Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks

Narayana, P.L.; Kim, Jae Hyeok; Lee, Jae-Hyun; Choi, Sunwoong; Lee, Sang‐Won; Park, Chan Hee; Yeom, Jong‐Taek; Reddy, Nagireddy Gari Subba; Hong, Jae‐Keun

doi:10.1007/s00170-021-07115-1

Cited by 17 publications

(4 citation statements)

References 39 publications

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“…Mondal et al used a hybrid data-driven method to predict the melt pool dimensions via a low-cost Gaussian process (GP) surrogate model developed from an experimentally validated analytical threedimensional model [175] . Narayana et al used an ANN to generate process maps for the optimization of build height and density [176] . Nguyen et al setup an ANN to minimize the porosity Ti6Al4V [177] .…”

Section: Surrogate Modellingmentioning

confidence: 99%

“…The covariance between each distinct parameter is therefore reflected in the spatial distance between any two data points and is typically defined by a kernel function. GP with metropolis-hastings MCMC algorithm PBF SS316L Melt pool dimensions [168] GP model with Bayesian estimation PBF 17-4PHSS Porosity [179] GP with regression tree SLM SS316L Melt pool dimensions [180] GP PBF SS316L and 17-4PHSS Porosity [181] Deep learning with ANN SLM Ti6Al4V Porosity [177] ANN DED Ti6Al4V Porosity and build height [176] The advantage of the GP model is its relatively easy implementation and ability to smooth out noisy environments with a limited number of training data. This makes GP an "efficient and effective regression tool with few input features and a small data set" [172] .…”

Section: Surrogate Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Process parameter optimization of metal additive manufacturing: a review and outlook

Chia¹,

Wu²,

Wang³

et al. 2022

J Mater Inf

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The selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it directly influences the defect formation and microstructure of the printed part. Over the past decade, research efforts have been devoted to identifying "optimal" processing regimes for different materials to achieve defect-free manufacturing, which mostly involve costly trial-and-error experiments and computationally expensive mechanistic simulations. Hence, it is apropos to critically review the methods used to achieve the optimal process parameters in AM. This work seeks to provide a structured analysis of current methodologies and discuss systematic approaches toward general optimization work in AM and the process parameter optimization of new AM alloys. A brief review of process-induced defects due to process parameter selection is given and the current methods for identifying "optimal processing windows" are summarized. Research works are analyzed under a standard optimization framework, including the design of experiments and characterization, modelling and optimization algorithms. The research gaps that preclude multi-objective optimization in AM are identified and future directions toward optimization work in AM are discussed. With growing capabilities in AM, we should reconsider what the definition of the "optimal processing region".

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Section: Surrogate Modellingmentioning

confidence: 99%

Section: Surrogate Modellingmentioning

confidence: 99%

Process parameter optimization of metal additive manufacturing: a review and outlook

Chia¹,

Wu²,

Wang³

et al. 2022

J Mater Inf

View full text Add to dashboard Cite

show abstract

“…The bulk of these works focus on controlling aspects of the melt pool and resulting tracks, including: track width and height [34][35][36], melt pool geometry [37], and thermal history of the melt pool [38]. More recent works have studied the properties of the final part: Narayana et al [39] built a NN to predict built part height and density from laser power, scan speed, powder feed rate, and layer thickness. It was found that these parameters were all of significant importance for density whereas scan speed and feed rate had the largest effect on build height.…”

Section: Parameter Optimisationmentioning

confidence: 99%

Machine Learning for Additive Manufacturing

2021

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Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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“…In recent years, data-driven models are becoming increasingly popular for performing such tasks as they are less computationally expensive and do not require that assumptions be made about the underlying physical process [20]. Hereby, deterministic models such as artificial neural networks [21][22][23][24][25][26][27][28] or Regression trees (RT) [24,29] are for example applied to predict the track geometry as a function of the process parameters in DED. However, deterministic models cannot provide uncertainty quantification (UQ), which is crucial for reliable additive manufacturing due to the various sources of uncertainties in additive manufacturing [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition

Hermann,

Michalowski,

Brünnette

et al. 2023

Materials

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Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.

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Optimization of process parameters for direct energy deposited Ti-6Al-4V alloy using neural networks

Cited by 17 publications

References 39 publications

Process parameter optimization of metal additive manufacturing: a review and outlook

Process parameter optimization of metal additive manufacturing: a review and outlook

Machine Learning for Additive Manufacturing

Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition

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