Manufacturing dish shaped rings on radial-axial ring rolling mills

Ring rolling is an established method to produce seamless rings of different cross-sectional geometries. For dish shaped rings, there are applications in different areas such as offshore, aeronautics or the energy sector. At the moment, dish shaped rings are produced by machining of rings with rectangular shaped cross section, by (open die) hollow forging on a conical mandrel or by using shaped ring rolling tools. These ways of manufacturing have the disadvantage of high material waste, additional costs for special tools, long process time and limited or inflexible geometries. Therefore, the manufacturing of dish shaped rings on conventional radial-axial ring rolling mills would expand the range of products for ring producers. The aim of this study is to investigate the feasibility of an alternative to the current manufacturing processes, without requiring additional tooling and material costs. Therefore, the intended formation of dish shaped rings-previously regarded as a form error-is investigated. Based on an analysis of geometrical requirements and metal flow mechanisms, a rolling strategy is presented, causing dishing and ring climbing by a large height reduction of the ring. Using this rolling strategy dish shaped rings with dishing angles up to 18° were achieved. In addition to the experiments finite element method (FEM)-simulations of the process have been successfully conducted, in order to analyze the local strain evolution. However, when the contact between ring and main roll is lost in the process the ring starts to oscillate around the mandrel and neither dishing nor ring climbing is observed. © 2013 German Academic Society for Production Engineering (WGP).

Two-dimensional Cartesian air-gap element (CAGE) for dynamic finite-element modeling of electrical machines with a flat air gap

This paper extends the air-gap element (AGE) to enable the modeling of flat air gaps. AGE is a macroelement originally proposed by Abdel-Razek et al.for modeling annular air gaps in electrical machines. The paper presents the theory of the new macroelement and explains its implementation within a time-stepped finite-element (FE) code. It validates the solution produced by the new macroelement by comparing it with that obtained by using an FE mesh with a discretized air gap. It then applies the model to determine the open-circuit electromotive force of an axial-flux permanent-magnet machine and compares the results with measurements.

Language model combination and adaptation using weighted finite state transducers

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaptation may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences. ©2010 IEEE.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.