Abstract
In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.
Highlights
Over the past two decades, robust parameter design (RPD), known as robust design (RD), has been widely applied to improve the quality of products in the offline stage of practical manufacturing processes
The most significant alternative to Taguchi’s approach is the dual-response model approach based on the response surface methodology (RSM) [8]
The process mean and variance are approximated as two separate functions of input factors based on the least squares method (LSM)
Summary
Over the past two decades, robust parameter design (RPD), known as robust design (RD), has been widely applied to improve the quality of products in the offline stage of practical manufacturing processes. The most significant alternative to Taguchi’s approach is the dual-response model approach based on the response surface methodology (RSM) [8] In this approach, the process mean and variance (or standard deviations) are approximated as two separate functions of input factors based on the LSM. Lin and Tu [11] identified a drawback in the dual-response model approach whereby the process bias and variance are not simultaneously minimized To overcome this issue, they proposed a mean square error (MSE) model. To define the input–output functional relationship, the conventional LSM is used to estimate unknown model coefficients. Most estimation methods based on the RSM consider several assumptions or require specific data types to determine functions between the process mean or variance and input factors. J=1 i=1 i=1 where h_mean and h_std denote the quantity of the hidden neurons of the h-hidden-node NN for the mean and standard deviation functions, respectively
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