Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data

Process design has been intensively studied to reduce dimensional variability of products produced in multi-station manufacturing processes (MMPs). Most of the existing studies focus on predicting variation propagation and evaluating process robustness. However, these studies overlook the potential use of historical shop-floor quality data of existing MMPs in order to extract the actual manufacturing operation capabilities from each station, and then, to evaluate more accurately the expected dimensional variability of new candidate process plans. This paper proposes a methodology to improve process plan selection based on three components: (i) based on historical shop floor data, an inference on the process capabilities of the stations in an existing MMP, which will be used to produce the new product; (ii) a sensitivity analysis of candidate process plans to identify critical fixtures and manufacturing stations/operations; and (iii) an optimal selection of candidate process plans. A case study is presented to demonstrate the effectiveness of the methodology.