Real-Time Prediction of Temperature Distribution in Additive Manufacturing Processes Based on Machine Learning

2021 ◽  
Author(s):  
Sharareh Bayat ◽  
Mohammad Mohseni ◽  
Delaram Behnami ◽  
Purang Abolmaesumi
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Real Time ◽  
Manufacturing Processes ◽  
Time Prediction

Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning

2019 ◽  
Vol 34 (5) ◽  
pp. 1437-1451 ◽  
Author(s):  
Amy McGovern ◽  
Christopher D. Karstens ◽  
Travis Smith ◽  
Ryan Lagerquist
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Weather Prediction ◽  
Warning System ◽  
Objective Evaluation ◽  
Boosted Regression Trees ◽  
Time Prediction ◽  
Operational Testing ◽  
Trade Offs ◽  
Naturalistic Environment

Abstract Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.

scholarly journals A Machine Learning Approach to Predict Hypotensive Events in ICU Settings

2019 ◽  
Author(s):  
Mina Chookhachizadeh Moghadam ◽  
Ehsan Masoumi ◽  
Nader Bagherzadeh ◽  
Davinder Ramsingh ◽  
Guann-Pyng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Physiological Status ◽  
Time Prediction ◽  
Evaluation Approach ◽  
High Positive Predictive Value ◽  
Data Points

AbstractPurposePredicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge due to the dynamic changes in patients’ physiological status under the drug administration which is limiting the amount of useful data available for the algorithm.MethodsTo mimic real-time monitoring, we developed a machine learning algorithm that uses most of the available data points from patients’ record to train and test the algorithm. The algorithm predicts hypotension up to 30 minutes in advance based on only 5 minutes of patient’s physiological history. A novel evaluation method is proposed to assess the algorithm performance as a function of time at every timestamp within 30 minutes prior to hypotension. This evaluation approach provides statistical tools to find the best possible prediction window.ResultsDuring 181,000 minutes of monitoring of about 400 patients, the algorithm demonstrated 94% accuracy, 85% sensitivity and 96% specificity in predicting hypotension within 30 minutes of the events. A high PPV of 81% obtained and the algorithm predicted 80% of the events 25 minutes prior to their onsets. It was shown that choosing a classification threshold that maximizes the F1 score during the training phase contributes to a high PPV and sensitivity.ConclusionThis study reveals the promising potential of the machine learning algorithms in real-time prediction of hypotensive events in ICU setting based on short-term physiological history.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal

2021 ◽  
Author(s):  
Bochun Wang ◽  
Xuanyu Yi ◽  
Jiandong Gao ◽  
Yanru Li ◽  
Wen Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Sound Signal ◽  
Time Prediction ◽  
Respiratory Events

Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes

2022 ◽  
pp. 75-95
Author(s):  
Ranjit Barua ◽  
Sudipto Datta ◽  
Pallab Datta ◽  
Amit Roychowdhury
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Quality Evaluation ◽  
Mechanical Characteristics ◽  
Manufacturing Processes ◽  
Superior Part ◽  
Machine Learning Methods ◽  
Excellent Control ◽  
Part Feature ◽  
Counting Model

Additive manufacturing (AM) make simpler the manufacturing of difficult geometric structures. Its possibility has quickly prolonged from the manufacture of pre-fabrication conception replicas to the making of finish practice portions driving the essential for superior part feature guarantee in the additively fabricated products. Machine learning (ML) is one of the encouraging methods that can be practiced to succeed in this aim. A modern study in this arena contains the procedure of managed and unconfirmed ML algorithms for excellent control and forecast of mechanical characteristics of AM products. This chapter describes the development of applying machine learning (ML) to numerous aspects of the additive manufacturing whole chain, counting model design, and quality evaluation. Present challenges in applying machine learning (ML) to additive manufacturing and possible solutions for these problems are then defined. Upcoming trends are planned in order to deliver a general discussion of this additive manufacturing area.

Real-time prediction of propulsion motor overheating using machine learning

2021 ◽  
pp. 1-9
Author(s):  
K. H. Hellton ◽  
M. Tveten ◽  
M. Stakkeland ◽  
S. Engebretsen ◽  
O. Haug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Prediction
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