DOE （design of experiments） is a systematic, rigorous approach to engineering problem-solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, ...DOE （design of experiments） is a systematic, rigorous approach to engineering problem-solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, and supportable engineering conclusions. This paper presents a comparison of three different experimental designs （full experimental design, fractional design and Taguchi design） aimed at studying the effects of cutting parameters variations on surface finish. The results revealed that the effects obtained by analyzing both fractional and Taguchi designs were comparable to the main effects and two-level interactions obtained by the full factorial design. Thus, we conclude that full factorial design appear to be reliable and more economical since they permit to reduce by a factor the amount of time and effort required to conduct the experimental design without losing valuable information. Thus, we conclude that full factorial design appear to be reliable and more economical and without losing valuable information.展开更多
文摘DOE （design of experiments） is a systematic, rigorous approach to engineering problem-solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, and supportable engineering conclusions. This paper presents a comparison of three different experimental designs （full experimental design, fractional design and Taguchi design） aimed at studying the effects of cutting parameters variations on surface finish. The results revealed that the effects obtained by analyzing both fractional and Taguchi designs were comparable to the main effects and two-level interactions obtained by the full factorial design. Thus, we conclude that full factorial design appear to be reliable and more economical since they permit to reduce by a factor the amount of time and effort required to conduct the experimental design without losing valuable information. Thus, we conclude that full factorial design appear to be reliable and more economical and without losing valuable information.
