M31 PAndromeda Cepheid sample observed in four HST bands

Using the M31 PAndromeda Cepheid sample and the HST PHAT data we obtain the largest Cepheid sample in M31 with HST data in four bands. For our analysis we consider three samples: A very homogeneous sample of Cepheids based on the PAndromeda data, the mean magnitude corrected PAndromeda sample and a sample complementing the PAndromeda sample with Cepheids from literature. The latter results in the largest catalog with 522 fundamental mode (FM) Cepheids and 102 first overtone (FO) Cepheids with F160W and F110W data and 559 FM Cepheids and 111 FO Cepheids with F814W and F475W data. The obtained dispersion of the Period-Luminosity relations (PLRs) is very small (e.g. 0.138 mag in the F160W sample I PLR). We find no broken slope in the PLRs when analyzing our entire sample, but we do identify a subsample of Cepheids that causes the broken slope. However, this effect only shows when the number of this Cepheid type makes up a significant fraction of the total sample. We also analyze the sample selection effect on the Hubble constant.Comment: 32 pages, 19 figures, 9 tables, accepted for publication in ApJ, electronic data will be available on CD

Improvement of Machine Learning Models for Time Series Forecasting in Radial-Axial Ring Rolling through Transfer Learning

Due to the increasing computing power and corresponding algorithms, the use of machine learning (ML) in production technology has risen sharply in the age of Industry 4.0. Data availability in particular is fundamental at this point and a prerequisite for the successful implementation of a ML application. If the quantity or quality of data is insufficient for a given problem, techniques such as data augmentation, the use of synthetic data and transfer learning of similar data sets can provide a remedy. In this paper, the concept of transfer learning is applied in the field of radial-axial ring rolling (rarr) and implemented using the example of time series prediction of the outer diameter over the process time. Radial-axial ring rolling is a hot forming process and is used for seamless ring production

Complementary database generation for machine learning in quality prediction of cold ring rolling

Reducing scrap products and unnecessary rework has always been a goal of the manufacturing industry. With the increasing data availability and the developments in the field of artificial intelligence (AI) for industrial applications, machine learning (ML) has been applied to radial-axial ring rolling (RARR) to predict product quality [1]. However, the accuracy of these predictions is currently still limited by the quantity and quality of the data [2]. In order to apply supervised learning to predict part quality and possible scrap parts, there must be plenty of datasets logged for both good and scrap parts. One suitable way to increase the number of datasets is to utilize simulation strategies to generate synthetic datasets. However, in the hot ring rolling field, there is no fast simulation method that can be used to generate a sufficiently large synthetic database of rolled parts with form or process errors. The research on transfer learning between different mills and datasets has offered a new idea of taking a cold ring rolling process as the object of study [2]. Next it will investigate the extent to which the cold ring rolling can be used as a similar process for future transfer of models and results to radial-axial ring rolling. Compared to RARR, the cold ring rolling is a process under room temperature and contains complete radial forming instead of simultaneous forming in the radial and axial directions. The simpler forming mechanism makes it possible to build a semi-analytical model, which takes much less time compared to conventional FEMapproaches under acceptable accuracies. Furthermore, the smaller ring geometry, simplified rolling process and reduced energy consumption mean that in-house experiments can be conducted to verify the quality of the synthetic data based on confidence intervals

Identification Of Investigation Procedures To Predict Work Roll Fatigue For Developing Machine Learning Applications – A Systematic Literature Review

Machine learning approaches present significant opportunities for optimizing existing machines and production systems. Particularly in hot rolling processes, great potential for optimization can be exploited. Radial-axial ring rolling is a crucial process utilized to manufacture seamless rings. However, the failure of the mandrel represents a defect within the ring rolling process that currently cannot be adequately explained. Mandrel failure is unpredictable, occurs without a directly identifiable reason, and can appear several times a week depending on the ring rolling mill and capacity utilization. Broken rolls lead to unscheduled production downtimes, defective rings and can damage other machine parts. Considering the extensive recording of production data in ring rolling, the implementation of machine learning models for the prediction of such roll breaks offers great potential. To present a comprehensive overview of the potential influencing factors which are possibly relevant to the lifetime of mandrels, a systematic literature review (SLR) focusing on work roll wear in hot rolling processes is conducted. Based on the results of the SLR, a first selection of features and the used investigation procedures are presented. The insights can be used for the prediction of mandrel failure with machine learning models in further work

Isogeometric dual mortar methods for computational contact mechanics

International audienceIn recent years, isogeometric analysis (IGA) has received great attention in many fields of computational mechanics research. Especially for computational contact mechanics, an exact and smooth surface representation is highly desirable. As a consequence, many well-known finite e lement m ethods a nd a lgorithms f or c ontact m echanics h ave b een t ransferred t o I GA. I n t he present contribution, the so-called dual mortar method is investigated for both contact mechanics and classical domain decomposition using NURBS basis functions. In contrast to standard mortar methods, the use of dual basis functions for the Lagrange multiplier based on the mathematical concept of biorthogonality enables an easy elimination of the additional Lagrange multiplier degrees of freedom from the global system. This condensed system is smaller in size, and no longer of saddle point type but positive definite. A very simple and commonly used element-wise construction of the dual basis functions is directly transferred to the IGA case. The resulting Lagrange multiplier interpolation satisfies discrete inf–sup stability and biorthogonality, however, the reproduction order is limited to one. In the domain decomposition case, this results in a limitation of the spatial convergence order to O(h 3 /2) in the energy norm, whereas for unilateral contact, due to the lower regularity of the solution, optimal convergence rates are still met. Numerical examples are presented that illustrate these theoretical considerations on convergence rates and compare the newly developed isogeometric dual mortar contact formulation with its standard mortar counterpart as well as classical finite elements based on first and second order Lagrange polynomials

Transfer Learning Approaches In The Domain Of Radial-Axial Ring Rolling For Machine Learning Applications

Due to increased data accessibility, data-centric approaches, such as machine learning, are getting more represented in the forming industry to improve resource efficiency and to optimise processes. Prior research shows, that a classification of the roundness of shaped rings, using machine learning algorithms, is applicable to radial-axial ring rolling. The accuracy of these predictions nowadays is still limited by the amount and quality of the data. Therefore, this paper will focus on how to make the best use of the limited amount of data, using transfer learning approaches. Since acquiring data for homogenised databases is time, energy and resource consuming, logged data gathered by the industry is often used in research. This paper takes both, industrial data from thyssenkrupp rothe erde Germany GmbH and a smaller dataset of an inhouse research plant, into account. Additionally, a synthetic dataset, created by generative adversarial networks, is considered. To accomplish an improvement of machine learning predictions using accessible data, three transfer learning approaches are investigated in order to extend existing models: (I) transferring from a radial-axial ring rolling mill to a different mill containing less available data with a ratio of 20:1, (II) learning from unlabelled data using an autoencoder and (III) training on synthetic data. The obtained improvements are further evaluated. Based on these results, future possible investigations are elaborated, in particular the consideration of transfer learning from the less complex cold ring rolling process

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita)

Properties of M31. II: A Cepheid disk sample derived from the first year of PS1 PAndromeda data

We present a sample of Cepheid variable stars towards M31 based on the first year of regular M31 observations of the PS1 survey in the r_P1 and i_P1 filters. We describe the selection procedure for Cepheid variable stars from the overall variable source sample and develop an automatic classification scheme using Fourier decomposition and the location of the instability strip. We find 1440 fundamental mode (classical \delta) Cep stars, 126 Cepheids in the first overtone mode, and 147 belonging to the Population II types. 296 Cepheids could not be assigned to one of these classes and 354 Cepheids were found in other surveys. These 2009 Cepheids constitute the largest Cepheid sample in M31 known so far and the full catalog is presented in this paper. We briefly describe the properties of our sample in its spatial distribution throughout the M31 galaxy, in its age properties, and we derive an apparent period-luminosity relation (PLR) in our two bands. The Population I Cepheids nicely follow the dust pattern of the M31 disk, whereas the 147 Type II Cepheids are distributed throughout the halo of M31. We outline the time evolution of the star formation in the major ring found previously and find an age gradient. A comparison of our PLR to previous results indicates a curvature term in the PLR