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ABSTRACT
Understanding the uncertainty in simulation outputs is important for careful decision-making regarding a machining process. However, Monte Carlo–based methods cannot be used for evaluating the uncertainty when the simulations are computationally expensive. An alternative approach is to build an easy-to-evaluate emulator to approximate the computer model and run the Monte Carlo simulations on the emulator. Although this approach is very promising, it becomes inefficient when the computer model is highly nonlinear and the region of interest is large. Most machining simulations are of this kind because the output is affected by several quantitative factors—such as the workpiece material properties, cutting tool parameters, and process parameters whose effects can change depending on other qualitative factors such as the type of materials, tool designs, and tool paths. Because the number of levels of the qualitative factors can range from tens to thousands, building an accurate emulator is not an easy task. This article proposes a new approach, called an in situ emulator, to overcome this problem. The idea is to build an emulator for the user-specified levels of the qualitative factors and inside the local region defined by the input uncertainty distribution of the quantitative factors. Efficient experimental design and statistical modeling techniques are used for constructing the in situ emulator. The approach is illustrated using the simulations of two solid end milling processes.
About the Authors
Dr. Gul is a senior data scientist in Precima.
Dr. Joseph is a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He is a senior member of ASQ.
Dr. Yan is a quantitative analytics consultant at Wells Fargo.
Dr. Melkote is associate director of the Georgia Tech Manufacturing Institute and is Morris M. Bryan Jr. Professor of the George W. Woodruff School of Mechanical Engineering at Georgia Tech.
Additional information
Funding
Notes on contributors
Evren Gul
Dr. Gul is a senior data scientist in Precima.
V. Roshan Joseph
Dr. Joseph is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He is a senior member of ASQ.
Huan Yan
Dr. Yan is a quantitative analytics consultant at Wells Fargo.
Shreyes N. Melkote
Dr. Melkote is Associate Director of the Georgia Tech Manufacturing Institute and is Morris M. Bryan Jr. Professor of the George W. Woodruff School of Mechanical Engineering at Georgia Tech.