Mathematical Modelling and Statistical Methods of Quality Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 787

Special Issue Editor


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Guest Editor
Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taichung City, Taiwan
Interests: design of experiments; statistical quality control; genetic statistics; deep learning; Bayesian methods; machine learning

Special Issue Information

Dear Colleagues,

The aim of this Special Issue on "Mathematical Modelling and Statistical Methods of Quality Engineering" is to provide a platform for researchers, practitioners, and experts from various disciplines to explore the latest advancements, methodologies, and applications in quality engineering. The scope of this issue encompasses a wide range of topics related to mathematical modelling and statistical methods that contribute to the improvement, analysis, and optimization of quality in diverse industries.

Quality engineering is crucial in ensuring the delivery of products, services, and processes that meet or exceed customer expectations. This Special Issue aims to showcase innovative research that integrates mathematical modelling and statistical methods to address challenges and enhance the understanding of quality-related phenomena. Contributions are sought in areas such as statistical process control, design of experiments, reliability analysis, quality optimization, Six Sigma, lean manufacturing, quality management systems, and more.

The scope of this issue also includes the application of mathematical models, statistical techniques, data analysis, simulation, or optimization methods in quality engineering. We encourage submissions of original research articles, reviews, and case studies that present novel concepts, methodologies, and practical applications. By sharing diverse perspectives and experiences, this Special Issue aims to facilitate interdisciplinary collaboration and foster the exchange of knowledge and best practices in quality engineering.

Prof. Dr. Changyun Lin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • statistical process control
  • design of experiments
  • reliability analysis
  • data analysis
  • computer science

Published Papers (1 paper)

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Research

29 pages, 1901 KiB  
Article
Process Capability Evaluation Using Capability Indices as a Part of Statistical Process Control
by Marta Benková, Dagmar Bednárová and Gabriela Bogdanovská
Mathematics 2024, 12(11), 1679; https://doi.org/10.3390/math12111679 - 28 May 2024
Viewed by 493
Abstract
This study aims to highlight the importance of a systematic approach to process capability assessment and the importance of following a sequence of steps. Statistical process control provides several different ways of assessing process capability. This study evaluates the process capability of crown [...] Read more.
This study aims to highlight the importance of a systematic approach to process capability assessment and the importance of following a sequence of steps. Statistical process control provides several different ways of assessing process capability. This study evaluates the process capability of crown cap manufacturing through capability indices. In addition to calculating the indices, the evaluation involves extensive data analysis. Before calculating the capability indices, the assumptions for their correct selection and use were also verified. Several statistical tests were used to verify each assumption. The research value of the study lies in pointing out that not all tests led to the same conclusions. It highlights the importance of selecting the appropriate test type for the evaluated process quality characteristics. Full article
(This article belongs to the Special Issue Mathematical Modelling and Statistical Methods of Quality Engineering)
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