Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling
Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/10/5/683 |
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| author | Sudeepta Mondal Daniel Gwynn Asok Ray Amrita Basak |
| author_facet | Sudeepta Mondal Daniel Gwynn Asok Ray Amrita Basak |
| author_sort | Sudeepta Mondal |
| collection | DOAJ |
| description | Metal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable <i>a priori</i> estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process. |
| first_indexed | 2024-03-10T19:39:19Z |
| format | Article |
| id | doaj.art-140e8a421bde49d98c9168a49ffdc67e |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T19:39:19Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-140e8a421bde49d98c9168a49ffdc67e2023-11-20T01:22:46ZengMDPI AGMetals2075-47012020-05-0110568310.3390/met10050683Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid ModelingSudeepta Mondal0Daniel Gwynn1Asok Ray2Amrita Basak3Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USADepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USAMetal additive manufacturing (AM) works on the principle of consolidating feedstock material in layers towards the fabrication of complex objects through localized melting and resolidification using high-power energy sources. Powder bed fusion and directed energy deposition are two widespread metal AM processes that are currently in use. During layer-by-layer fabrication, as the components continue to gain thermal energy, the melt pool geometry undergoes substantial changes if the process parameters are not appropriately adjusted on-the-fly. Although control of melt pool geometry via feedback or feedforward methods is a possibility, the time needed for changes in process parameters to translate into adjustments in melt pool geometry is of critical concern. A second option is to implement multi-physics simulation models that can provide estimates of temporal process parameter evolution. However, such models are computationally near intractable when they are coupled with an optimization framework for finding process parameters that can retain the desired melt pool geometry as a function of time. To address these challenges, a hybrid framework involving machine learning-assisted process modeling and optimization for controlling the melt pool geometry during the build process is developed and validated using experimental observations. A widely used 3D analytical model capable of predicting the thermal distribution in a moving melt pool is implemented and, thereafter, a nonparametric Bayesian, namely, Gaussian Process (GP), model is used for the prediction of time-dependent melt pool geometry (e.g., dimensions) at different values of the process parameters with excellent accuracy along with uncertainty quantification at the prediction points. Finally, a surrogate-assisted statistical learning and optimization architecture involving GP-based modeling and Bayesian Optimization (BO) is employed for predicting the optimal set of process parameters as the scan progresses to keep the melt pool dimensions at desired values. The results demonstrate that a model-based optimization can be significantly accelerated using tools of machine learning in a data-driven setting and reliable <i>a priori</i> estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.https://www.mdpi.com/2075-4701/10/5/683additive manufacturingmelt pool dimension controlmachine learningGaussian process modelingBayesian Optimizationsurrogate-assisted modeling |
| spellingShingle | Sudeepta Mondal Daniel Gwynn Asok Ray Amrita Basak Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling Metals additive manufacturing melt pool dimension control machine learning Gaussian process modeling Bayesian Optimization surrogate-assisted modeling |
| title | Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling |
| title_full | Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling |
| title_fullStr | Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling |
| title_full_unstemmed | Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling |
| title_short | Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling |
| title_sort | investigation of melt pool geometry control in additive manufacturing using hybrid modeling |
| topic | additive manufacturing melt pool dimension control machine learning Gaussian process modeling Bayesian Optimization surrogate-assisted modeling |
| url | https://www.mdpi.com/2075-4701/10/5/683 |
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