Application of Machine Learning Techniques in Additive Manufacturing: A Review

2022 ◽  
pp. 1-24
Author(s):  
Amithkumar Gajakosh ◽  
R. Suresh Kumar ◽  
V. Mohanavel ◽  
Ragavanantham Shanmugam ◽  
Monsuru Ramoni
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Manufacturing Process ◽  
Control Process ◽  
Machine Learning Techniques ◽  
Review Of The Literature ◽  
Learning Techniques ◽  
Manufactured Products ◽  
And Control ◽  
Monitoring Quality

This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.

scholarly journals Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

2020 ◽  
Author(s):  
Riya Tapwal ◽  
Nitin Gupta ◽  
Qin Xin
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Bandwidth Utilization ◽  
Data Caching ◽  
Learning Techniques ◽  
Future Demands ◽  
Iot Devices ◽  
And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

scholarly journals Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

2020 ◽  
Author(s):  
Riya Tapwal ◽  
Nitin Gupta ◽  
Qin Xin
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Bandwidth Utilization ◽  
Data Caching ◽  
Learning Techniques ◽  
Future Demands ◽  
Iot Devices ◽  
And Control

<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

scholarly journals Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1063 ◽  
Author(s):  
Marc-Alexander Lutz ◽  
Stephan Vogt ◽  
Volker Berkhout ◽  
Stefan Faulstich ◽  
Steffen Dienst ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Cost Saving ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Learning Techniques ◽  
Monitoring Quality

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

Application of machine learning techniques for classification of ADHD and control adults

2011 ◽  
Vol 500 ◽  
pp. e31-e32
Author(s):  
Aleksandar Tenev ◽  
Silvana Markovska-Simoska ◽  
Ljupco Kocarev ◽  
Jordan Pop-Jordanov
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
And Control
Sign in / Sign up

Export Citation Format

Share Document