.png){kind=icon}
.png){kind=icon}
Return
Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jmsy.2022.05.016.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machining process is currently widely employed in mechanical manufacturing systems. Optimum selection of machining process parameters can improve the environmental impact and production efficiency of the machining process effectively. However, existing studies toward machining process parameters optimisation are focusing on computationally expensive numerical simulations and costly physical models, which are inefficient and labor-expensive. Moreover, the numerical simulations and physical models often show an unsatisfactory accuracy in the actual exploitation stage, which would make the final optimisation solution cannot achieve the best optimum results. Therefore, this paper proposes a deep learning based data-driven genetic algorithm and TOPSIS for multi objective optimisation of machining process parameters and searching the final solutions. First, deep learning is employed in this paper to automatically develop the data-driven prediction function of different optimized objectives. Then the developed optimized objective prediction function is converted into the surrogate model and integrated with the genetic algorithm for generating the Pareto set. Finally, the TOPSIS is employed to automatically search the best optimum processing parameter from the generated Pareto set. The experiments conducted on a milling machine and the experimental results show that the proposed parameters selection method is feasible and effective, and it can effectively and adjustably help operators to realize a balance among the multiple different conflicting objectives.
Keywords:
Multi-objective optimisation
Deep learning
Data-driven genetic algorithm
Machining process
Journal
IF:
14.2
Papers:
2.7K
Citations:
1.6W