Stopping time detection of wood panel compression: A functional time series approach

TL;DR

This paper develops a functional time series method to accurately detect the optimal stopping time in wood panel compression, improving manufacturing efficiency through near-infrared spectroscopy data analysis.

Contribution

It introduces a novel estimation procedure combining structural break detection and forecast error analysis for optimal stopping time detection in manufacturing processes.

Findings

Effective detection of stopping time in simulation studies

Significant energy and time savings demonstrated

Method applicable to real-time process monitoring

Abstract

We consider determining the optimal stopping time for the glue curing of wood panels in an automatic process environment. Using the near-infrared spectroscopy technology to monitor the manufacturing process ensures substantial savings in energy and time. We collect a time series of curves from a near-infrared spectrum probe consisting of 72 spectra and aim to detect an optimal stopping time. We propose an estimation procedure to determine the optimal stopping time of wood panel compression and the estimation uncertainty associated with the estimated stopping time. Our method first divides the entire data set into a training sample and a testing sample, then iteratively computes integrated squared forecast errors based on the testing sample. We then apply a structural break detection method with one breakpoint to determine an estimated optimal stopping time from a univariate time series of the integrated squared forecast errors. We also investigate the finite-sample performance of the proposed method via a series of simulation studies.