Fatigue Life Evaluation Method for Foundry Crane Metal Structure Considering Load Dynamic Response and Crack Closure Effect

To compensate for the shortcomings of quasi-static law in anti-fatigue analysis of foundry crane metal structures,the fatigue life evaluation method of foundry crane metal structure considering load dynamic response and crack closure effect is proposed.In line with the theory of mechanical vibration,a dynamic model of crane structure during the working cycle is constructed,and dynamic coefficients under diverse actions are analysed.Calculation models of the internal force dynamic change process of dangerous cross-sections and a simulation model of first principal stress-time history are established by using the steel structure design criteria,which is utilised to extract the change of first principal stress of danger points over time.Then,the double-parameter stress spectrum is obtained by the rain flow counting method.The fatigue life calculation formula is corrected by introducing a crack closure parameter that can be calculated by the stress ratio and the effective stress ratio.Under the finite element model imported into Msc.Patran,crack propagation analysis is performed by the growth method in the fatigue integration module Msc.Fatigue.Taking the metal structure of a 100/40t-28.5m foundry crane with track offset as an example,the accuracy of calculation results and the feasibility and applicability of the proposed method are verified by theoretical calculation and finite element simulation,which provide a theoretical basis for improvement of the fatigue resistance design of foundry cranes.

Design for a Crane Metallic Structure Based on Imperialist Competitive Algorithm and Inverse Reliability Strategy

Uncertainties in parameters such as materials, loading, and geometry are inevitable in designing metallic structures for cranes. When considering these uncertainty factors, reliability-based design optimization （RBDO） offers a more reasonable design approach. However, existing RBDO methods for crane metallic structures are prone to low convergence speed and high computational cost. A unilevel RBDO method, combining a discrete imperialist competitive algorithm with an inverse reliabil- ity strategy based on the performance measure approach, is developed. Application of the imperialist competitive algorithm at the optimization level significantly improves the convergence speed of this RBDO method. At the reli- ability analysis level, the inverse reliability strategy is used to determine the feasibility of each probabilistic constraint at each design point by calculating its a-percentile per- formance, thereby avoiding convergence failure, calcula- tion error, and disproportionate computational effort encountered using conventional moment and simulation methods. Application of the RBDO method to an actual crane structure shows that the developed RBDO realizes a design with the best tradeoff between economy and safety together with about one-third of the convergence speed and the computational cost of the existing method. This paper provides a scientific and effective design approach for the design of metallic structures of cranes.

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.