Assembly time estimation is a key factor in evaluating the performance of the assembly process. The overall goal of this study is to develop an efficient assembly time estimation method by generating the prediction model from an experimental design. This paper proposes a way to divide an assembly operation into four actions which consist of a) part movement, b) part installation, c) secure operations, and d) subassembly rotations. The focus of this paper is to design a time estimation model for the secure operation. To model secure times, a design of experiments is applied to collect experimental data based on the physical assembly experiments performed on products that are representative of common assembly processes. The Box-Behnken design (BBD) is an experiment design to support response surface methodology to interpret and estimate a prediction model for the securing operations. The goal is to use a quadratic model, which contains squared terms and variable interactions, to study the effects of different engineering parameters of securing time. The experiment is focused on individual-operator assembly operations. Various participants perform the experiment on representative product types, including a chainsaw, a lawn mower engine, and an airplane seat. In order to optimize the assembly time with different influence factors, mathematical models were estimated by applying the stepwise regression method in MATLAB. The second-order equations representing the securing time are expressed as functions with six input parameters. The models are trained by using all combinations of required data by the BBD method and predict the hold back data within a 95% confidence interval. Overall, the results indicate that the predicted value found was in good agreement with experimental data, with an Adjusted R-Squared value of 0.769 for estimated securing time. This study also shows that the BBD could be efficiently applied for the assembly time modeling, and provides an economical way to build an assembly time model with a minimum numbers of experiments.

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