<p>With the dynamic arrival of production orders and ever-changing shop-floor conditions within a production system, production scheduling presents a challenge for manufacturing firms to ensure production demands that are met with high productivity and low operating cost. Before a production schedule is generated to process the incoming production orders, the production planning stage must take place. Given the large number of input parameters involved in production planning, it is important to understand their interactions between production planning and scheduling. This is to ensure production planning and scheduling could be done effectively and efficiently in achieving the best / optimal production performance such as minimizing cost. In this study, by utilizing the capabilities of data pervasiveness in smart manufacturing setting, we propose an approach to develop a surrogate model using the input parameters from a production plan to predict the production performance By making use of three categories of input parameters, namely current production system load, product-based and machine-based parameters, the prediction is performed through the construction of a surrogate model using multivariate adaptive regression spline (MARS). The effectiveness of the proposed MARS model is demonstrated using an industrial case study of a wafer fabrication production through the random sampling of varying numbers of training data set.</p>

Prediction of Production Performance in Smart Manufacturing Using Multivariate Adaptive Regression Spline

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2021)

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