Journal of The Japanese Society for Quality Control
Online ISSN : 2432-1044
Print ISSN : 0386-8230
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Volume 53, Issue 3
Displaying 1-7 of 7 articles from this issue
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Features
  • Data-Driven Quality Control and Agile Improvement using DN7
    Mutsumi YOSHINO
    Article type: Features
    2023 Volume 53 Issue 3 Pages 138-143
    Published: July 15, 2023
    Released on J-STAGE: December 27, 2023
    DOIhttps://doi.org/10.20684/quality.53.3_138
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    　Many companies are currently tackling on reforms to Digital Transformation.
    　However, the conversion of manufacturing sites to DX does not proceed as expected. For example, even if IoT is introduced and data is collected, utilization of the data does not progress as expected.
    　DN7 was devised as a solution to this problem. In this paper, I would like to discuss data-driven quality control and agile improvement efforts by introducing a series of steps from discovering problems hidden in data using DN7 to linking them to improvement proposals.
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  • Methodology of Autonomous Adaptive Control by Local Regression Modeling
    Masayoshi TAKAYANAGI
    Article type: Features
    2023 Volume 53 Issue 3 Pages 144-149
    Published: July 15, 2023
    Released on J-STAGE: December 27, 2023
    DOIhttps://doi.org/10.20684/quality.53.3_144
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    　In response to the growing demand for autonomous adaptive control in manufacturing lines for productivity enhancement and carbon neutrality, we propose a novel methodology for autonomous control of product quality, taking into account the effects of non-measurable parameters. Using local linear regression modeling combined with temporal neighborhood data, we identified a single manufacturing parameter guided by the derived regression coefficients. Our simulation results revealed that conventional multiple regression modeling often produced undesirable control behavior, characterized by fluctuations in product quality. To mitigate this instability, we employed semiparametric regression modeling. Notably, the semiparametric regression model succeeded in stabilizing control, through accurate selection of the control target parameter and the incorporation of an additional non-linear term that offsets time-dependent, non-measurable parameters. Our approach facilitates enhanced manufacturing control, promoting both efficiency and sustainability.
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  • Towards Data-driven Upstream Improvement ― Development of Causal Discovery Method for Manufacturing Data ―
    Genta KIKUCHI
    Article type: Features
    2023 Volume 53 Issue 3 Pages 150-155
    Published: July 15, 2023
    Released on J-STAGE: December 27, 2023
    DOIhttps://doi.org/10.20684/quality.53.3_150
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    　Causal discovery for data-driven quality improvements has been attracting increasing attention in the manufacturing domain due to the more diverse data accumulated in the wake of digital transformation. However, manufacturing data often exhibit heteroscedastic noise, which hinders the estimation performance of many existing functional causal models that assume the independence of noise terms. This study introduces a continuous optimization-based estimation method that can handle heteroscedastic noise under multivariate non-linear data with no latent confounders.Numerical experiments on synthetic data show that our estimation method improves the estimation of the causal structure under the heteroscedastic noise. We also report the result of the estimation method to real-world data collected from a ceramic substrate manufacturing process, and the results also prove the effectiveness of our approach.
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  • IE-Based Management for Promoting DX and Quality Cultivation in a Manufacturing Field
    Keisuke SHIDA
    Article type: Features
    2023 Volume 53 Issue 3 Pages 156-161
    Published: July 15, 2023
    Released on J-STAGE: December 27, 2023
    DOIhttps://doi.org/10.20684/quality.53.3_156
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    　The strength of Japan’s manufacturing industry lies in its ability to produce a wide variety of products while maintaining high quality.This is the result of continuous efforts in integrated development,production technology, and manufacturing improvement, and in this aspect, our country significantly outperforms other nations. However, manufacturing fields face various challenges. Particularly, frequent quality issues are one of the problems that arise. Process managers are daily dealing with three difficult issues: resolving these quality problems, improving productivity, and reducing manufacturing costs. Therefore, in this study, we will introduce three examples of utilizing digital transformation (DX) that can provide hints for solving these problems. It is our hope that these examples will serve as a helpful resource for problem solving in many manufacturing industries.
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Research Topics
  • Application of Information Theory, Statistical Science, and Bayesian Theory to Statistical Process Control
    Yasuhiko TAKEMOTO
    Article type: Research Topics
    2023 Volume 53 Issue 3 Pages 168-173
    Published: July 15, 2023
    Released on J-STAGE: December 27, 2023
    DOIhttps://doi.org/10.20684/quality.53.3_168
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    　Statistical process control （SPC） is a methodology for monitoring sequential processes to make sure of stable and proper performance in process quality. In particular, control charts are a quantitative management tool utilized in SPC and basically monitor a process condition utilizing quality characteristics with stochastic variability. The major part of control chart research has been developed around mathematical statistics until now. Nowadays various theories and technologies have been applied to control charts such as information theory and machine learning. This manuscript introduces a major part of my research briefly first, and then my research on control charting methodology based on such as information theory, statistical science, and Bayesian theory. The latest research topics are focusing on change point detection （CPD）, information visualization, and process stability. At last, this manuscript is concluded through the prospect of my future research.
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