The influence of repeated loading, residual stresses and shakedown on the behaviour of tribological contacts

Most tribological pairs carry their service load not just once but for a very large number of repeated cycles. During the early stages of this life, protective residual stresses may be developed in the near surface layers which enable loads which are of sufficient magnitude to cause initial plastic deformation to be accommodated purely elastically in the longer term. This is an example of the phenomenon of 'shakedown' and when its effects are incorporated into the design and operation schedule of machine components this process can lead to significant increases in specific loading duties or improvements in material utilization. Although the underlying principles can be demonstrated by reference to relatively simple stress systems, when a moving Hertzian pressure distribution in considered, which is the form of loading applicable to many contact problems, the situation is more complex. In the absence of exact solutions, bounding theorems, adopted from the theory of plasticity, can be used to generate appropriate load or shakedown limits so that shakedown maps can be drawn which delineate the boundaries between potentially safe and unsafe operating conditions. When the operating point of the contact lies outside the shakedown limit there will be an increment of plastic strain with each application of the load - these can accumulate leading eventually to either component failure or the loss of material by wear. © 2005 Elsevier Ltd. All rights reserved.

Closing the gap: Improved bounds on optimal POMDP solutions

POMDP algorithms have made significant progress in recent years by allowing practitioners to find good solutions to increasingly large problems. Most approaches (including point-based and policy iteration techniques) operate by refining a lower bound of the optimal value function. Several approaches (e.g., HSVI2, SARSOP, grid-based approaches and online forward search) also refine an upper bound. However, approximating the optimal value function by an upper bound is computationally expensive and therefore tightness is often sacrificed to improve efficiency (e.g., sawtooth approximation). In this paper, we describe a new approach to efficiently compute tighter bounds by i) conducting a prioritized breadth first search over the reachable beliefs, ii) propagating upper bound improvements with an augmented POMDP and iii) using exact linear programming (instead of the sawtooth approximation) for upper bound interpolation. As a result, we can represent the bounds more compactly and significantly reduce the gap between upper and lower bounds on several benchmark problems. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.