Abstract
Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.
Highlights
Due to the existence of machining errors and the difference of process parameters, the machining process of hammerheads will cause a mass error
A mathematical optimization model based on the principle of dynamic balance is established with the hammerhead arrangement of PFY2026 scrap metal shredders (SMSs) as the research object, which is an multiple traveling salesman problem (MTSP) problem
Based on the good characteristics of GA in global optimization and the good performance of simulated annealing algorithm (SA) at local optimal solutions, an improved genetic algorithm is proposed, which is obviously superior to ACO. e optimal resultant force is reduced by 80.64%, and the dynamic unbalance on the shredder is greatly improved
Summary
Due to the existence of machining errors and the difference of process parameters, the machining process of hammerheads will cause a mass error. Lee et al [23] proposed a new method combining a genetic algorithm and a truncated Monte Carlo simulation to reduce optimal cost. To solve the optimal reactive power programming problem, Jwo et al [25] proposed a global optimization technique and hybrid simulated annealing/ genetic algorithm (HSAGA) combining GA and SA. An improved genetic simulated annealing algorithm (IGA) is proposed, in which the triple traveling salesman problem is set up in parallel by real number coding to optimize the dynamic balance of the arrangement of hammerheads and fenders on scrap metal shredders (SMSs). E hybrid of GA and SA is an innovative attempt It employs the superior performance of the SA algorithm to solve the local optimal solution and improves the convergence of GA in optimization.
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