Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product

Promoppatum, Patcharapit; Yao, Shi‐Chune; Pistorius, Petrus Christiaan; Rollett, Anthony D.; Coutts, Peter J.; Lia, Frederick; Martukanitz, R. P.

doi:10.1007/s40964-018-0039-1

Cited by 64 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The thermal cycle has been numerically examined by modeling a temperature distribution profile induced by a melting pool of post deposit layers [ [8] , [9] , [10] , [11] ], which was useful to optimize the heat source parameter. Promoppatum et al [ 12 ] investigated the thermal history of the AM component experimentally and numerically, and Rodgers et al [ 13 ] simulated the microstructure imposed by the AM process. The investigation of internal stress development on the AM component is an important issue in the integrity of the material, as in the case of the welding process [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Unravelling thermal history during additive manufacturing of martensitic stainless steel

Chae

Huang

Woo

et al. 2021

Journal of Alloys and Compounds

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Unravelling thermal history during additive manufacturing of martensitic stainless steel

Chae

Huang

Woo

et al. 2021

Journal of Alloys and Compounds

View full text Add to dashboard Cite

show abstract

“…In the SLM process, the quality of the weld tracks on each layer depends not only on the processing parameters used for that layer but also on the temperature distribution of the previous layers. As described above, the temperature of the solidified layer beneath the powder bed gradually increases as the SLM process proceeds due to the effects of heat accumulation (Promoppatum et al, 2018;Krauss et al, 2015). If the temperature of the solidified layer reaches an extreme value, then an inappropriate choice of the parameter settings for the next layer may result in a wide range of undesirable defects such as over-burning, key-hole melting and extreme evaporation (Carl, 2015).…”

Section: Layer Heating Simulation Modelmentioning

confidence: 99%

“…Furthermore, as the number of fabricated layers in the SLM process increases, the temperature distribution of the solidified layer beneath the powder bed also increases due to the heat accumulation effect (Promoppatum et al, 2018). Therefore, the processing parameters selected at the beginning of the SLM process need to be also workable over a wide temperature range.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Tran

et al. 2021

RPJ

View full text Add to dashboard Cite

Purpose Depending on an experimental approach to find optimal parameters for producing fully dense (relative density > 99%) Inconel 718 (IN718) components in the selective laser melting (SLM) process is expensive and offers no guarantee of success. Accordingly, this study aims to propose a multi-scale simulation framework to guide the choice of processing parameters in a more pragmatic manner. Design/methodology/approach In the proposed approach, a powder layer, ray tracing and heat transfer simulation models are used to calculate the melt pool dimensions and evaporation volume corresponding to a small number of laser power and scanning speed conditions within the input design space. A layer-heating model is then used to determine the inter-layer idle time required to maximize the temperature convergence rate of the solidified layer beneath the power bed. The simulation results are used to train surrogate models to construct SLM process maps for 3,600 pairs of the laser power and scanning speed within the input design space given three different values of the underlying solidified layer temperature (i.e., 353 K, 673 K and 873 K). The ideal selection of laser power and scanning speed of each process map is chosen based on four quality-related criteria listed as follows: without the appearance of key-hole melting; an evaporation volume less than the volume of the d90 powder particles; ensuring the stability of single scan tracks; and avoiding a weak contact between the melt pool and substrate. Finally, the optimal laser power and scanning speed parameters for the SLM process are determined by superimposing the optimal regions of the individual process maps. Findings The feasibility of the proposed approach is demonstrated by fabricating IN718 test specimens using the optimal processing conditions identified by the simulation framework. It is shown that the maximum density of the fabricated parts is 99.94%, while the average density is 99.88% and the standard deviation is less than 0.05%. Originality/value The present study proposed a multi-scale simulation model which can efficiently predict the optimal processing conditions for producing fully dense components in the SLM process. If the geometry of the three-dimensional printed part is changed or the machine and powder material is altered, users can use the proposed method for predicting the processing conditions that can produce the high-density part.

show abstract

“…FEA has been used extensively to model nickel-based superalloys and components (Maharaj et al 2012). Furthermore, FEA and numerical models have been used to model AM microstructure and its evolution (Nie et al 2014;Tan et al 2020), thermal history (Promoppatum et al 2018), process parameter influence on grain morphology (Raghavan et al 2016), meltpool morphology predictions in LPBF alloy 718 (Romano et al 2016) and more. However, FEA can be complex, computationally expensive and over/under estimate stresses (Saberi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

et al. 2021

View full text Add to dashboard Cite

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at $$650\,^\circ $$ 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of $$1.40\%$$ 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications.

show abstract

Numerical modeling and experimental validation of thermal history and microstructure for additive manufacturing of an Inconel 718 product

Cited by 64 publications

References 36 publications

Unravelling thermal history during additive manufacturing of martensitic stainless steel

Unravelling thermal history during additive manufacturing of martensitic stainless steel

Multi-scale simulation approach for identifying optimal parameters for fabrication ofhigh-density Inconel 718 parts using selective laser melting

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

Contact Info

Product

Resources

About