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- Barlo, Alexander, M.Sc. Eng. 1994-, et al.
(author)
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Investigation of a Bending Corrected Forming Limit Surface for Failure Prediction in Sheet Metals
- 2019
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In: Forming Technology Forum.
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Conference paper (peer-reviewed)abstract
- Ensuring process feasibility is a high priority in the automotive industry today. Within theCAE departments concerning the manufacturing of body components, one of the most important areas ofinterest is the accurate prediction of failure in components through Finite Element simulations. This paperinvestigates the possibility of introducing the component curvature as a parameter to improve failureprediction. Bending-under-tension specimens with different radii are used to create a Bending CorrectedForming Limit Surface (BC-FLS), and a test die developed at Volvo Cars, depicting production-like scenariosby exposing an AA6016 aluminium alloy blank to a stretch-bending condition with biaxial pre-stretching, isused to validate the proposed model in the commercial Finite Element code AutoFormTM R8. The findings ofthis paper showed that the proposed BC-FLS approach performed well in the failure prediction of the test diecompared to the already in AutoFormTM R8 implemented max failure approach.
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| 2. |
- Poulding, Simon, et al.
(author)
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The optimisation of stochastic grammars to enable cost-effective probabilistic structural testing
- 2015
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In: Journal of Systems and Software. - : Elsevier. - 0164-1212 .- 1873-1228. ; 103, s. 296-310
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Journal article (peer-reviewed)abstract
- The effectiveness of statistical testing, a probabilistic structural testing strategy, depends on the characteristics of the probability distribution from which test inputs are sampled. Metaheuristic search has been shown to be a practical method of optimising the characteristics of such distributions. However, the applicability of the existing search-based algorithm is limited by the requirement that the software's inputs must be a fixed number of ordinal values. In this paper we propose a new algorithm that relaxes this limitation and so permits the derivation of probability distributions for a much wider range of software. The representation used by the new algorithm is based on a stochastic grammar supplemented with two novel features: conditional production weights and the dynamic partitioning of ordinal ranges. We demonstrate empirically that a search algorithm using this representation can optimise probability distributions over complex input domains and thereby enable costeffective statistical testing, and that the use of both conditional production weights and dynamic partitioning can be beneficial to the search process. (C) 2014 Elsevier Inc. All rights reserved.
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