support Structure Performance Benchmark
7.1 Technical Performance
7.1.1 Design of benchmark parts
7.1.2 Definition of measurement methods
7.2 Economic Performance
7.2.1 Cost model for support structures in PBF-LB/M
7.2.2 Procedure for quick cost evaluation during the benchmark procedure
8 Demonstration of algorithmic support structures
8.1 Process simulation
8.1.1 Simulation setup
8.1.2 Results & discussion
8.2 Support generation
8.2.1 Block & cone supports
8.2.2 Algorithmic tree supports
8.3 Specimen manufacturing and technical evaluation
8.3.1 Data preparation
8.3.2 Manufacturing results
8.3.3 Support removal
8.3.4 Dimensional accuracy
8.4 Economic evaluation
8.4.1 Input parameters
8.4.2 Results
8.5 Benchmark summary & conclusion
9 Conclusion
10 References
Appendix
A.1
Technical documentation of slicer test specimen
A.2
Technical documentation of the benchmark parts
A.3
Static input parameter of cost model
A.4
Experimental results of demonstration – block support
A.5
Experimental results of demonstration – cone support
A.6
Experimental results of demonstration – tree support (dg = 2 mm)
A.7
Experimental results of demonstration – tree support (dg = 3 mm)