A lumped parametric analytical model for predicting molten pool temperature and clad geometry in pre-placed powder laser cladding

Bhatnagar, Shrey; Mullick, Suvradip; Muvvala, Gopinath

doi:10.1016/j.ijleo.2021.168015

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…The predicted temperature results showed a high degree of consistency with the actual temperature; Wu et al (2021) used ABAQUS software to establish a composite heat source model and predicted the temperature of the 316 L powder during the cladding process through simulation. The test results showed good agreement with the predicted results; Bhatnagar et al (2021) established an analysis mode based on energy transfer and loss mechanisms to predict the temperature of the molten pool and achieved good predictive results; Shao et al (2021) established 3D temperature and residual stress field models for the process of laser deposition coating using a rectangular laser beam and calculated the temperature evolution during the cladding process; Gao et al (2020) established a singletrack processing prediction model (STPPM) for laser cladding. Using Gaussian heat source, the temperature of the cladding process was predicted based on the birth-death element method, and the prediction error of temperature was about 8.1%.…”

Section: Introductionmentioning

confidence: 77%

Prediction of molten pool temperature in laser solid forming based on PSO-LSTM

Wang

et al. 2023

Front. Mater.

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The temperature of the molten pool in Laser Solid Forming has a direct effect on the dimensional accuracy and mechanical properties of the parts. Accurate prediction of the melt pool temperature is important to ensure the stability of the melt pool temperature and to improve the forming accuracy and quality of the part. In order to accurately predict the melt pool temperature, this study proposes a melt pool temperature prediction method based on particle swarm optimization (PSO) optimised long short-term memory neural network (LSTM). Using IR camera to obtain melt pool temperature data and establish long short-term memory neural network melt pool temperature prediction model based on experimental data. Optimization of the initial learning rate and the number of hidden layer units of the long short-term memory neural network model using the particle swarm optimization algorithm to build a PSO-LSTM model for prediction of melt pool temperature. The results show that the PSO-LSTM prediction model outperforms the long short-term memory neural network and Ridge Regression models in all evaluation indicators and can achieve accurate prediction of melt pool temperature.

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Section: Introductionmentioning

confidence: 77%

Prediction of molten pool temperature in laser solid forming based on PSO-LSTM

Wang

et al. 2023

Front. Mater.

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“…To realize the complex mapping between laser cladding process parameters and cladding layer quality, three methods are commonly used: (i) statistical analysis method, to establish the regression model between the process parameters and the response [10][11][12]; (ii) using finite element analysis methods, the established threedimensional model controls each parameter variable, simulates the experimental process of laser cladding, and predicts the desired experimental results [13][14][15][16]; (iii) application of machine learning (ML) algorithms such as Random forest regression (RFR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718

Yang,

Geng,

Alfano

et al. 2024

Mater. Res. Express

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Laser cladding, an innovative surface modification and coating preparation process, has emerged as a research hotspot in material surface modification and green remanufacturing domains. In the laser cladding process, the interaction between laser light, powder particles, and the substrate results in a complicated mapping connection between process parameters and clad layer quality. This work aims to shed light on this mapping using fast evolving machine learning algorithms. A full factorial experimental design was employed to clad Inconel 718 powder on an A286 substrate comprising 64 groups. Analysis of variance, contour plots, and surface plots were used to explore the effects of laser power, powder feeding rate, and scanning speed on the width, height, and dilution rate of the cladding. The performance of the predictive models was evaluated using the index of merit (IM), which includes mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). By comparing the performance of the models, it was found that the Extra Trees, Random forest regression, Decision tree regression, and XGBoost algorithms exhibited the highest predictive accuracy. Specifically, the Extra Trees algorithm outperformed other machine learning models in predicting the cladding width, while the RFR algorithm excelled in predicting the associated height. The DTR algorithm demonstrated the best performance in predicting the cladding dilution rate. The R2 values for width, height, and dilution rate were found to be 0.949, 0.954, and 0.912, respectively, for these three models.

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“…It is the process of depositing a material called a "cladding material" onto a substrate with the help of the thermal energy provided by a laser beam [3][4][5]. The cladding material could be applied to the substrate by wire feeding, powder injection, or a preset powder [6][7][8]. Among them, the wire feeding system is very suitable for processing with high deposition rates [9].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

et al. 2023

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This review examines the methods used to optimize the process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, as well as intelligent system optimization algorithms such as neural network models, genetic algorithms, support vector machines, the new non-dominance ranking genetic algorithm II, and particle swarm algorithms. The advantages and disadvantages of various laser cladding process optimization methods are analyzed and summarized. Finally, the development trend of optimization methods in the field of laser cladding is summarized and predicted. It is believed that the result would serve as a foundation for future studies on the preparation of high-quality laser cladding coatings.

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A lumped parametric analytical model for predicting molten pool temperature and clad geometry in pre-placed powder laser cladding

Cited by 10 publications

References 15 publications

Prediction of molten pool temperature in laser solid forming based on PSO-LSTM

Prediction of molten pool temperature in laser solid forming based on PSO-LSTM

Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

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