Abstract
AbstractThe expanding proliferation of components for engineering applications requires greater optimisation of parameters, which consequently increases the need for more efficient boring practices. The Taguchi Pareto-Box Behnken design is an effective optimisation procedure for the process parametric optimisation of the IS 2062 E250 steel plates. However, the weakness of the Taguchi method in its inability to distinguish which parameters have greater effects on the boring process needs to be further suppressed. Consequently, this study investigates the coupling of the firefly algorithm to the Taguchi-Pareto-Box Behnken design method for the processing of the IS 2062 E250 steel plates during the boring operation. Linear programmes were developed for the problem formulation with two variants of the objective function definition. In the first variant, the Box Behnken design optimized parameters and the firefly-oriented optimisation procedure was addressed to attain optimal solutions. For the second variant, a regression equation was substituted as the objective function and the firefly procedure was implemented to obtain the optimal solutions. Based on a defined population for the problem, an initial test of convergence was actualized and 50 iterations were found as an effective convergence point for the iterations. Numerical simulation coupled with experimental data analysis was conducted to ascertain the effectiveness of the proposed method. Literature data on IS 2062 E250 steel plate processing on the CNC machine was used in the testing. The results revealed that the proposed method exhibits good performance for boring operations in machine shops. Using the Taguchi-Pareto-Box Behnken-firefly algorithm, the obtained results are promising. The application of this proposal would aid machining to better decisions that improve the quality of products and reduces the cost of production.
Publisher
Springer Science and Business Media LLC
Reference30 articles.
1. Dostál, P. (2013). The use of optimization methods in business and public services. In: Zelinka, I., Snášel, V., Abraham, A. (eds) Handbook of Optimization. Intelligent Systems Reference Library, vol 38. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30504-7_29
2. Dutta A, Das A, Joshi SN (2017) Optimum process parameters for efficient and quality thin wall machining using firefly algorithm. Int J Additive Subtract Mat Manufact 1:3–22
3. Kant G, Sangwan KS (2014) Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J Clean Prod 83:151–164
4. Kumari S, Kumar A, Yadav RK, Vivekananda K (2018) Optimisation of machining parameters using grey relation analysis integrated with harmony search for turning of AISI D2 steel. Materials Today: Proceedings 5:12750–12756
5. Lee YZ, Ponnambalam SG (2012) Optimisation of multipass turning operations using PSO and GA-AIS algorithms. Int J Prod Res 50:6499–6518
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献