e-journal

A Quality-Relevant Sequential Phase Partition Approach for Regression Modeling and Quality Prediction Analysis in Manufacturing Processes

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Competition and demand for consistent and high-quality product have spurred the development of quality
prediction methods for industrial manufacturing processes. Multiplicity of phases is, in general, common nature of many batch manufacturing processes. Considering that different phases may
have different effects on qualities, one of the key issues is how to partition the whole batch process into multiple phases. In the present work, an automatic quality-relevant step-wise sequential
phase partition (QSSPP) algorithm is developed for phase-based regression modeling and quality prediction. It considers the time sequence of operation phases and can capture the time-varying
quality prediction relationships. Using this algorithm, phases are separated in order from quality-relevant perspective, revealing different quality prediction relationships. The phase-based regression
system is set up for online quality prediction and the online prediction results are quantitatively evaluated for each phase. The feasibility and performance of the proposed algorithm are illustrated by an important manufacturing process, injection molding.
Note to Practitioners—This paper was motivated by competition and demand for consistent and high-quality product for industrial manufacturing processes with multiphase characteristics.
Industrial experts have noted that events taking place in different phases may have different impacts on the final product yield and quality. Phase-based quality prediction has drawn their attention
by providing quality information before the end of processes. This paper suggests an automatic sequential phase partition algorithm for phase-based regression modeling and quality prediction which
can identify phases in sequence from the quality-relevant perspective. The division result is easy to read and does not need a heavy postprocessing as what is done for clustering-based result. The phase information is identified in order by directly relating changes of process characteristics with their influence on quality prediction performance. More accurate regression models and online quality prediction results can thus be readily obtained. Preliminary experiments suggest that this approach is feasible but it has not yet been incorporated into a quality control system nor tested in practical industrial production. In future research, we will address the design of phase-based quality prediction and control system that can be put into online application.
Index Terms—Cumulative analysis, multiphase batch processes, quality prediction, regression modeling, sequential phase partition.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 4, OCTOBER 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 4, OCTOBER 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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