scholarly journals Machine‐learning‐based interatomic potentials for advanced manufacturing

2021 ◽  
Vol 1 (2) ◽  
pp. 159-172
Author(s):  
Wei Yu ◽  
Chaoyue Ji ◽  
Xuhao Wan ◽  
Zhaofu Zhang ◽  
John Robertson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Advanced Manufacturing ◽  
Interatomic Potentials

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Taking materials dynamics to new extremes using machine learning interatomic potentials

2021 ◽  
Author(s):  
Yang Yang ◽  
Long Zhao ◽  
Chen-Xu Han ◽  
Xiang-Dong Ding ◽  
Turab Lookman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Interatomic Potentials

Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy

2021 ◽  
Vol 104 (9) ◽  
Author(s):  
Hongliang Yang ◽  
Yifan Zhu ◽  
Erting Dong ◽  
Yabei Wu ◽  
Jiong Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Adaptive Sampling ◽  
Interatomic Potentials

Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation

2019 ◽  
pp. 253-288 ◽  
Author(s):  
Ivan A. Kruglov ◽  
Pavel E. Dolgirev ◽  
Artem R. Oganov ◽  
Arslan B. Mazitov ◽  
Sergey N. Pozdnyakov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Global Optimization ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Interatomic Potentials

Accessing thermal conductivity of complex compounds by machine learning interatomic potentials

2019 ◽  
Vol 100 (14) ◽  
Author(s):  
Pavel Korotaev ◽  
Ivan Novoselov ◽  
Aleksey Yanilkin ◽  
Alexander Shapeev
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Complex Compounds ◽  
Interatomic Potentials

scholarly journals Performance and Cost Assessment of Machine Learning Interatomic Potentials

2020 ◽  
Vol 124 (4) ◽  
pp. 731-745 ◽  
Author(s):  
Yunxing Zuo ◽  
Chi Chen ◽  
Xiangguo Li ◽  
Zhi Deng ◽  
Yiming Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Assessment ◽  
Interatomic Potentials

scholarly journals Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

2020 ◽  
Vol 10 (19) ◽  
pp. 6856 ◽  
Author(s):  
Leandro Ruiz ◽  
Manuel Torres ◽  
Alejandro Gómez ◽  
Sebastián Díaz ◽  
José M. González ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Technological Development ◽  
Critical Issue ◽  
Machine Learning Techniques ◽  
Manufacturing Processes ◽  
Advanced Manufacturing ◽  
Vision Systems ◽  
Development Capacity

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

scholarly journals Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

2019 ◽  
Vol 123 (12) ◽  
pp. 6941-6957 ◽  
Author(s):  
Henry Chan ◽  
Badri Narayanan ◽  
Mathew J. Cherukara ◽  
Fatih G. Sen ◽  
Kiran Sasikumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
First Principles ◽  
Training Data ◽  
Interatomic Potentials

Automatic Classification for Anti Mixup Events in Advanced Manufacturing System

2015 ◽  
Author(s):  
Marina Paolanti ◽  
Emanuele Frontoni ◽  
Adriano Mancini ◽  
Roberto Pierdicca ◽  
Primo Zingaretti
Keyword(s):  
Machine Learning ◽  
Manufacturing System ◽  
Real Data ◽  
Point Of View ◽  
Embedded Sensors ◽  
Learning Approach ◽  
Advanced Manufacturing ◽  
Advanced Manufacturing System ◽  
Machine Learning Approach ◽  
Simulated Scenario

The mix-up is a phenomenon in which a tablet/capsule gets into a different package. It is an annoying problem because mixing different products in the same package could result dangerous for consumers that take the incorrect product or receive an unintended ingredient. So, the consequences could be very dangerous: overdose, interaction with other medications a consumer may be taking, or an allergic reaction. The manufacturers are not able to guarantee the contents of the packages and so for this reason they are very exposed to the risk in which users rightly want to obtain compensation for possible damages caused by the mix-up. The aim of this work is the identification of mix-up events, through machine learning approach based on data, coming from different embedded systems installed in the manufacturing facilities and from the information system, in order to implement integrated policies for data analysis and sensor fusion that leads to waste and detection of pieces that do not comply. In this field, two types of approaches from the point of view of embedded sensors (optical and NIR vision and interferometry) will be analyzed focusing in particular on data processing and their classification on advanced manufacturing scenarios. Results are presented considering a simulated scenario that uses pre-recorded real data to test, in a preliminary stage, the effectiveness and the novelty of the proposed approach.

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