Selective laser melting is fast evolving into an industrially applicable manufacturing process. While components produced from high-value materials, such as Ti6Al4V and Inconel 718 alloys, are already being produced, the processing of multi-material components still remains to be achieved by using laser additive manufacturing. The physical handling of multi-material in a SLM setup continues to be a primary challenge along with the selection of process parameters/plan to achieve the desired results – both challenges requiring considerable experimental undertakings. Consequently, numerical process modelling has been adopted towards tackling the latter challenge in an effective manner. In this paper, a numerical simulation based optimization study is undertaken to enable selective laser melting of multi-material tool inserts. A standard copper specimen covered by a thin layer of nickel is chosen, over which a layer of steel has been deposited using cold-spraying technique, such as to protect the microstructure of Ni during selective laser melting. The process modelled thus entails additively manufacturing a steel tool insert around the multi-material specimen with a goal of achieving a dense product while preventing recrystallization in the Nickel layer. The process is simulated using a high-fidelity thermo-microstructural model with constant processing parameters to capture the effect on Nickel layer. Based on results, key structural and process parameters are identified, and subsequently an optimization study is conducted using evolutionary algorithms to determine the appropriate process parameter values as well as processing sequence. The optimized process plan is then used to manufacture real multi-material tool insert samples by selective laser melting.
Repeatability and reproducibility of parts produced by selective laser melting is a standing issue, and coupled with a lack of standardized quality control presents a major hindrance towards maturing of selective laser melting as an industrial scale process. Consequently, numerical process modelling has been adopted towards improving the predictability of the outputs from the selective laser melting process. Establishing the reliability of the process, however, is still a challenge, especially in components having overhanging structures.
In this paper, a systematic approach towards establishing reliability of overhanging structure production by selective laser melting has been adopted. A calibrated, fast, multiscale thermal model is used to simulate the single track formation on a thick powder bed. Single tracks are manufactured on a thick powder bed using same processing parameters, but at different locations in a powder bed and in different laser scanning directions. The difference in melt track widths and depths captures the effect of changes in incident beam power distribution due to location and processing direction. The experimental results are used in combination with numerical model, and subjected to uncertainty and reliability analysis. Cumulative probability distribution functions obtained for melt track widths and depths are found to be coherent with observed experimental values. The technique is subsequently extended for reliability characterization of single layers produced on a thick powder bed without support structures, by determining cumulative probability distribution functions for average layer thickness, sample density and thermal homogeneity.
Residual stresses and deformations continue to remain one of the primary challenges towards expanding the scope of selective laser melting as an industrial scale manufacturing process. While process monitoring and feedback-based process control of the process has shown significant potential, there is still dearth of techniques to tackle the issue. Numerical modelling of selective laser melting process has thus been an active area of research in the last few years. However, large computational resource requirements have slowed the usage of these models for optimizing the process.
In this paper, a calibrated, fast, multiscale thermal model coupled with a 3D finite element mechanical model is used to simulate residual stress formation and deformations during selective laser melting. The resulting reduction in thermal model computation time allows evolutionary algorithm-based optimization of the process. A multilevel optimization strategy is adopted using a customized genetic algorithm developed for optimizing cellular scanning strategy for selective laser melting, with an objective of reducing residual stresses and deformations. The resulting thermo-mechanically optimized cellular scanning strategies are compared with standard scanning strategies and have been used to manufacture standard samples.
Selective laser melting is yet to become a standardized industrial manufacturing technique. The process continues to suffer from defects such as distortions, residual stresses, localized deformations and warpage caused primarily due to the localized heating, rapid cooling and high temperature gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge.
In this paper, a methodology for generating reliable, optimized scanning paths and process parameters for selective laser melting of a standard sample is introduced. The processing of the sample is simulated by sequentially coupling a calibrated 3D pseudo-analytical thermal model with a 3D finite element mechanical model.
The optimized processing parameters are subjected to a Monte Carlo method based uncertainty and reliability analysis. The reliability of the scanning paths are established using cumulative probability distribution functions for process output criteria such as sample density, thermal homogeneity, etc. A customized genetic algorithm is used along with the simulation model to generate optimized cellular scanning strategies and processing parameters, with an objective of reducing thermal asymmetries and mechanical deformations. The optimized scanning strategies are used for selective laser melting of the standard samples, and experimental and numerical results are compared.
Selective laser melting, as a rapid manufacturing technology, is uniquely poised to enforce a paradigm shift in the manufacturing industry by eliminating the gap between job- and batch-production techniques. Products from this process, however, tend to show an increased amount of defects such as distortions, residual stresses and cracks; primarily attributed to the high temperatures and temperature gradients occurring during the process. A unit cell approach towards the building of a standard sample, based on literature, has been investigated in the present work. A pseudo-analytical model has been developed and validated using thermal distributions obtained using different existing scanning strategies. Several existing standard and non-standard scanning methods have been evaluated and compared using the empirical model as well as a 3D-thermal finite element model. Finally, a new grid-based scan strategy has been developed for processing the standard sample, one unit cell at a time, using genetic algorithms, with an objective of reducing thermal asymmetries.
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