scholarly journals Penetration Depth Modeling and Process Parameter Maps for Laser Welds Using Machine Learning

2021 ◽  
Author(s):  
Bum-su Go ◽  
Hyeonjeong You ◽  
Hee-seon Bang ◽  
Cheolhee Kim
Keyword(s):  
Machine Learning ◽  
Penetration Depth ◽  
Process Parameter ◽  
Laser Welds

Control of penetration depth fluctuations in single-mode fiber laser welds

2010 ◽  
Vol 22 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Matt J Reiter ◽  
Dave F Farson ◽  
M Mehl
Keyword(s):  
Fiber Laser ◽  
Penetration Depth ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Laser Welds

Analysis of penetration depth fluctuations in single-mode fibre laser welds

2009 ◽  
Vol 42 (11) ◽  
pp. 115501 ◽  
Author(s):  
Jung-Ho Cho ◽  
Dave F Farson ◽  
Matt J Reiter
Keyword(s):  
Penetration Depth ◽  
Single Mode ◽  
Fibre Laser ◽  
Single Mode Fibre ◽  
Laser Welds ◽  
Mode Fibre

scholarly journals Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling

Metals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 683 ◽  
Author(s):  
Sudeepta Mondal ◽  
Daniel Gwynn ◽  
Asok Ray ◽  
Amrita Basak
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
A Priori Estimates ◽  
Process Parameter ◽  
Bayesian Optimization ◽  
Layer By Layer ◽  
Complex Objects ◽  
Melt Pool ◽  
Gp Model

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 a priori estimates of process parameter evolution can be generated to obtain desired melt pool dimensions for the entire build process.

scholarly journals ADAPTIVE SCHEDULING THROUGH MACHINE LEARNING-BASED PROCESS PARAMETER PREDICTION

2019 ◽  
Vol 2019 (04) ◽  
pp. 3060-3066
Author(s):  
M. Frye ◽  
D. Gyulai ◽  
J. Bergmann ◽  
R.H. Schmitt
Keyword(s):  
Machine Learning ◽  
Process Parameter ◽  
Adaptive Scheduling ◽  
Parameter Prediction

scholarly journals Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5063
Author(s):  
Yingyan Chen ◽  
Hongze Wang ◽  
Yi Wu ◽  
Haowei Wang
Keyword(s):  
Machine Learning ◽  
Selective Laser Melting ◽  
Process Parameter ◽  
Supervised Machine Learning ◽  
Laser Melting ◽  
Metallographic Examination ◽  
Free Process ◽  
Input Variables ◽  
Surface Morphologies

Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.

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