Abstract
Post-manufacturing induced defects in the form of scratches are sometimes inadvertently introduced onto sheet metal surfaces during either transportation, storage or handling. However, limited research has been previously carried out to establish the impact of such surface defects on sheet formability. Test trial results after press brake forming of Ti-3Al-2.5V showed that for longitudinal scratches oriented along the sheet rolling direction, scratch profiles with depth in the ranges of -1μm to -18μm and pile up height between 1μm to 16μm can be successfully formed; hence could be deemed acceptable during the sheet selection process. Failure of the coupons during the press brake forming trials was due to the impact of the scratch defects in their role as stress raisers and occurred primarily at the longitudinal scratch defect zones
Highlights
Sheet metal surface defects could be encountered at various stages of a material fabrication process
The high cost incurred in correcting post- production surface defects has led to the formulation of various optimization techniques such as; the one-shot deflectometry technique based on the Fourier- transform method, development of a genetic algorithm which relies on extreme machine learning, convolutional neural network system for recognition and detection and an image thresholding method based on automated vision systems [3,4,5,6]
Press brake forming trials were conducted on Ti-3Al-2.5V coupons with surfaces of varied longitudinal scratch defect profiles
Summary
Sheet metal surface defects could be encountered at various stages of a material fabrication process Defects such as inclusions, oxide lines, thermal cracks, scars and blowholes may be produced during material production. The high cost incurred in correcting post- production surface defects has led to the formulation of various optimization techniques such as; the one-shot deflectometry technique based on the Fourier- transform method, development of a genetic algorithm which relies on extreme machine learning, convolutional neural network system for recognition and detection and an image thresholding method based on automated vision systems [3,4,5,6]. Press brake forming trials were undertaken to assess the effect of post-manufacturing
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