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Multi-objective, reliability-based design optimization of a steering linkage
(2020-09-01)
Sleesongsom, Suwin
;
Bureerat, Sujin
Reliability-based design optimization (RBDO) of a mechanism is normally based on the non-probabilistic model, which is viewed as failure possibility constraints in each optimization loop. It leads to a double-loop nested problem that causes a computationally expensive evaluation. Several methods have been developed to solve the problem, which are expected to increase the realization of optimum results and computational efficiency. The purpose of this paper was to develop a new technique of RBDO that can reduce the complexity of the double-loop nested problem to a single-loop. This involves using a multi-objective evolutionary technique combined with the worst-case scenario and fuzzy sets, known as a multi-objective, reliability-based design optimization (MORBDO). The optimization test problem and a steering linkage design were used to validate the performance of the proposed technique. The proposed technique can reduce the complexity of the design problem, producing results that are more conservative and realizable.
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Multi-objective optimization of a steering linkage using alternative objective functions
(2019-01-01)
Sleesongsom, Suwin
;
Bureerat, Sujin
This paper proposes comparing objective functions in multi-objective optimization of a rack-and-pinion steering linkage in which the optimum results can be affected by the types of objective functions especially when using evolutionary algorithms. The optimization of a steering linkage in the past studied minimizing a steering error and/or a turning radius which can be formulated as a single or multi-objective optimization problem. Steering error usually defines as the different angle between actual angle of steering wheels and the theoretical angle of the wheels according to the Ackerman’s principal. Alternatively, the steering error can be rearranged in form of a deviation of instantaneous center based on the same principal, but it still needs to clarify the advantage of using different steering error measures. As a result, it is our attention to study the effect of objective functions to the optimum results on multi-objective optimization of a rack-and-pinion steering linkage. The objective functions are assigned to simultaneously minimize a steering error (dimensionless angle or length) and a turning radius. The design variables are linkage dimensions. The design problem is solved by improving the hybridization of real-code population-based incremental learning and differential evolution (RPBIL-DE). The comparison shows that the alternative objective function can compare with the traditional objective, which leads to effective design of rack-and-pinion steering linkages.
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Topology optimisation using MPBILs and multi-grid ground element
(2018-02-12)
Sleesongsom, Suwin
;
Bureerat, Sujin
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.

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