An interactive artificial neural networks approach to multiresponse optimization

作者：Noorossana Rassoul; Zadbood Amineh^*; Zandi Faramak; Noghondarian Kazem

来源：International Journal of Advanced Manufacturing Technology, 2015, 76(5-8): 765-777.

DOI：10.1007/s00170-014-6276-7

• Multiresponse optimization
• Process capability index
• Artificial neural networks
• Genetic algorithm
• Response surface methodology

In product or process development, it is very common to deal with a situation where simultaneous optimization of several quality characteristics is required. A common approach in multiresponse optimization problems is to use the desirability function approach based on the polynomial regression models. However, desirability function approach does not consider both location and dispersion effects. In addition, when dealing with complex manufacturing processes, artificial neural networks can estimate the relationship between responses and input variables effectively. In this paper, an artificial neural networks approach is presented which utilizes a process capability index to combine multiple responses into a single value function. Then genetic algorithm is applied to optimize the resulting function. In order to improve quality of the output results, the decision maker is allowed to interactively incorporate some preference parameters into the problem. Performance of the proposed approach is evaluated against two existing approaches. The results indicate that the proposed interactive approach is an effective and efficient method to solve multiresponse optimization problems.

• 出版日期2015-2