Authors: Paul L. Goethals; Byung Rae Cho
Addresses: Department of Industrial Engineering, Clemson University, Clemson, South Carolina, 29634, USA. ' Department of Industrial Engineering, Clemson University, Clemson, South Carolina, 29634, USA
Abstract: For any given manufacturing system, the initial setting of the process target is critical in preventing excessive product rejection and rework costs. Often referred to as the 'process target problem', the traditional approach relies first on the assumption of certain values for the process mean and variance, prior to identifying the optimal setting. This paper, in contrast, proposes integrating estimated response surface functions developed for the mean and variance based upon observations made on a given process, thus removing any assumption on the parameters. In addition, this paper considers non-standard experimental regions, where constraints may exist on the factor space or restrictions are implemented on the number of experimental runs conducted. In doing so, greater flexibility is obtained in finding solutions to process target problems and the scope of the research field is broadened. Non-linear programming methods are used to facilitate this approach, and numerical examples are provided to illustrate findings. [Received 16 February 2010; Revised 27 May 2010; Revised 8 November 2010; Accepted 9 November 2010]
Keywords: quality improvement; response surface functions; optimal process targets; D-optimal designs; manufacturing systems; experimental design.
European Journal of Industrial Engineering, 2012 Vol.6 No.2, pp.234 - 258
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 23 Feb 2012 *