The demand for electric resistance welded (ERW) pipe for deep-water installation has increased, which necessitates a higher degree of accuracy in evaluating the strength of pipe in order to satisfy the design limit state, otherwise referred to as the collapse performance. Since ovality and residual stress governs the collapse performance, an accurate evaluation of these factors is needed. An analytical approach using a three-dimensional finite element method was proposed to simulate the roll-forming and sizing processes in manufacturing ERW pipe. To simulate significant plastic deformation during manufacturing, a nonlinear material model that included the Bauschinger effect was incorporated. The manufacturing of ERW pipe made of API 5L X70 steel was simulated and analyzed for collapse performance. Controlling the ovality of the pipe significantly decreased the amount of pressure that would cause a collapse, whereas the effect of residual stress was minor. These two factors could be improved via the use of a proper sizing ratio.

Abstract:Optical character recognition (OCR) automatically recognizes text in an image. OCR is still a challenging problem in computer vision. A successful solution to OCR has important device applications, such as text-to-speech conversion and automatic document classification. In this work, we analyze character recognition performance using the current state-of-the-art deep-learning structures. One is the AlexNet structure, another is the LeNet structure, and the other one is the SPNet structure. For this, we have built our own dataset that contains digits and upper-and lowercase characters. We experiment in the presence of salt-and-pepper noise or Gaussian noise, and report the performance comparison in terms of recognition error. Experimental results indicate by five-fold cross-validation that the SPNet structure (our approach) outperforms AlexNet and LeNet in recognition error.

Plastic deformation during the manufacture process of electric resistance welded (ERW) pipe determines the stress–strain relationship of the steel pipe, which affects the collapse pressure of offshore pipelines. To track the deformation history of the pipe, the entire process was simulated via finite element analysis using a solid element. A material model that considered both the Bauschinger effect and strain hardening was adopted. Various sizes of pipe cross-sections were simulated. As greater compression was applied during the sizing process, the strain hardening effect became more significant, so that the compressive yield strength was increased in the circumferential direction. The strain hardening effect was most prominent for a smaller diameter-to-thickness ratio (D/t), so that an increase in the collapse pressure could be obtained with a larger sizing ratio. Therefore, current design criteria for the collapse pressure recommended by Det Norske Verita (DNV) and API could be enhanced for a smaller D/t to consider the strain hardening effect during the sizing process.

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