Taguchi experimental design for manufacturing process optimisation using historical data and a neural network process model

Abstract

Purpose

The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.

Design/methodology/approach

The objectives are achieved with two separate techniques: the Retrospective Taguchi approach selects the designed experiment's data from a historical database, whilst in the Neural Network (NN) – Taguchi approach, this data is used to train a NN to estimate process response for the experimental settings. A case study illustrates both approaches, using real production data from an aerospace application.

Findings

Detailed results are presented. Both techniques identified the important factor settings to ensure the process was improved. The case study shows that these techniques can be used to gain process understanding and identify significant factors.

Research limitations/implications

The most significant limitation of these techniques relates to process data availability and quality. Current databases were not designed for process improvement, resulting in potential difficulties for the Taguchi experimentation; where available data does not explain all the variability in process outcomes.

Practical implications

Manufacturers may use these techniques to optimise processes, without expensive and time‐consuming experimentation.

Originality/value

The paper describes novel approaches to data acquisition associated with Taguchi experimentation.

Citation

Sukthomya, W. and Tannock, J.D.T. (2005), "Taguchi experimental design for manufacturing process optimisation using historical data and a neural network process model", International Journal of Quality & Reliability Management, Vol. 22 No. 5, pp. 485-502. https://doi.org/10.1108/02656710510598393