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

Additive manufacturing is considered a key technology for digital production. However, several barriers towards the broad industrial application exist, e.g. the associated cost and the required experience regarding the manufacturing process. To eradicate these barriers, the complete digitalization of the value creation process is needed. In this thesis, a digital, automated support structure design procedure is developed. Topology optimization is used for design rule determination, and the space colonization algorithm is adapted for the automated design. The validity of the procedure is proven experimentally, revealing sufficient mechanical performance alongside cost reduction at medium to large production scales. The content Provides a concise review of support structure optimization in laser powder bed fusion Includes thermo-mechanical material model of Ti-6Al-4V alloy Contains cost model for calculation of support-induced costs The author Katharina Bartsch studied mechanical engineering with a focus on product development, materials and production at the Technical University of Hamburg. Here, she received her doctorate in 2022 under Prof. Dr.-Ing. Claus Emmelmann (Institute for Laser and System Technologies - iLAS). During her time as a doctoral candidate, she worked as a research associate as well as chief engineer (since 2020) at the iLAS and as a team leader and research associate (since 2019) at the LZN Laser Zentrum Nord GmbH, later Fraunhofer Research Institution for Additive Production Technologies IAPT Abstract 8 Table of content 10 List of figures 13 List of tables 16 List of listings 18 List of abbreviations 19 Nomenclature 21 1 Introduction 33 2 Digital production by additive manufacturing 36 2.1 Laser powder bed fusion of metals (PBF-LB/M) 36 2.1.1 Technical process 36 2.1.2 Digital transformation of (additive) manufacturing 40 2.2 Digitalization of part design by topology optimization 43 2.2.1 Topology optimization methods 45 2.2.2 Numerical challenges 52 2.2.3 Physics addressed in topology optimization 54 2.2.4 Solver algorithms 56 2.2.5 Topology optimization for additive manufacturing 57 2.3 Digitalization of PBF-LB/M process 59 2.3.1 Process modeling 59 2.3.2 Material modeling 65 2.4 Support structures in PBF-LB/M 75 2.4.1 Integration of supports in the manufacturing process 75 2.4.2 Challenges in the application of supports 79 2.5 Support structure optimization 80 2.5.1 Support structure avoidance 84 2.5.2 Optimization of available support structures 87 2.5.3 Development of novel support structures 91 2.5.4 General characteristics of optimization approaches 95 2.5.5 Optimization goals 96 2.5.6 Quantification of optimization success 97 2.5.7 Automated support structure removal 100 2.6 Economic evaluation of additive manufacturing 102 2.6.1 Cost calculation 104 2.6.2 Cost prediction 107 3 Research Hypothesis and Methodology 110 4 Material Model of Ti-6Al-4V Alloy in Laser Powder Bed Fusion 113 4.1 Thermo-physical properties of Ti-6Al-4V 114 4.1.1 β-transus temperature 115 4.1.2 Solidus temperature 120 4.1.3 Liquidus temperature 120 4.1.4 Evaporation temperature 121 4.1.5 Martensite start temperature 122 4.1.6 Density 123 4.1.7 Specific heat capacity 126 4.1.8 Thermal conductivity 131 4.1.9 Powder material properties 136 4.2 Optical properties of Ti-6Al-4V 139 4.3 Mechanical properties of Ti-6Al-4V 140 4.3.1 Young’s modulus 148 4.3.2 Yield strength 149 4.3.3 Ultimate tensile strength 151 4.3.4 Poisson’s ratio 151 4.3.5 Coefficient of thermal expansion 153 5 Support Structure Topology Optimization 155 5.1 Topology optimization setup 155 5.1.1 Tree evaluation parameters 158 5.1.2 Experimental plan 159 5.1.3 Mesh convergence study 163 5.2 Results of systematic support structure topology optimization 163 5.2.1 General observations 163 5.2.2 Details on relevant tree design parameters 166 6 Support Structure Design 175 6.1 Digital tree (support) structure generation 175 6.1.1 Academic approaches to tree support generation 175 6.1.2 Commercial implementations of tree support structures 178 6.1.3 Algorithmic botany 181 6.2 Tree support modeling procedure 186 6.2.1 Data import 188 6.2.2 Creation of interface points 189 6.2.3 Tree sampling 192 6.2.4 Tree design 194 6.2.5 Data export 200 6.3 Validation of design rule implementation 200 7 Support Structure Performance Benchmark 203 7.1 Technical Performance 203 7.1.1 Design of benchmark parts 206 7.1.2 Definition of measurement methods 208 7.2 Economic Performance 209 7.2.1 Cost model for support structures in PBF-LB/M 209 7.2.2 Procedure for quick cost evaluation during the benchmark procedure 216 8 Demonstration of algorithmic support structures 218 8.1 Process simulation 218 8.1.1 Simulation setup 219 8.1.2 Results & discussion 221 8.2 Support generation 224 8.2.1 Block & cone supports 224 8.2.2 Algorithmic tree supports 228 8.3 Specimen manufacturing and technical evaluation 234 8.3.1 Data preparation 235 8.3.2 Manufacturing results 236 8.3.3 Support removal 239 8.3.4 Dimensional accuracy 241 8.4 Economic evaluation 242 8.4.1 Input parameters 242 8.4.2 Results 244 8.5 Benchmark summary & conclusion 251 9 Conclusion 253 10 References 257 Appendix 310 A.1 Technical documentation of slicer test specimen 310 A.2 Technical documentation of the benchmark parts 312 A.3 Static input parameter of cost model 317 A.4 Experimental results of demonstration – block support 320 A.5 Experimental results of demonstration – cone support 322 A.6 Experimental results of demonstration – tree support (dg = 2 mm) 324 A.7 Experimental results of demonstration – tree support (dg = 3 mm) 326