Cut edge of annealed float glass: crack system and possibilities to increase the edge strength by adjusting the cutting process

Müller-Braun, Steffen; Seel, Matthias; König, Markus; Hof, Peter; Schneider, Jens; Oechsner, Matthias

doi:10.1007/s40940-019-00108-3

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…In particular, it has been found that when the edge is viewed perpendicular to the glass surface ( Fig. 2), the so-called lateral cracks, which can be observed here, allow the best predictions for strength (Müller-Braun et al 2020). The challenge here is to detect these lateral cracks in an accurate way.…”

Section: Cutting Of Glassmentioning

confidence: 81%

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Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

et al. 2020

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In this paper, artificial intelligence (AI) will be applied for the first time in the context of glass processing. The goal is to use an algorithm based on artificial intelligence to detect the fractured edge of a cut glass in order to generate a so-called mask image by AI. In the context of AI, this is a classical problem of semantic segmentation, in which objects (here the cut-edge of the cut glass) are automatically surrounded by the power of AI or detected and drawn. An original image of a cut glass edge is implemented into a deep neural net and processed in such a way that a mask image, i.e. an image of the cut edge, is automatically generated. Currently, this is only possible by manual tracing the cut-edge due to the fact that the crack contour of glass can sometimes only be recognized roughly. After manually marking the crack using an image processing program, the contour is then automatically evaluated further. AI and deep learning may provide the potential to automate the step of manual detection of the cut-edge of cut glass to great extent. In addition to the enormous time savings, the objectivity and reproducibility of detection is an important aspect, which will be addressed in this paper.

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Section: Cutting Of Glassmentioning

confidence: 81%

“…In the second step, the cut is opened along the fissure by applying a bending stress. This cutting process is influenced by many parameters (Müller-Braun et al 2020). The edge strength in particular can be reproducibly increased by a proper adjustment of the parameters (Ensslen and Müller-Braun 2017).…”

Section: Cutting Of Glassmentioning

confidence: 99%

Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

et al. 2020

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“…In addition, it was possible to determine optical characteristics, such as the depth of lateral and median cracks, which can be correlated to the edge strength. That way, an estimation of the cut edge strength can be based on optical methods (Müller-Braun et al 2020).…”

Section: Generation Of Cutting Process Parametersmentioning

confidence: 99%

Study on the optical quality and strength of glass edges after the grinding and polishing process

et al. 2020

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Glass edges result from cutting glass sheets and a further optional finishing. The mechanical interference into the brittle material glass causes flaws and cracks at the edge surface. Those defects have an influence on the strength of the whole glazing. Within the scope of a research project at the Institute of Building Construction from the Technische Universität Dresden, the grinding and polishing process is examined in terms of characteristic visible effects on the glass edge and the edge strength. Thereby a special focus of the research project is the impact of various polishing cup wheels for the chamfer surface of annealed glass. The article presents some basics about the processing steps of glass edges surfaces, introduces the considered grinding and polishing cup wheels and gives an overview of the performed experimental examinations. A microscopic analysis enables a characterisation of typical defects at the surfaces. Furthermore, four-point bending tests are performed to determine the bending tensile stresses at failure. The combination of both methods enables an analysis of the fracture-causing defect before destruction and a correlation between the optical surface quality and the bending tensile stresses. Additionally, the microscopy could be used to support the adjustment of a grinding machine and control reproducible edge qualities. The evaluation shows that a special development of polishing cup wheels for the chamfer can improve the surface quality and consequently increases the edge strength.

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“…When glass is cut it is first scribed under a sharp roller with oil applied in front of the cutting head to generate cracks which are subsequently driven through the thickness of the pane by flexuring the glass and breaking it in two. The result of the cut depends on the type of roller and the force, angle and velocity applied to it, in addition to the composition of cutting oil, all of which are controlled to generate median cracks and limit the introduction of lateral cracks, which degrade the edge quality, see also Müller-Braun et al (2020) (Lawn, 1993;Le Bourhis, 2008). Moreover, the quality depends on the flexure stress and magnitude of surface residual stresses that may be present.…”

Section: Manufacturementioning

confidence: 99%

Using a Hierarchical Weibull model to Predict Failure Strength of Different Glass Edge Profiles

Kinsella¹,

Lindström²

2020

International Journal of Structural Glass and Advanced Material

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The edge strength of glass is analyzed using a Weibull statistical framework based on 78 data samples from a range of experiments recorded in literature. Based on the analysis, a 45 MPa strength value (computed as the lower bound in a one-sided confidence interval at the 75% level for the 5-percentile in the distribution) could be conservatively used with arrised, ground and polished edges when related to a reference length of 100 mm at an applied stress rate of 2 MPa/s. The size effect can be represented by the usual weakest-link scaling formula with the Weibull modulus taken to be 8.0, 12.0, 8.0 and 6.5, respectively, for as-cut, arrised, ground and polished edges. It is estimated that static fatigue is best accounted for with a value of stress corrosion parameter about n = 16. The results are obtained with random sampling MC in a hierarchical modelling approach with the Weibull parameters treated as nested random variables. By accounting for the influence of glass supplier as a mixed-effect in a linear statistical model, it is found that supplier effects are significant and important to consider along with others due to, e.g., stress rate and edge length exposed to maximum stress. The data samples which are limited to glass tested in an ambient environment using four-point bending fixture, show that Weibull statistics generally scatter considerably. Numerical investigations with random sampling show that shape parameter estimates scatter substantially when sample size is limited, which can explain some of the observed variability in shape more so for ground and polished edges than for as-cut and arrised. For the as-cut edge, it is suggested that the shape parameter is scale-dependent. The Weibull parameters are also estimated using a clustered likelihood estimator under the condition that the shape factor has constant value for each edge type.

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Cut edge of annealed float glass: crack system and possibilities to increase the edge strength by adjusting the cutting process

Cited by 12 publications

References 4 publications

Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

Study on the optical quality and strength of glass edges after the grinding and polishing process

Using a Hierarchical Weibull model to Predict Failure Strength of Different Glass Edge Profiles

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