Overload and Condition Monitoring in Rolling Mills via Internet

Deckers, Joerg; Becker, E.

doi:10.1016/s1474-6670(17)31037-6

Cited by 1 publication

(2 citation statements)

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“…We have solved both the nominal problem P(p) and the robust counterpart (12) and compare the results in this section. We followed the first discretize, then optimize approach, where the PDE given by eqs.…”

Section: Resultsmentioning

confidence: 99%

“…One additional method to approach this issue is to identify the loads and states of the structures that actually occur during the usage phase. To mitigate the effects of uncertainty by monitoring the states and loads of parts [14] and processes [12], several methods are known, such as Structural Health Monitoring or Process Monitoring. By identifying forces it is possible to react to unforeseen events or to shut down systems before reaching safety critical conditions.…”

Section: Introductionmentioning

confidence: 99%

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Robust Design of a Smart Structure under Manufacturing Uncertainty via Nonsmooth PDE-Constrained Optimization

Kolvenbach¹,

Ulbrich²,

Krech

et al. 2018

AMM

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We consider the problem of finding the optimal shape of a force-sensing element which is integrated into a tubular structure. The goal is to make the sensor element sensitive to specific forces and insensitive to other forces. The problem is stated as a PDE-constrained minimization program with both nonconvex objective and nonconvex constraints. The optimization problem depends on uncertain parameters, because the manufacturing process of the structures underlies uncertainty, which causes unwanted deviations in the sensory properties. In order to maintain the desired properties of the sensor element even in the presence of uncertainty, we apply a robust optimization method to solve the uncertain program.The objective and constraint functions are continuous but not differentiable with respect to the uncertain parameters, so that existing methods for robust optimization cannot be applied. Therefore, we consider the nonsmooth robust counterpart formulated in terms of the worst-case functions, and show that subgradients can be computed efficiently. We solve the problem with a BFGS--SQP method for nonsmooth problems recently proposed by Curtis, Mitchell and Overton.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%