The size and training parameters of artificial neural networks have a critical effect on their
performance. This paper presents the application of the Taguchi Design of Experiments …

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

Parameter design optimization problems have found extensive industrial applications,
including product development, process design and operational condition setting. The …

In robust design (RD) modeling, the response surface methodology (RSM) based on the
least-squares method (LSM) is a useful statistical tool for estimating functional relationships …

Response surface methodology (RSM) has been widely recognized as an essential
estimation tool in many robust design studies investigating the second-order polynomial …

Over the last two decades, robust design (RD) has emerged as one of the best quality
improvement tools in industry. Each step in the RD procedure plays an important role in …

Although there are some skillful techniques to analyze the parameter design problems, the
methods for tackling the dynamic multi-response are rare. This work proposes an approach …

Intelligent manufacturing is becoming more and more attractive for industrial societies
especially after the introduction of industry 4.0 where most of industrial operations are to be …

Prediction of process parameters is very important in parameter design. If predictions are
fairly accurate, the quality improvement process will be useful to save time and reduce cost …

Applying parameter design to a system that has a binary-type performance, an efficient
metric is to employ the operating window (OW) which is the range between two performance …