Citation: Alwan, W.; Ngadiman,
N.H.A.; Hassan, A.; Sau, S.R.;
Mahmood, S. Ensemble Classier for
Recognition of Small Variation in
X‑Bar Control Chart Paerns.
Machines 2023, 11, 115.
hps://doi.org/10.3390/
machines11010115
Academic Editor: Ahmed
Abu‑Siada
Received: 30 November 2022
Revised: 6 January 2023
Accepted: 13 January 2023
Published: 14 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Swierland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Aribution (CC BY) license (hps://
creativecommons.org/licenses/by/
4.0/).
Article
Ensemble Classier for Recognition of Small Variation in X‑Bar
Control Chart Paerns
Waseem Alwan
1
, Nor Hasrul Akhmal Ngadiman
1,2,
* , Adnan Hassan
1
, Syahril Ramadhan Sau
1
and Salwa Mahmood
3
1
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, UTM Skudai, Johor Bahru 81310, Malaysia
2
Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga,
Surabaya 60115, Indonesia
3
Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh 84600, Malaysia
* Correspondence: norhasrul@utm.my
Abstract: Manufacturing processes have become highly accurate and precise in recent years, par‑
ticularly in the chemical, aerospace, and electronics industries. This has aracted researchers to
investigate improved procedures for monitoring and detection of small process variations to remain
in line with such advances. Among these techniques, statistical process controls (SPC), in particular
the control chart paern (CCP), have become a popular choice for monitoring process variance, being
utilized in numerous industrial and manufacturing applications. This study provides an improved
control chart paern recognition (CCPR) method focusing on X‑bar chart paerns of small process
variations using an ensemble classier comprised of ve complementing algorithms: decision tree,
articial neural network, linear support vector machine, Gaussian support vector machine, and k‑
nearest neighbours. Before advancing to the classication step, Nelson’s Rus Rules were utilized as
a monitoring rule to distinguish between stable and unstable processes. The study’s ndings indi‑
cate that the proposed method improves classication performance for paerns with mean changes
of less than 1.5 sigma, and conrm that the performance of the ensemble classier is superior to that
of the individual classier. The ensemble classier can distinguish unstable paern types with a
classication accuracy of 99.55% and an ARL1 of 11.94.
Keywords: control chart paerns; ensemble classier; small variation
1. Introduction
These days, the competition among manufacturing companies is increasingly oriented
towards quality, with the end goal being to produce a product that is of the best possible
quality. Manufacturing processes have become highly accurate and precise, particularly
in the chemical, aerospace, and electronics industries. This has aracted researchers to in‑
vestigate improved procedures for monitoring and detection of small process variation in
order to be in line with such advances [1,2]. Manufacturing companies are using advanced
technologies for quality control, such as articial intelligence and control chart paern
recognition (CCPR). CCPR is regarded as one of the most important statistical process con‑
trol (SPC) techniques. The implementation of CCPR with suitable algorithms has gained
importance due to its capability to recognize unstable processes. In addition, it can pro‑
vide operators with early warning, allowing for preventive action to avoid production of
defective products. CCPR gains its popularity because it provides useful hints for locating
the source of process variation. This is valuable for industrial practitioners such as quality
inspectors and production supervisors in determining the root causes of various problems.
A particular CCP can be associated with the potential origin of process variation [3–5].
Such variability may be aributed to human faults, defective manufacturing equipment,
broken tools, or defective materials, among others. When a process is out of control, pro‑
cess behavior can take on a number of unnatural paerns on an X‑bar chart, including the
Machines 2023, 11, 115. https://doi.org/10.3390/machines11010115 https://www.mdpi.com/journal/machines
