An intelligent approach to robust multi-response process design

Abstract

In order to meet strict customer demands in a global highly-complex industrial sector, it is necessary to design manufacturing processes based on a clear understanding of the customer's requirements and usage of a product, by translating this knowledge into the process parameter design. This paper presents an integrative, general and intelligent approach to the multi-response process design, based on Taguchi's method, multivariate statistical methods and artificial intelligence techniques. The proposed model considers process design in a general case where analytical relations and interdependency in a process are unknown, thus making it applicable to various types of processes, and incorporates customer demands for several (possible correlated) characteristics of a product. The implementation of the suggested approach is presented on a study that discusses the design of a thermosonic copper wire bonding process in the semiconductor industry, for assembly of microelectronic devices used in automotive applications. The results confirm the effectiveness of the approach in the presence of different types of correlated product quality characteristics.

Keywords:

• Taguchi method
• multi-response design
• artificial neural networks
• genetic algorithm
• multivariate statistical techniques