Abstract
The objective of this work is to provide an efficient tool of Reliability-Based Design Optimization (RBDO) for soil tillage machine design process in order to achieve designs with a required reliability (safety) level. An efficient methodology that controls the reliability levels for different statistical distribution cases of random soil properties is developed. This developed strategy is based on design sensitivity concepts in order to determine the influence of each random parameter. The application of this method consists in taking into account the uncertainties on the soil tillage forces. The tillage forces are calculated in accordance with analytical model of McKyes and Ali with some modifications to include the effect of both soil-metal adhesion and tool speed. The different results show the importance of the developed strategy to improve the performance of the soil tillage equipments considering both random geometry and loading parameters.
Highlights
In the deterministic design optimization [1,2], the designer aims to reduce the engineering design cost without caring about the effects of uncertainties concerning materials, geometry and loading
The objective of this work is to provide an efficient tool of Reliability-Based Design Optimization (RBDO) for soil tillage machine design process in order to achieve designs with a required reliability level
In this paper, we develop an efficient methodology that can lead to optimum designs under uncertainties
Summary
In the deterministic design optimization [1,2], the designer aims to reduce the engineering design cost without caring about the effects of uncertainties concerning materials, geometry and loading. The resulting optimal solution may represent an inappropriate reliability level. The integration of reliability analysis during the optimization process leads to reduce the structural weight in uncritical regions that does provide an improved design and a higher level of confidence in the design. This approach can be carried out in two separate spaces: the physical space and the normalized space. Since many repeated searches are needed in the above two spaces, the computational time for such an optimization is a big problem. The solution of the above nested problems leads to a high computational cost, especially for large-scale structures. The major difficulty lies in the structural reliability evaluation, which is carried out by a special optimization
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