Butterfly Specimens Research Articles

Characterization of forming limits at fracture for aluminum alloy 6K21-T4 sheets in non-linear strain paths using a biaxial tension/shear loading test

A good knowledge of forming limits at fracture (FLF) for aluminum sheets exhibiting poor formability at room temperature in various non-linear strain paths (N-LSPs) is essential for fully exploiting their forming ability and avoiding their fracture failure. However, the FLF over a wide range of strain states in N-LSPs with pre-strain paths between uniaxial tension and simple shear cannot be determined through the existing two-stage loading test methods. To solve the problem and fill the gap, a new two-stage loading test method employing an optimized butterfly specimen is proposed in this paper, in which strain path changes are controlled by changing biaxial loading conditions acting on the specimen. To validate the capability of the new test method to characterize the FLF in the N-LSPs and investigate the effect of the loading path changes on aluminum 6K21-T4 sheet formability at fracture, different proportional and non-proportional loading experiments using the butterfly specimen obtained from the aluminum sheets are performed. Experimental results indicate that the forming limit strains at fracture over a wide scope of strain states in the N-LSPs can be effectively determined by utilizing the proposed method, thus filling the gap mentioned above. Additionally, four newly developed ductile fracture criteria (a new model proposed in this paper, Hu−Chen 2017, Lou−Huh 2012 and MMC3) together with a non-linear damage accumulation rule (N-LDAR) are employed to forecast the FLF for the 6K21-T4 sheets in the N-LSPs. The results show that both the proposed new model with the N-LDAR and the Hu−Chen model along with the N-LDAR can provide the highest prediction accuracy for the FLF in the N-LSPs. However, the prediction accuracy of the proposed model is much higher that of the Hu−Chen 2017 model for aluminum alloy 5083-O sheets.

Butterfly fauna in and around Kyoto City in the early Showa period inferred from the butterfly specimens collected by Dr Tadachika Minoura

Butterfly fauna in and around Kyoto City in the early Showa period inferred from the butterfly specimens collected by Dr Tadachika Minoura

Fine-grained visual categorization of butterfly specimens at sub-species level via a convolutional neural network with skip-connections

Fine-grained visual categorization often suffers from the challenges that the subordinate categories within an entry-level category can only be distinguished by subtler discriminations. It demands an effective algorithm to learn a multiple-perspective density distribution for precious categorization. To shape a coarse-to-fine object perception, a hierarchical convolutional neural network (CNN) denoting the skip-connections convolutional neural network (S-CCNN) was proposed, focusing on a butterfly domain at sub-species level due to the fine-grained structure of the category taxonomy. Specifically, based on the serial backbone, three skip-connections with Grating layers are established to link the earlier layers and upper layers of network, and integrated with DropConnect, exponential linear unit (ELU), and local response normalization (LRN) to alleviate over-fitting and vanishing gradient. Benefitting from the long-span skip-connections, coarse-grained context with orientation descriptions and finer-grained context with semantic representations can be both took into consideration, and they are jointly incorporated into framework. S-CCNN can achieve the cross-utilization of object-level and part-level representations, and its rationality were evidenced with both theory and practice. For effectness verification, a total of 24,836 lab-made images of butterfly specimens spanning 56 sub-species are utilized as testing samples, while 173,852 augmented images are employed for model training. S-CCNN delivers a consistent and significant boost in performance, i.e., validation accuracy achieved 94.17% and testing accuracy achieved 93.36%, which outperformed state-of-the-arts. S-CCNN can easily relish accuracy gains from skip-connections in fine-grained visual categorization of butterfly sub-species, without any bells and whistles.

The Practice of Deep Learning Methods in Biodiversity Information Collection

Deep learning is one machine learning method based on the layers used in artificial neural networks. The breakthrough of deep learning in classification led to its rapid application in speech recognition, natural language understanding, and image processing and recognition. In the field of biodiversity informatics, deep learning efforts are being applied in rapid species identification and counts of individuals identified based on image, audio, video, and other data types. However, deep learning methods hold great potential for application in all aspects of biodiversity informatics. We present a case study illustrating how to increase data collection quality and efficiency using well-established technology such as optical character recognition (OCR) and some image classification. Our goal is to image data from the scanned documents of various butterfly atlases, add species, specimens, collections, photographs and other relevant information, and build a database of butterfly images. Information collection involves image annotation and text-based descritpion input. Although the work of image annotation is simple, this process can be accelerated by deep learning-based target segmentation to make the selection process easier, such as changing box select to a double click. The process of information collection is complicated, involving input of species names, specimen collection, specimen description, and other information. Generally, there are many images in atlases, the text layout is rather messy, and overall OCR success is poor. Therefore, the measures we take are as follows: Step A: select the screenshot of the text and then call the OCR interface to generate the text material; Step B: proceed with NLP- (natural language processing) related processing; Step C: perform manual operations on the results, and introduce the NLP function again to this process; Step D: submit the result. The deep learning applications we integrated in our client tool include: target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. Some simple machine learning methods such as k-nearest neighbor can be used to automatically determine gender, pose, and so on. While complex information such as collection place, time, and collector can be analyzed by deep learning-based NLP methods in the future. In our infomation collection process, ten fields are required to submit one single record. Of those, about 4-5 input fields can be dealt with the AI-assistant. It can thus be seen from the above process that deep learning has reduced the workload of manual information annotation by at least 30%. With improvements in accuracy, the era of using automatic information extraction robots to replace manual information annotation and collection is just around the corner.

Characterizing forming limits at fracture for aluminum 6K21-T4 sheets using an improved biaxial tension/shear loading test

The main objective of this paper is to provide a reliable experimental methodology that utilizes biaxial tension and shear loading combined with appropriate butterfly specimens for characterizing forming limits at fracture (FLF) for aluminum alloy 6K21-T4 sheets over a wide range of strain paths. To realize the goal, the performance of an existing butterfly specimen for fracture testing of the aluminum sheet is first experimentally and numerically studied. Experimental and numerical results show that premature edge fracture may easily occur on the aluminum sheet specimen under combined tension and shear loading, which would lead to a high degree of uncertainty for the measured limit strains at fracture. To solve the problem and provide a reliable testing method, optimized butterfly specimens are proposed, and their performance for fracture testing of the aluminum sheet is numerically evaluated. Subsequently, verification experiments on the optimized butterfly specimens extracted from the aluminum sheet are conducted under seven different combined tension and shear loading conditions and a strain-rate-based time-dependent method combined with the image inspection of specimen surface is presented to determine the onset of fracture. Experimental results show that the forming limit strains at fracture in arbitrary strain paths between simple shear and plane strain can be accurately obtained by using the proposed experimental methodology. In addition, three newly proposed ductile fracture criteria (Lou−Huh 2012, MMC3, and Hu−Chen) are employed to predict the FLF for the aluminum 6K21-T4 sheet over a wide range of strain paths. It is found that the Hu−Chen model exhibits the best prediction capability for the FLF.

Analisis Dimorfisme Kupu-Kupu Sayap Burung (Ornithoptera sp.) Endemik Papua

Ornitophera sp. is one of the groups of butterflies endemic in Papua and West Papua provinces. The significant morphological differences between male and female butterflies (dimorphism) are in the shape, size and color of the wings. Morphological characteristics are important sources of information. Based on the results of specimen collections at the Papua Insect Collection Laboratory (KSP) Jayapura, there are several species from various locations in Papua. The variation in dimorphism is likely to occur in this species which gives rise to new sub-species. The purpose of this study was to record the dimorphism of the endemic Bird Wing of Papua which had been stored in the Papua Insect Collection Laboratory (KSP) Jayapura. The method used is a direct measurement of the morphology of bird wing butterfly specimens stored in the KSP Laboratory, Cenderawasih University. Morphometric data were analyzed using the SPSS 20 and Multi Variate Statistical Package (MVSP 3.1) programs. The results obtained were 7 Ornithoptera species stored in the KSP Jayapura Laboratory, namely O. chiamera, O. goliath, O. meridionalis, O. paradisea, O. priamus, O. thitonus and O. rothschildi. Based on observations there are striking differences in color, shape and size between male and female in the same species.   Key words: birdwing butterfly, Ornithoptera sp., KSP Jayapura, dimorphisme. Â

Design of heterogeneous mechanical tests: Numerical methodology and experimental validation

AbstractStandard material parameters identification strategies for constitutive equations generally use an extensive number of classical tests for collecting the required experimental data. Recently, new specimen geometries for heterogeneous tests were designed to enhance the richness of the strain field and capture supplementary strain states using full‐field measurement techniques. The butterfly specimen is an example of such a geometry, designed through a numerical optimization procedure where an indicator capable of evaluating the heterogeneity and the richness of strain information is used. The aim of this work is to experimentally validate the heterogeneous butterfly mechanical test in the parameter identification framework. Blanks of mild steel DC04 are cut with the butterfly geometry, and specific grips are designed. Tests are performed with Digital Image Correlation technique, and a Finite Element Model Update inverse strategy is used for the parameter identification, as well as the calculation of the indicator. The identification strategy is accomplished with the data obtained from the experimental tests, and the results are compared with quasi‐homogeneous tests.

Climate is a strong predictor of near-infrared reflectance but a poor predictor of colour in butterflies.

Colour variation across climatic gradients is a common ecogeographical pattern; yet there is long-standing contention over underlying causes, particularly selection for thermal benefits. We tested the evolutionary association between climate gradients and reflectance of near-infrared (NIR) wavelengths, which influence heat gain but are not visible to animals. We measured ultraviolet (UVA), visible (Vis) and NIR reflectance from calibrated images of 372 butterfly specimens from 60 populations (49 species, five families) spanning the Australian continent. Consistent with selection for thermal benefits, the association between climate and reflectance was stronger for NIR than UVA-Vis wavelengths. Furthermore, climate predicted reflectance of the thorax and basal wing, which are critical to thermoregulation; but it did not predict reflectance of the entire wing, which has a variable role in thermoregulation depending on basking behaviour. These results provide evidence that selection for thermal benefits has shaped the reflectance properties of butterflies.

Failure prediction on steel sheet under different loading paths based on fully coupled ductile damage model

It is well known that the ductile damage evolution on sheet metal under complex loading conditions is highly dependent on the complexity of the applied loading path. In this study, a fully coupled ductile damage model including the effect of Lode angle and microcracks closure together with the mixed nonlinear isotropic and kinematic hardenings is used to simulate the fracture occurrence of the chosen low carbon steel. Various mechanical tests including the uniaxial tensile tests, the pre-notched tensile tests with four notched radii, the butterfly specimen tests under different loading paths, and deep drawing tests are performed on the chosen low carbon steel sheet to calibrate and validate the proposed model. The influence of the Lode angle on damage evolution is investigated throughout various numerical simulations. These results showed the wide capabilities of the proposed fully coupled damage model on the prediction of ductile fracture under the wide range of loading conditions.

Fruit-feeding butterflies assemblage in Deciduous Seasonal Forest fragments and grassland

This study aims to investigate the diversity and richness of fruit-feeding butterflies in two fragments of Deciduous Seasonal Forest, with different anthropic explorations, comparing the border and interior environments, and an anthropic area with predominance of grassland, in Taquaruçu do Sul, Rio Grande do Sul, Brazil. Samplings were conducted fortnightly, from August 2012 to July 2013. In each area, were installed attractive traps with a fermented banana and sugarcane juice. In the fragments were installed four traps on the border and four traps 40 meters from the border and in the cultivated pasture area, four traps were also installed. A total of 2,077 specimens of fruit-feeding butterflies were collected, distributed in 45 species and four subfamilies of the Nymphalidae family. The diversity and richness of the fruit-feeding butterflies were higher in the forest fragments. The quantitative cluster analysis segregated the environments into distinct groups, aggregating border environments. By the qualitative analysis there is greater similarity between the fragments. It is concluded that the composition of the fruit-feeding butterflies species is different among the studied areas, and the environments of the forest fragments have similar diversity and richness among them, while the cultivated grassland area is less richness and diversity.

Increasingly Specialized Perception Network for Fine-Grained Visual Categorization of Butterfly Specimens

Existing fine-grained categorization methods predominantly conquer challenges independently, while neglecting the fact that patch proposal and feature extraction can reinforce each other. This necessitates to extract the domain-specific representations and localize key (most discriminative) patches alternately, since implicit to fine-grained specialization is the existence of an entry-category visual shared among all categories. In this study, an increasingly specialized perception convolutional neural network (ISP-CNN) is proposed, focusing on a butterfly domain at sub-species level due to the biosystematics structure. Its pipeline is an coarse-to-fine specialization that hierarchically extracts fine-scale features and proposes distinctive patches at multiple scales. Specifically, the framework consists of two highlights, i.e., hierarchical learning support vector machines (HL-SVMs) and patch proposal sub-networks (PPNs). Depending on the confidences obtained in HL-SVMs, the samples are classified at appropriate subset (i.e., sub-family, genus, and sub-species). Then the PPNs zoom the images to shift the focus on the most representational patches by taking previous predictions of HL-SVMs as a reference, while a finer scale network takes as input an amplified attended region from previous scales with gradual steps. As for self-optimization, ISP-CNN is driven by a patch-level loss and a class-level loss, to mutually learn patch proposals and decisions. For effectness verification, a total of 19,368 lab-made images of butterfly specimens spanning 48 sub-species are utilized as testing samples, while 116,208 augmented images are employed for training. ISP-CNN delivers the better or comparable performance, i.e., validation accuracy achieves 93.67% and testing accuracy achieves 92.13%, which outperforms state-of-the-arts.

Characterization of elastic-plastic fracture toughness of AM60 Mg alloy under mixed-mode loading conditions

In this paper, an estimation of the elastic-plastic fracture of the AM60 alloy under pure mode-I, mixed-mode, and pure mode-II loading conditions has been investigated. For this purpose, a modified Arcan-type device and a butterfly specimen were used. Fracture tests were conducted according to ASTM standard E1820; however, due to the standard being limited to some specific geometries and the mode-I loading, the relationships and parameters presented in the standard, including the non-dimensional stress intensity factor, the geometric work factor, and other parameters were extracted and analyzed using finite element analysis for the butterfly specimens and under different loading conditions and relationships developed for them. In the following, the fracture tests for the butterfly specimen were carried out under mode-I, 45° mixed-mode, and mode-II loading conditions and the material resistance curves against crack growth (J-R) were extracted. As a result, the material fracture toughness was extracted for each. Also, for the verification of the fixture used in this study, a beam-shaped specimen test was arranged, and its results were compared with the values of mode-I obtained from the modified Arcan fixture. The results indicated that, by changing the loading angle from mode-I to mode-II, the critical values of the fracture took on a decreasing trend, which indicated the greater resistance of the material to crack growth under the conditions of mode-I (tension) compared to mode-II (shear). On the other hand, the difference between the results obtained from the fixture used and the beam-shaped specimen was less than 9%, which indicates the correct performance of the fixture. Finally, in order to get a deeper understanding of the material fracture mechanism under different loading conditions, the fracture surfaces were examined using scanning electron microscopy (SEM).

Strength of Aluminum Alloys Under Static Mixed-Mode I/II Loading Conditions

Abstract Structures for aerospace and space applications often experience combination of tension and shear-loading conditions beside pure tensile opening loads (mode I). This paper presents the results of mixed-mode load tests conducted on two different grades of aluminum alloys frequently used in aerospace, automotive, and industrial structures. Testing was conducted using butterfly specimens and Arcan fixtures. Specimens were tested at different loading angles ranging from pure tension (mode I) to pure shear (mode II), as well as mixture of both modes I/II. Strength-loading-angle correlation was established. The results showed that the strength decreases linearly with increasing loading angle from pure tension to pure shear. About 45 %–47 % reduction in strength was observed upon changing loading scenario from pure tensile to pure shear. Displacements at different loading angles were measured using the digital image correlation (DIC) method and have been found to increase load mixity. Ratio of shear to tensile strength was calculated to be in range of 0.53–0.54 for aluminum alloys tested in this work. Crack extension and fracture mechanism deviated with the stress state. Finite-element modeling was also carried out and it was found to be in good agreement with the experimental results.

On modelling of shear fracture in deep drawing of a high-strength dual-phase sheet steel

The paper presents application of fracture behaviour characterisation results of a dual-phase sheet steel DP600 to an FEA of its deep-drawing for shear fracture prediction. The characterisation results were obtained with the help of a characterisation method based on a tensile test on a novel butterfly specimen and published previously by the authors. The aim of the present paper is to evaluate that characterisation method on a deep-drawing process. Based on the previous results of the authors, the fracture behaviour is modelled here with the help of the modified Mohr-Coloumb fracture model. The obtained FEA results reveal that shear fracture of the studied material is predicted too early by the used MMC fracture model. A novel adjustment of the model is proposed yielding infinitely high fracture strains at strongly pressure-superimposed stress states. As it is often the case in the state-of-the-art fracture characterisation of high-strenght sheet steels, such stress states were not tested during the previously performed fracture characterisation but occur during the studied deep drawing process. With the help of the adjusted MMC fracture model, it is possible to predict the crack initiation moment very accurately and the crack initiation location sufficiently accurately.

Failure parameter identification and validation for a dual-phase 780 steel sheet

A hybrid experimental-numerical procedure was implemented to determine the failure surface of a dual-phase 780 steel sheet as a function of the effective plastic strain, triaxiality, and Lode parameter using butterfly specimens with in situ digital image correlation strain measurement and supporting finite element calculations. A butterfly-type test specimen was employed to experimentally obtain stress states ranging from simple shear to plane strain tension including mixed tensile and shear loading. The numerically-derived failure surface was implemented into the phenomenological GISSMO damage model in the commercial finite element code LS-DYNA and the accuracy of the failure surface was determined using finite element predictions of the characterization experiments. A series of independent validation experiments related to sheet metal forming were performed including a hole tension test, a conical and flat punch hole expansion test, and a hemispherical punch test. The finite element models utilizing the damage model were able to accurately reproduce the load–displacement and surface strains of the sheet material for both the characterization and validation experiments. Prediction of the failure orientation and location compared favorably to each of the validation tests.

Maintaining mimicry diversity: optimal warning colour patterns differ among microhabitats in Amazonian clearwing butterflies.

Mimicry is one of the best-studied examples of adaptation, and recent studies have provided new insights into the role of mimicry in speciation and diversification. Classical Müllerian mimicry theory predicts convergence in warning signal among protected species, yet tropical butterflies are exuberantly diverse in warning colour patterns, even within communities. We tested the hypothesis that microhabitat partitioning in aposematic butterflies and insectivorous birds can lead to selection for different colour patterns in different microhabitats and thus help maintain mimicry diversity. We measured distribution across flight height and topography for 64 species of clearwing butterflies (Ithomiini) and their co-mimics, and 127 species of insectivorous birds, in an Amazon rainforest community. For the majority of bird species, estimated encounter rates were non-random for the two most abundant mimicry rings. Furthermore, most butterfly species in these two mimicry rings displayed the warning colour pattern predicted to be optimal for anti-predator defence in their preferred microhabitats. These conclusions were supported by a field trial using butterfly specimens, which showed significantly different predation rates on colour patterns in two microhabitats. We therefore provide the first direct evidence to support the hypothesis that different mimicry patterns can represent stable, community-level adaptations to differing biotic environments.

LepNet: The Lepidoptera of North America Network.

The Lepidoptera of North America Network, or LepNet, is a digitization effort recently launched to mobilize biodiversity data from 3 million specimens of butterflies and moths in United States natural history collections (http://www.lep-net.org/). LepNet was initially conceived as a North American effort but the project seeks collaborations with museums and other organizations worldwide. The overall goal is to transform Lepidoptera specimen data into readily available digital formats to foster global research in taxonomy, ecology and evolutionary biology.

A Faunistic Study of Diurnal and Nocturnal Lepidoptera in Darboğaz, Bolkar Mountains (Niğde, Turkey)

ABSTRACT: In this study, the fauna of diurnal and nocturnal Lepidoptera from the Darbogaz, Bolkar Mountains in Ulukisla, Nigde province in the south of the Anatolian region of Turkey has been investigated. Field studies were carried out during the years 2008 and 2009. Specimens of butterflies and moths were collected from nine localities for this study. These specimens belong to 63 diurnal and 105 nocturnal species distributed in 15 families. The distribution of species, according to families, is as follows: Hesperidae, 7; Nymphalidae, 18; Papilionidae, 1; Pieridae, 7; Lycaenidae, 30; Crambidae, 17; Pyralidae, 10; Drepanoidae, 2; Lasiocampidae, 3; Sphingidae, 1; Geometridae, 28; Erebidae, 10; Noctuidae, 30; Nolidae, 2 and Notodontidae, 2. The evaluation of literature revealed that forty of 63 butterflies and all of the moths species determined in this study are newly recorded for the Lepidoptera fauna of the Darbogaz, Bolkar Mountains.

Availability of short microsatellite markers from butterfly museums and private specimens

AbstractKnowledge of the temporal changes in genetic diversity and structure is important for identifying factors causing a decline in threatened insect species, and for establishing conservation programs for these species. Thus, there is recently an increasing interest in the restoration of genetic diversity in conservation programs using DNA data from historical museum specimens. For butterfly specimens, we measured the yields and fragment sizes of the extracted DNA and investigated the genotyping success probability of nine short microsatellite markers (allele size 73–191 bp). We used leg samples of specimens of a medium‐sized butterfly species, Melitaea ambigua (Lepidoptera; Nymphalidae), collected from the 1960s to the 2010s. The yields of specimen‐extracted DNA longer than 150 bp decreased with increasing specimen age. There were negative correlations between the genotyping success probability and specimen age for each of all microsatellite markers. A negative correlation was also observed between the genotyping success probability and allele size of each microsatellite marker. We conclude that short microsatellite markers and analysis of recently obtained specimens are particularly suitable for microsatellite analysis of butterfly specimens.

Experimental-numerical evaluation of a new butterfly specimen for fracture characterisation of AHSS in a wide range of stress states

Results of an experimental-numerical evaluation of a new butterfly specimen for fracture characterisation of AHHS sheets in a wide range of stress states are presented. The test on the new butterfly specimen is performed in a uniaxial tensile machine and provides sufficient data for calibration of common fracture models. In the first part, results of a numerical specimen evaluation are presented, which was performed with a material model of a dual-phase steel DP600 taken from literature with plastic flow and fracture descriptions. In the second part, results of an experimental-numerical specimen evaluation are shown, which was conducted on another dual-phase steel DP600, which was available with a description of plastic flow only and whose fracture behaviour was characterised in the frame of this work. The overall performance of the new butterfly specimen at different load cases with regard to characterisation of the fracture behaviour of AHSS was investigated. The dependency of the fracture strain on the stress triaxiality and Lode angle as well as space resolution is quantified. A parametrised CrachFEM ductile shear fracture model and modified Mohr-Coloumb ductile shear fracture model are presented as a result of this quantification. The test procedure and results analysis are believed to contribute to current discussions on requirements to AHSS fracture characterisation.