Prediction of Tool Failure from a Probabilistic Point of View

Engel U (1994)


Publication Type: Journal article

Publication year: 1994

Journal

Publisher: None

Book Volume: 42

Pages Range: 1-13

Journal Issue: 1

DOI: 10.1016/0924-0136(94)90072-8

Abstract

Tool performance and tool life are important criteria in the designing and optimizing of forming processes. Tool behaviour and service life can be analysed by computer aided modelling, taking the whole process into consideration. However, the result of such a process simulation is usually a single value of life time, not reflecting the high degree of its uncertainty that characterizes the life time, as is well known from practice. Thus, the necessity of taking into account the stochastic characteristics of tool life is quite evident. The objective of failure analysis should be the prediction of life time for a well defined level of confidence. A systematic approach to this problem reveals that the evolution of failure probability can be derived by quantifying the interaction of load on the tool and tool strenght, both being influenced by deterministic as well as stochastic factors. Hence, a promising way to come to a reliable failure prediction is given by the combination of mechanical/numerical methods and statistical analysis, i.e. by the integration of statistics into process simulation. The benefit of such an approach is illustrated by two examples in which statistical methods are applied to achieve an improvement of tool performance. In the first example the reduction of strenght dispersion is shown to be very effective in decreasing the failure probability. Provided that the stochastic character of both load and strenght can be quantified, the most promising measures to reduce strength dispersion can be found by process simulation. In the second example the shape optimization of a cold-forging die is considered. The effect of optimization can only be asssessed if the topographical features of the tool surface are taken into account, which can be realized by applying a combination of fracture mechanics and statistical concepts. The result is again a quantifiable probability of failure. © 1994.

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APA:

Engel, U. (1994). Prediction of Tool Failure from a Probabilistic Point of View. Journal of Materials Processing Technology, 42(1), 1-13. https://dx.doi.org/10.1016/0924-0136(94)90072-8

MLA:

Engel, Ulf. "Prediction of Tool Failure from a Probabilistic Point of View." Journal of Materials Processing Technology 42.1 (1994): 1-13.

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