Comparison of Early Stopping Neural Network and Random Forest for In-Situ Quality Prediction in Laser Based Additive Manufacturing

Behnke, Matthew; Guo, Shenghan; Guo, Weihong

doi:10.1016/j.promfg.2021.06.065

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…h. m. c. e.-s.-t.-a.-n.-n.-m. Brownlee, 2018). The early stopping technique stops the training on ML models once the ML model's performance starts dropping and then increasing (Behnke & Guo, 2021).The regularization technique can be applied in multiple ways. Their examples are L1, L2, and Dropout regularization (Alem & Kumar, 2022) 1.4.…”

Section: Early Stopping and Regularizationmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

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Section: Early Stopping and Regularizationmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

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“…A confusion matrix, also known as an error matrix, is used for classification models [84] . These matrices help in evaluating, monitoring and managing models [85] .…”

Section: Model Performance Calculation 141 Confusion Matrix (Popular ...mentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

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“…A deeper comprehension of these field interactions, it is believed, could usher in expansive optimization opportunities for the overarching production cycle, leading towards more streamlined and cost-effective manufacturing [11,12]. Among the tools to achieve this, deep learning, at the forefront of modern artificial intelligence techniques, has been heralded for its capabilities in encapsulating and simulating these multifaceted relations [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cooperative Modelling of Thermal Flow and Stress Fields in Laser Powder Bed Fusion

Huang,

Huang

2023

IJHT

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In the realm of global manufacturing, the proliferation of Laser Powder Bed Fusion (LPBF) technologies has necessitated an in-depth understanding of the dynamics between thermal flow and stress fields during its operative procedures. Interactions between these fields have been observed to induce thermal distortions in components, potentially jeopardizing the structural integrity and operational efficiency of the end products. Insights have been garnered through conventional finite element methods and empirical models, yet these methodologies encounter evident constraints when deciphering highly nonlinear, multiscale systems. This research delves into the employment of deep learning techniques for the cooperative modelling of the aforementioned fields, suggesting an innovative approach to thermal distortion predictions. The outcomes derived from this inquiry are foreseen to unveil novel optimization strategies for laser melting manufacturing methodologies, propelling the evolution of this specialized field.

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Comparison of Early Stopping Neural Network and Random Forest for In-Situ Quality Prediction in Laser Based Additive Manufacturing

Cited by 12 publications

References 18 publications

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Machine learning application in GIS and remote sensing: An overview

Deep Learning-Based Cooperative Modelling of Thermal Flow and Stress Fields in Laser Powder Bed Fusion

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