Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

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High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽

10.1016/j.solener.2021.01.046 ◽

2021 ◽

Vol 218 ◽

pp. 48-56

Author(s):

Max Pargmann ◽

Daniel Maldonado Quinto ◽

Peter Schwarzbözl ◽

Robert Pitz-Paal

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

High Accuracy ◽

Data Driven ◽

State Prediction ◽

Accuracy Data

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Inverse design of glass structure with deep graph neural networks

Nature Communications ◽

10.1038/s41467-021-25490-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Qi Wang ◽

Longfei Zhang

Keyword(s):

Neural Networks ◽

Monte Carlo ◽

Configuration Space ◽

Glass Structure ◽

Stable State ◽

Specific Property ◽

Inverse Design ◽

Data Driven ◽

Common Belief ◽

Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

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Reconfigurable data driven virtual machine tool: Geometric error modeling and evaluation

CIRP Journal of Manufacturing Science and Technology ◽

10.1016/j.cirpj.2015.03.001 ◽

2015 ◽

Vol 10 ◽

pp. 120-130 ◽

Cited By ~ 8

Author(s):

Ronnie R. Fesperman ◽

Shawn P. Moylan ◽

Gregory W. Vogl ◽

M. Alkan Donmez

Keyword(s):

Machine Tool ◽

Virtual Machine ◽

Geometric Error ◽

Error Modeling ◽

Data Driven ◽

Virtual Machine Tool

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Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽

10.3390/pr9050737 ◽

2021 ◽

Vol 9 (5) ◽

pp. 737

Author(s):

Chaitanya Sampat ◽

Rohit Ramachandran

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real Time ◽

Data Driven ◽

Wet Granulation ◽

Manufacturing Processes ◽

High Shear ◽

Process Data ◽

Physical Constraints ◽

Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

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Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere

10.1002/essoar.10502543.1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Jonathan A Weyn ◽

Dale Richard Durran ◽

Rich Caruana

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Weather Prediction ◽

Data Driven ◽

Deep Convolutional Neural Networks ◽

Cubed Sphere

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Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

Bulletin of the Seismological Society of America ◽

10.1785/0120200346 ◽

2021 ◽

Author(s):

Daniel Roten ◽

Kim B. Olsen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Site Response ◽

Site Amplification ◽

Data Driven ◽

Vertical Array ◽

The Neural Network ◽

Simplifying Assumptions ◽

The Mean ◽

Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

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