Optimized Robotic WAAM

2022 ◽  
pp. 114-137
Author(s):  
Aya Abd Alla Ramadan ◽  
Sherif Elatriby ◽  
Abd El Ghany ◽  
Azza Fathalla Barakat
Keyword(s):  
Base Alloy ◽  
Network Models ◽  
Optimization Techniques ◽  
Tension Stress ◽  
Neural Network Models ◽  
Cold Metal Transfer ◽  
The Third ◽  
Third Stage ◽  
Cold Metal ◽  
Phd Thesis

This chapter summarizes a PhD thesis introducing a methodology for optimizing robotic MIG (metal inert gas) to perform WAAM (wire and arc additive manufacturing) without using machines equipped with CMT (cold metal transfer) technology. It tries to find the optimal MIG parameters to make WAAM using a welding robot feasible production technique capable of making functional products with proper mechanical properties. Some experiments were performed first to collect data. Then NN (neural network) models were created to simulate the MIG process. Then different optimization techniques were used to find the optimal parameters to be used for deposition. These results were practically tested, and the best one was selected to be used in the third stage. In the third stage, a block of metal was deposited. Then samples were cut from deposited blocks in two directions and tested for tension stress. These samples were successful. They showed behavior close to base alloy.

The Microstructure of Weld Overlay Ni-Base Alloy Deposited on Carbon Steel by CMT Method

2015 ◽  
Vol 231 ◽  
pp. 119-124 ◽  
Author(s):  
Monika Solecka ◽  
Paweł Petrzak ◽  
Agnieszka Radziszewska
Keyword(s):  
Carbon Steel ◽  
Base Alloy ◽  
Dendritic Structure ◽  
Inconel 625 ◽  
Cold Metal Transfer ◽  
Inconel 625 Alloy ◽  
Second Phases ◽  
Transfer Method ◽  
Cold Metal ◽  
Weld Overlays

Ni-base alloys, like Inconel 625, exhibit a high temperature corrosion and oxidation resistance. For this reason, these alloys are typically used as a one of the most important coating material and can be applied in a different environments and elements of devices having various applications. In this work, Inconel 625 was deposited onto a carbon steel P235GH by Cold Metal Transfer method. Due to the segregation of Ni, Cr, Nb and Mo elements the Inconel 625 weld overlays cladded on boiler pipes P235GH obtained the dendritic structure, with the formation of a second phases at the end of solidification. The presence of γ (with high dislocation density), the Laves and (Nb,Ti)C phases was revealed by means of TEM examinations. The multipoint EDS analysis confirmed the presence of low Fe concentration in the Inconel 625 alloy coatings. The concentration profiles of Ni, Cr, Mo and Nb performed across the dendritic structure showed segregation of these elements.

Melodic Expectation in Finnish Spiritual Folk Hymns: Convergence of Statistical, Behavioral, and Computational Approaches

1999 ◽  
Vol 17 (2) ◽  
pp. 151-195 ◽  
Author(s):  
Carol L. Krumhansl ◽  
Jukka Louhivuori ◽  
Petri Toiviainen ◽  
Topi Järvinen ◽  
Tuomas Eerola
Keyword(s):  
Network Models ◽  
Folk Songs ◽  
The Self ◽  
Behavioral Experiment ◽  
General Distribution ◽  
Style Analysis ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
The Third ◽  
Melodic Expectation

This study of Finnish spiritual folk hymns combined three approaches to understanding melodic expectation. The first approach was a statistical style analysis of a representative corpus of 18 hymns, which determined the relative frequencies of tone onsets and two- and three-tone transitions. The second approach was a behavioral experiment in which listeners, either familiar (experts) or unfamiliar (nonexperts) with the hymns, made judgments about melodic continuations. The third approach simulated melodic expectation with neural network models of the self-organizing map (SOM) type (Kohonen, 1997). One model was trained on a corpus of Finnish folk songs and Lutheran hymns (Finnish SOM), while another was trained with the hymn contexts used in the experiment with the correct continuation tone (Hymn SOM). The three approaches converged on the following conclusions: (1) Listeners appear to be sensitive to the distributions of tones and tone transitions in music, (2) The nonexperts' responses more strongly reflected the general distribution of tones, whereas the experts' responses more strongly reflected the tone transitions and the correct continuations, (3) The SOMs produced results similar to listeners and also appeared sensitive to the distributions of tones and tone transitions, (4) The Hymn SOM correlated more strongly with the experts' judgments than the Finnish SOM, and (5) the principles of the implication-realization model (Narmour, 1990) were weighted similarly by the behavioral data and the Hymn SOM. /// Tässä suomalaisia hengellisiä kansansävelmiä käsittelevässä tutkimuksessa pyrittiin selvittämään melodisia odotuksia kolmen tutkimusmenetelmän avulla. Ensimmäinen menetelmä oli kyseistä tyyliä edustavien 18 sävelmän tilastollinen analyysi, jossa määritelteltiin sävelkorkeuksien sekä kahden ja kolmen sävelen siirtymien tilastolliset jakaumat. Toinen menetelmä oli behavioraalinen koe, jossa kuulijat arvioivat sävelmien jatkoja. Kuulijat jakaantuivat kahteen ryhmään: sävelmät tunteviin (asiantuntijoihin) ja sävelmiä tuntemattomiin (ei-asiantuntijoihin). Kolmannessa menetelmässä simuloitiin melodisia odotuksia itsejärjestäytyvään karttaan (Kohonen, 1997) perustuvalla keinotekoisella hermoverkkomallilla. Ensimmäiselle mallille opetettiin joukko suomalaisia kansanlauluja ja luterilaisia virsiä (suomalainen verkko), toiselle kokeessa käytettyjä hengellisiä kansansävelmiä (hengellinen verkko). Käytetyt menetelmät tuottivat yhteneviä tuloksia ja antoivat aihetta seuraaviin johtopäätöksiin: (1) kuulijat näyttävät olevan vastaanottavaisia musiikin säveljakaumille ja sävelsiirtymille, (2) ei-asiantuntijoiden vastaukset noudattivat enemmän sävelten yleistä jakaumaa, kun taas asiantuntijoiden vastaukset heijastivat enemmän sävelsiirtymiä ja sävelmien oikeita jatkoja, (3) hermoverkot tuottivat tuloksia, jotka olivat samankaltaisia kuulijoiden arvioiden kanssa ja jotka noudattivat sävelten ja sävelsiirtymien jakaumia, (4) hengellisen verkon tulokset korreloivat suomalaisen verkon tuloksia voimakkaammin asiantuntijoiden arvioiden kanssa, ja (5) behavioraaliset tulokset ja hengellinen verkko painottavat implikaatio-realisaatio-mallin (Narmour, 1990) periaatteita samalla tavalla.

scholarly journals Exploring the Sensitivity of Recurrent Neural Network Models for Forecasting Land Cover Change

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 282
Author(s):  
Alysha van Duynhoven ◽  
Suzana Dragićević
Keyword(s):  
Land Cover ◽  
Short Term Memory ◽  
Network Models ◽  
Optimization Techniques ◽  
Neural Network Models ◽  
Method Performance ◽  
Timeseries Analysis ◽  
Method Optimization ◽  
Long Short Term Memory ◽  
Real World Datasets

Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs demonstrated favourable performance for Land Cover (LC) change analyses, few studies have explored or quantified the geospatial data characteristics required to utilize this method. Likewise, many studies utilize overall measures of accuracy rather than metrics accounting for the slow or sparse changes of LC that are typically observed. Therefore, the main objective of this study is to evaluate the performance of LSTM models for forecasting LC changes by conducting a sensitivity analysis involving hypothetical and real-world datasets. The intent of this assessment is to explore the implications of varying temporal resolutions and LC classes. Additionally, changing these input data characteristics impacts the number of timesteps and LC change rates provided to the respective models. Kappa variants are selected to explore the capacity of LSTM models for forecasting transitions or persistence of LC. Results demonstrate the adverse effects of coarser temporal resolutions and high LC class cardinality on method performance, despite method optimization techniques applied. This study suggests various characteristics of geospatial datasets that should be present before considering LSTM methods for LC change forecasting.

On the Heuristics of Discrete Optimization

2001 ◽  
Author(s):  
Mohamed H. Gadallah
Keyword(s):  
Design Of Experiments ◽  
Orthogonal Array ◽  
Statistical Design ◽  
Nonlinear Problems ◽  
Optimization Techniques ◽  
Statistical Design Of Experiments ◽  
The Third ◽  
Third Stage ◽  
Optimum Solution ◽  
Two Stages

Abstract The importance of developing optimization techniques capable of tackling realistic engineering problems cannot be underestimated. In this study, a modification to the usual Branch and Bound algorithm is presented. This modification deals with the high dimensionality of linear integer problems in three steps. The first step, statistical design of experiments is used to detect the most and least important variables. The least important variables are assigned the maximum or minimum value according to the nature of original problem. The second step, the remaining variables are assigned to an orthogonal array of proper size. The complexity of our algorithm becomes n (m – o) instead of the usual n (m). Results encouraged the extension of the developed algorithm to include continuous nonlinear problems. The nonlinear continuous problem uses the integer linear optimum solution resulted from the two stages as input for the third stage. The third stage assigns increments for the variables in a suitably chosen orthogonal array. This array is used to enumerate the optimum solution with the variations. These modifications enrich the subject of optimization through combination of search techniques and orthogonal statistical design of experiments and accommodate the problem of size. Several test cases are used to verify the performance of these modifications and conclusions are drawn.

Generation of synthetic dielectric dispersion logs in organic-rich shale formations using neural-network models

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. D117-D129 ◽  
Author(s):  
Jiabo He ◽  
Siddharth Misra
Keyword(s):  
Neural Network ◽  
Reservoir Characterization ◽  
Network Models ◽  
Dielectric Dispersion ◽  
Ann Model ◽  
Neural Network Models ◽  
Shale Formations ◽  
The Third ◽  
Predictive Method ◽  
Predictive Methods

Dielectric dispersion (DD) logs acquired in subsurface geologic formations generally are composed of conductivity ([Formula: see text]) and relative permittivity ([Formula: see text]) measurements at four discrete frequencies in the range of 10 MHz to 1 GHz. Acquisition of DD logs in subsurface formations is operationally challenging, and it requires a hard-to-deploy infrastructure. We developed three supervised neural-network-based predictive methods to process conventional, easy-to-acquire subsurface logs for generating the eight DD logs acquired at four frequencies. These predictive methods will improve reservoir characterization in the absence of a DD logging tool. The predictive methods are tested in three wells intersecting organic-rich shale formations of the Permian Basin and the Bakken Shale. The first method predicts the eight dispersion logs simultaneously using a single artificial neural network (ANN) model, whereas the second method simultaneously predicts the four conductivity dispersion logs using one ANN model, followed by simultaneous prediction of four permittivity dispersion logs using a second ANN model. The third method sequentially predicts the eight dispersion logs, one at a time using eight sequential ANN models, based on a predetermined ranking of the prediction accuracy for each of the eight DD logs when simultaneously generated. Considering that the conventional and DD logs are recorded more than 10,000 ft deep in the subsurface using logging tools that are run at different times in rugose boreholes for sensing the near-wellbore geologic formation, the data used in this predictive work is prone to noise and biases that tend to adversely affect the prediction performances of the proposed methods. In terms of normalized root-mean square error (Nrms error), the prediction performances of the second predictive method are 8.5% worse and 6.2% better for the conductivity and permittivity dispersion logs, respectively, as compared with those of the first predictive method. The third method has best prediction performance for permittivity dispersion logs, which is 0.089 in terms of the Nrms error.

Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks

2016 ◽  
Vol 26 (05) ◽  
pp. 1650040 ◽  
Author(s):  
Francisco Javier Ropero Peláez ◽  
Mariana Antonia Aguiar-Furucho ◽  
Diego Andina
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Network Models ◽  
Activation Function ◽  
Inhibitory Interneurons ◽  
Neural Network Models ◽  
The Third ◽  
Initial Network ◽  
Sensory Cortices ◽  
Intrinsic Plasticity

In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a complex associative koniocortex network. In the initial network, intrinsic and synaptic plasticity govern the shifting of the activation function, and the modification of synaptic weights, respectively. In this first version, competition is forced, so that the most activated neuron is arbitrarily set to one and the others to zero, while in the second, competition occurs naturally due to inhibition between second layer neurons. In the third version of the network, whose architecture is similar to the koniocortex, competition also occurs naturally owing to the interplay between inhibitory interneurons and synaptic and intrinsic plasticity. A more complex associative neural network was developed based on this basic koniocortex-like neural network, capable of dealing with incomplete patterns and ideally suited to operating similarly to a learning vector quantization network. We also discuss the biological plausibility of the networks and their role in a more complex thalamocortical model.

The Coherence of Mary Barton: Romance, Realism, and Utopia

2015 ◽  
Vol 5 (3) ◽  
pp. 185-200
Author(s):  
Robert Z. Birdwell
Keyword(s):  
Thomas Carlyle ◽  
The Novel ◽  
Second Stage ◽  
The Third ◽  
Third Stage ◽  
Three Stages ◽  
Mode Of Production

Critics have argued that Elizabeth Gaskell's first novel, Mary Barton (1848), is split by a conflict between the modes of realism and romance. But the conflict does not render the novel incoherent, because Gaskell surpasses both modes through a utopian narrative that breaks with the conflict of form and gives coherence to the whole novel. Gaskell not only depicts what Thomas Carlyle called the ‘Condition of England’ in her work but also develops, through three stages, the utopia that will redeem this condition. The first stage is romantic nostalgia, a backward glance at Eden from the countryside surrounding Manchester. The second stage occurs in Manchester, as Gaskell mixes romance with a realistic mode, tracing a utopian drive toward death. The third stage is the utopian break with romantic and realistic accounts of the Condition of England and with the inadequate preceding conceptions of utopia. This third stage transforms narrative modes and figures a new mode of production.

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