Fast testing for explosive properties of mg-scale samples by thermal activation and classification by physical and chemical properties

Maurer, Stefan; Makarow, Ramona; Warmer, Johannes; Kaul, Peter

doi:10.1016/j.snb.2015.03.045

Cited by 5 publications

(3 citation statements)

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“…Nefati et al have attempted to train neural networks with databases of impact sensitivities of explosives and predict their characteristics [21]. By applying multivariate statistics, it is possible to determine characteristic values and their correlations to the properties of explosives, which could improve on a purely binary evaluation [16,22]. Going further with this idea, we evaluate the possibility of visualisation of the multidimensional measurement results of drop hammer experiments to find clusters and correlations according to substance specific features.…”

Section: Introductionmentioning

confidence: 99%

“…Other approaches to reduce the problems associated with using the drop hammer method have been explored in various publications, such as equipping drop hammers and other methods for initiating explosives with sensors [7,9,[16][17][18]. An important example of these methods is the glass anvil drop hammer, which enables recordings of reaction processes using a high-speed camera.…”

Section: Introductionmentioning

confidence: 99%

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Sensory Monitoring of Drop Hammer Experiments with Multivariate Statistics

Muhr

Klapötke

Holl

2022

Propellants Explo Pyrotec

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A precise characterization of substances is essential for the safe handling of explosives. One parameter regularly characterized is the impact sensitivity. This is typically determined using a drop hammer. However, the results can vary depending on the test method and even the operator, and it is not possible to distinguish the type of decomposition such as detonation and deflagration. This study monitors the reaction progress by constructing a drop hammer to measure the decomposition reaction of four different primary explosives (tetrazene, silver azide, lead azide, lead styphnate) in order to determine the reproducibility of this method. Additionally, further possible evaluation methods are explored to improve on the current binary statistical analysis. To determine whether classification was possible based on extracted features, the responses of equipped sensor arrays, which measure and monitor the reactions, were studied and evaluated. Features were extracted from this data and were evaluated using multivariate methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). The results indicate that although the measurements show substance specific trends, they also show a large scatter for each substance. By reducing the dimensions of the extracted features, different sample clusters can be represented and the calculated loadings allow significant parameters to be determined for classification. The results also suggest that differentiation of different reaction mechanisms is feasible. Testing of the regressor function shows reliable results considering the comparatively small amount of data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensory Monitoring of Drop Hammer Experiments with Multivariate Statistics

Muhr

Klapötke

Holl

2022

Propellants Explo Pyrotec

View full text Add to dashboard Cite

show abstract

“…Thereby, the identication on their components has emerged as an important task in industry and homeland security elds. 6,59 We selected and designed a series of single-component, binarycomponent and triple-component PBXs, involving in HMX (cyclotetramethylene tetranitramine), RDX (cyclotrimethylenetrinitramine), TATB (triamino trinitrobenzene) and TNT (trotyl), which are four of the most widely used secondary explosive ingredients in PBXs. 60,61 Consequently, 354 infrared spectra of the train dataset and 84 infrared spectra of the independent set were measured and constructed, which covered diverse composition proportion of the energetic components.…”

Section: Introductionmentioning

confidence: 99%