Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

Fotovvati, Behzad; Balasubramanian, Madhusudhanan; Asadi, Ebrahim

doi:10.3390/coatings10111104

Cited by 44 publications

(13 citation statements)

References 59 publications

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“…PBF technology is a common technique in 3D printing, which can be divided into two types according to different heat sources [50][51][52]. Additive manufacturing technologies with laser as heat source include SLS, SLM, etc., and with electron beam as heat source include SEBM, EBDM, etc.…”

Section: Pure Copper Additive Manufacture Methodsmentioning

confidence: 99%

A Review on Additive Manufacturing of Pure Copper

Jiang

Zhang

et al. 2021

Coatings

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With the development of the aerospace and automotive industries, high heat exchange efficiency is a challenge facing the development of various industries. Pure copper has excellent mechanical and physical properties, especially high thermal conductivity and electrical conductivity. These excellent properties make pure copper the material of choice for the manufacture of heat exchangers and other electrical components. However, the traditional processing method is difficult to achieve the production of pure copper complex parts, so the production of pure copper parts through additive manufacturing has become a problem that must be overcome in industrial development. In this article, we not only reviewed the current status of research on the structural design and preparation of complex pure copper parts by researchers using selective laser melting (SLM), selective electron beam melting (SEBM) and binder jetting (BJ) in recent years, but also reviewed the forming, physical properties and mechanical aspects of pure copper parts prepared by different additive manufacturing methods. Finally, the development trend of additive manufacturing of pure copper parts is also prospected.

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Section: Pure Copper Additive Manufacture Methodsmentioning

confidence: 99%

A Review on Additive Manufacturing of Pure Copper

Jiang

Zhang

et al. 2021

Coatings

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“…Each layer consists of a different number of neurons. The number of neurons in the input layer depends on the number of features, while the number of neurons in the output layer depends on the number of target values [65,66]. Therefore, changing the number of features and target values in the dataset will affect the number of neurons in the input and output layers.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Moeini

Shojaeizadeh

Geza

2021

Water

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Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

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“…Finally, the Artificial Neural Network (ANN) is one of the most well-known and popular methods, capable of capturing nonlinear patterns in functional relationship features and targets. It has been applied to many ML problems in different areas [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Methods Application in Temperature Prediction

Azari¹,

Hassan²,

Pierce³

et al. 2022

CRPASE

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Machine Learning (ML) techniques for time series prediction are becoming increasingly accurate and helpful, particularly in considering climate change. As more methods are developed, it follows that differentiating between them is becoming increasingly more important as well. This work took a local temperature time series as a dependent variable and a collection of relevant climatology time series as independent variables and applied leading Machine Learning methods to them. The six methods tested included four simple models: Linear Regression (L.R.), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), In addition of two ensemble model methods: Random Forest (R.F.) and Adaptive Boosting (AdB). Results compared all the method's training and predictive performances to evaluate the method's overall effectiveness in forecasting the average daily temperature value. Actual data was used to train each of the mentioned ML methods, and then they were used to predict the future temperature in the study area. The analysis revealed that out of the six methods tested, the Artificial Neural Network outperformed the others in both training and prediction of temperature values in the Memphis, TN climate.

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Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

Cited by 44 publications

References 59 publications

A Review on Additive Manufacturing of Pure Copper

A Review on Additive Manufacturing of Pure Copper

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Evaluation of Machine Learning Methods Application in Temperature Prediction

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