Abstract

Many engineering problems involve understanding effects of different variables on a desired output or response. Experimental-based problems can be challenging to assess, especially with limited resources, i.e. time and/or materials. When theoretical models become complicated and costly to produce, empirical or black-box models are highly sought. That can be achieved using mathematical and statistical tools to correlate between the input(s) and output(s) of a system. Proper design of experiment (DoE) is required to attain credible results and good-predicting model, which in turn, leads to proper optimization of the system. Response surface methodology has also been employed for such systems by providing visualization elements and a systematic approach to model an experimental model combining DoE and optimization in one method. Many software packages are utilized to carry-out DoE and ending up with optimization of systems using RSM. Access to such powerful packages can be challenging to many engineers and/or students, and hence; this paper aims to design and optimize an RSM-based case study using MS Excel. It is designed to accommodate the main features of RSM study and optimize the results with the readily available add-ins. This methodology can be employed in engineering-based courses and serve as a viable learning tool.