Multidisciplinary Design Optimization of Crankshaft Structure Based on Cooptimization and Multi-Island Genetic Algorithm

Abstract

The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN) model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA) combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced.

Reliability Optimization and Importance Analysis of Circular‐Consecutive k‐out‐of‐n System

“…Problem-independent technique is used to solve various complex problems, such as genetic algorithm [22], simulated annealing algorithm [23], particle swarm optimization [24], tabu search algorithm [25], and neural net algorithm [26]. The research of combining metaheuristics with importance measure is also developed to solve CAP.…”

Product Development Using an Intelligent Supporting System

“…In the area of product optimisation (case study of shaft in high performance compressor) the process was introduced which evaluated different properties of the product with the help of NN. Therefore the developer was able to choose the appropriate solution based on the data and in the meantime also decrease the development time and costs (Liu et al, 2016). For the decision of the product itself, a model has been introduced, which combines functions of design, development and marketing as early as in the concept phase for the purpose of optimal end product.…”

Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

“…erefore, an accurate optimal algorithm, which can precisely search for Pareto optimal frontier and acquire nondominated solutions, is urgently required. At present, a variety of multiobjective optimization algorithms are developed, such as multi-island genetic algorithm (MIGA) [31][32][33], neighbourhood cultivation genetic algorithm (NCGA) [34,35], the second generation of nondominated sorting genetic algorithm (NSGA-II) [36][37][38], and archive microgenetic algorithm (AMGA) [39,40]. Among them, owing to the virtues of nondominated sorting strategy and elitist retention strategy, NSGA-II has been widely used in MODO problems.…”