PREDICTION MODEL BASED MULTI-PROFILE MONITORING FOR MANUFACTURING PROCESS MANAGEMENT

Authors

DOI:

https://doi.org/10.23055/ijietap.2019.26.3.2431

Keywords:

multi-profile, prediction model, profile integrated measure, manufacturing process simulation

Abstract

In an advanced manufacturing environment, the analysis of profile data collected from the process equipment is a critical issue in improving process efficiency. In particular, multi-profile monitoring is essential for process control because an advanced manufacturing process consists of numerous pieces of equipment and their related sensors. The main goal of this study is to build a monitoring chart using a Profile Integrated Measure (PIM) from multi-profile data in order to observe an overall condition of various points in the process. To deploy the proposed algorithm, multi-profile data needed to be preprocessed and applied to the prediction model. The PIM is calculated from the prediction model and reflects the relationships between the multi-profile data property, which has normal/abnormal states. The proposed algorithm constructs a model using the PIM of a normal state and identifies the performance of the model. Experiments with the simulation datasets modified from the manufacturing process validate the effectiveness and applicability of the proposed algorithm.

Author Biographies

Seung Hwan Park, Chungnam National University

School of Mechanical Engineering

Cheong-Sool Park, Korea University

School of Industrial Management Engineering

Jun-Geol Baek, Korea University

School of Industrial Management Engineering

Published

2019-07-20

How to Cite

Park, S. H., Park, C.-S., & Baek, J.-G. (2019). PREDICTION MODEL BASED MULTI-PROFILE MONITORING FOR MANUFACTURING PROCESS MANAGEMENT. International Journal of Industrial Engineering: Theory, Applications and Practice, 26(3). https://doi.org/10.23055/ijietap.2019.26.3.2431

Issue

Section

Quality, Reliability, Maintenance Engineering